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   1) Programs and the Complexity of Human Mental Processes

   In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)

   2) Artificial Intelligence Is an Engineering Discipline

   Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)

   3) A Sceptical View of Artificial Intelligence

   Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....

   When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)

   4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, Eventually

   Just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   Many problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)

   6) The Meaning of a Symbolic Description

   What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)

   7) The Principle of Artificial Intelligence

   [AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)

   9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract Form

   The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)

    10) There Are Many Types of Reasoning

   There are many different kinds of reasoning one might imagine:

    Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."

    Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)

    12) The Synthesis of Man and Machine

   Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)

    14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory Formation

   It is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological AI

   Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.

   ♦ Weak Psychological AI

   Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.

   ♦ Strong Psychological AI

... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)

   ♦ Suprapsychological AI

   Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)

    16) Determination of Relevance of Rules in Particular Contexts

   Even if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)

    17) Form and Content Are Not Fundamentally Different

   Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)

    18) The Assumption That the Mind Is a Formal System

   Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)

    19) A Statement of the Primary and Secondary Purposes of Artificial Intelligence

   The primary goal of Artificial Intelligence is to make machines smarter.

   The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)

    20) Mathematical Logic Provides the Basis for Theory in AI

   The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....

   AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)

    21) Perceptual Structures Can Be Represented as Lists of Elementary Propositions

   In artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)

    22) Definitions of Artificial Intelligence

   Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)

    23) The Computer Can Not Be a Model of the Mind

   Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)

    24) Computers Will Not Always Be Inferior to Human Brains

   The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)

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 ■ Rumelhart, D. E., P. Smolensky, J. L. McClelland & G. E. Hinton (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland, D. E. Rumelhart & the PDP Research Group (Eds.), Parallel Distributed Processing (Vol. 2, pp. 7-57). Cambridge, MA: MIT Press.

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 ■ Russell, B. (1992). In N. Griffin (Ed.), The selected letters of Bertrand Russell (Vol. 1), The private years, 1884- 1914. Boston: Houghton Mifflin. Ryecroft, C. (1966). Psychoanalysis observed. London: Constable.

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   1) The Basic Idea of Cognitive Science

   The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense.... [P]eople and intelligent computers turn out to be merely different manifestations of the same underlying phenomenon. (Haugeland, 1981b, p. 31)

   2) Experimental Psychology, Theoretical Linguistics, and Computational Simulation of Cognitive Processes Are All Components of Cognitive Science

   I went away from the Symposium with a strong conviction, more intuitive than rational, that human experimental psychology, theoretical linguistics, and computer simulation of cognitive processes were all pieces of a larger whole, and that the future would see progressive elaboration and coordination of their shared concerns.... I have been working toward a cognitive science for about twenty years beginning before I knew what to call it. (G. A. Miller, 1979, p. 9)

   3) The Nature of Cognitive Science

    Cognitive Science studies the nature of cognition in human beings, other animals, and inanimate machines (if such a thing is possible). While computers are helpful within cognitive science, they are not essential to its being. A science of cognition could still be pursued even without these machines.

    Computer Science studies various kinds of problems and the use of computers to solve them, without concern for the means by which we humans might otherwise resolve them. There could be no computer science if there were no machines of this kind, because they are indispensable to its being. Artificial Intelligence is a special branch of computer science that investigates the extent to which the mental powers of human beings can be captured by means of machines.

   There could be cognitive science without artificial intelligence but there could be no artificial intelligence without cognitive science. One final caveat: In the case of an emerging new discipline such as cognitive science there is an almost irresistible temptation to identify the discipline itself (as a field of inquiry) with one of the theories that inspired it (such as the computational conception...). This, however, is a mistake. The field of inquiry (or "domain") stands to specific theories as questions stand to possible answers. The computational conception should properly be viewed as a research program in cognitive science, where "research programs" are answers that continue to attract followers. (Fetzer, 1996, pp. xvi-xvii)

   4) The Nature of Cognitive Science

   What is the nature of knowledge and how is this knowledge used? These questions lie at the core of both psychology and artificial intelligence.

   The psychologist who studies "knowledge systems" wants to know how concepts are structured in the human mind, how such concepts develop, and how they are used in understanding and behavior. The artificial intelligence researcher wants to know how to program a computer so that it can understand and interact with the outside world. The two orientations intersect when the psychologist and the computer scientist agree that the best way to approach the problem of building an intelligent machine is to emulate the human conceptual mechanisms that deal with language.... The name "cognitive science" has been used to refer to this convergence of interests in psychology and artificial intelligence....

   This working partnership in "cognitive science" does not mean that psychologists and computer scientists are developing a single comprehensive theory in which people are no different from machines. Psychology and artificial intelligence have many points of difference in methods and goals.... We simply want to work on an important area of overlapping interest, namely a theory of knowledge systems. As it turns out, this overlap is substantial. For both people and machines, each in their own way, there is a serious problem in common of making sense out of what they hear, see, or are told about the world. The conceptual apparatus necessary to perform even a partial feat of understanding is formidable and fascinating. (Schank & Abelson, 1977, pp. 1-2)

   5) The New Field of Cognitive Science

   Within the last dozen years a general change in scientific outlook has occurred, consonant with the point of view represented here. One can date the change roughly from 1956: in psychology, by the appearance of Bruner, Goodnow, and Austin's Study of Thinking and George Miller's "The Magical Number Seven"; in linguistics, by Noam Chomsky's "Three Models of Language"; and in computer science, by our own paper on the Logic Theory Machine. (Newell & Simon, 1972, p. 4)

[kraɪm]

charge with crime обвинять в совершении преступления collective crime групповое преступление computer crime злоумышленное использование вычислительной машины computer crime использование вычислительной машины в преступных целях computer crime компьютерное преступление computer crime преступление, совершенное с применением вычислительной машины computer-related crime злоупотребление вычислительной машиной computer-related crime преступление, связанное с применением вычислительной машины conduct crime ведение уголовного дела crime воен. карать за нарушение устава crime неправильное поведение crime преступление; злодеяние; crimes against humanity преступление против человечности crime преступление crime преступность crime преступление; злодеяние; crimes against humanity преступление против человечности economic crime экономическое преступление juvenile crime преступление несовершеннолетнего mass crime массовая преступность organized crime организованная преступность political crime политическое преступление serious crime серьезное преступление sex crime половое преступление sexual crime половое преступление solve a crime расследовать преступление unsolved crime нераскрытое преступление violent crime преступление, связанное с насилием над личностью war crime военное преступление

 

выбор оптимальных параметров
—
[А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

Тематики

• энергетика в целом

EN

• optimization

 

определение оптимальных характеристик
—
[ http://slovarionline.ru/anglo_russkiy_slovar_neftegazovoy_promyishlennosti/]

Тематики

• нефтегазовая промышленность

EN

• optimization

 

оптимизация
Процесс отыскания варианта, соответствующего критерию оптимальности
[Терминологический словарь по строительству на 12 языках (ВНИИИС Госстроя СССР)]

оптимизация
1. Процесс нахождения экстремума функции, т.е. выбор наилучшего варианта из множества возможных, процесс выработки оптимальных решений; 2. Процесс приведения системы в наилучшее (оптимальное) состояние. Иначе говоря, первое определение трактует термин «О.» как факт выработки и принятия оптимального решения (в широком смысле этих слов); мы выясняем, какое состояние изучаемой системы будет наилучшим с точки зрения предъявляемых к ней требований (критерия оптимальности) и рассматриваем такое состояние как цель. В этом смысле применяется также термин «субоптимизация» в случаях, когда отыскивается оптимум по какому-либо одному критерию из нескольких в векторной задаче оптимизации (см. Оптимальность по Парето, Векторная оптимизация). Второе определение имеет в виду процесс выполнения этого решения: т.е. перевод системы от существующего к искомому оптимальному состоянию. В зависимости от вида используемых критериев оптимальности (целевых функций или функционалов) и ограничений модели (множества допустимых решений) различают скалярную О., векторную О., мно¬гокритериальную О., стохастическую О (см. Стохастическое программирование), гладкую и негладкую (см. Гладкая функция), дискретную и непрерывную (см. Дискретность, Непрерывность), выпуклую и вогнутую (см. Выпуклость, вогнутость) и др. Численные методы О., т.е. методы построения алгоритмов нахождения оп¬тимальных значений целевых функций и соответствующих точек области допустимых значений — развитой отдел современной вычислительной математики. См. Оптимальная задача.
[ http://slovar-lopatnikov.ru/]

Параллельные тексты EN-RU из ABB Review. Перевод компании Интент

- safe against cryptanalysis

- solve by cryptanalysis

- try all possible keys cryptanalysis

- withstand cryptanalysis

- analytical cryptanalysis

- automated cryptanalysis

- black box cryptanalysis

- brute-force cryptanalysis

- computer-aided cryptanalysis

- computer-backed cryptanalysis

- computer performing cryptanalysis

- conventional cryptanalysis

- deterministic cryptanalysis

- dictionary cryptanalysis

- differential cryptanalysis

- digram frequency cryptanalysis

- exhaustive cryptanalysis

- forward search cryptanalysis

- inference cryptanalysis

- key clustering cryptanalysis

- iteration cryptanalysis

- key-only cryptanalysis

- key-search cryptanalysis

- known plaintext cryptanalysis

- letter-frequency cryptanalysis

- letter-pattern cryptanalysis

- mathematical cryptanalysis

- meet-in-the-middle cryptanalysis

- message cryptanalysis

- offline cryptanalysis

- online cryptanalysis

- partially known plaintext cryptanalysis

- plaintext cryptanalysis

- probable message cryptanalysis

- probable word cryptanalysis

- purely mathematical cryptanalysis

- random cryptanalysis

- random key cryptanalysis

- related data cryptanalysis

- related-key cryptanalysis

- role-based cryptanalysis

- short cut cryptanalysis

- single-letter frequency cryptanalysis

- square root cryptanalysis

- statistical cryptanalysis

- systematic cryptanalysis

- trial-and-error cryptanalysis

 

модульный центр обработки данных (ЦОД)
-
[Интент]

Параллельные тексты EN-RU

[ http://loosebolts.wordpress.com/2008/12/02/our-vision-for-generation-4-modular-data-centers-one-way-of-getting-it-just-right/]

[ http://dcnt.ru/?p=9299#more-9299]

Data Centers are a hot topic these days. No matter where you look, this once obscure aspect of infrastructure is getting a lot of attention. For years, there have been cost pressures on IT operations and this, when the need for modern capacity is greater than ever, has thrust data centers into the spotlight. Server and rack density continues to rise, placing DC professionals and businesses in tighter and tougher situations while they struggle to manage their IT environments. And now hyper-scale cloud infrastructure is taking traditional technologies to limits never explored before and focusing the imagination of the IT industry on new possibilities.

В настоящее время центры обработки данных являются широко обсуждаемой темой. Куда ни посмотришь, этот некогда малоизвестный аспект инфраструктуры привлекает все больше внимания. Годами ИТ-отделы испытывали нехватку средств и это выдвинуло ЦОДы в центр внимания, в то время, когда необходимость в современных ЦОДах стала как никогда высокой. Плотность серверов и стоек продолжают расти, все больше усложняя ситуацию для специалистов в области охлаждения и организаций в их попытках управлять своими ИТ-средами. И теперь гипермасштабируемая облачная инфраструктура подвергает традиционные технологии невиданным ранее нагрузкам, и заставляет ИТ-индустрию искать новые возможности.

At Microsoft, we have focused a lot of thought and research around how to best operate and maintain our global infrastructure and we want to share those learnings. While obviously there are some aspects that we keep to ourselves, we have shared how we operate facilities daily, our technologies and methodologies, and, most importantly, how we monitor and manage our facilities. Whether it’s speaking at industry events, inviting customers to our “Microsoft data center conferences” held in our data centers, or through other media like blogging and white papers, we believe sharing best practices is paramount and will drive the industry forward. So in that vein, we have some interesting news to share.

В компании MicroSoft уделяют большое внимание изучению наилучших методов эксплуатации и технического обслуживания своей глобальной инфраструктуры и делятся результатами своих исследований. И хотя мы, конечно, не раскрываем некоторые аспекты своих исследований, мы делимся повседневным опытом эксплуатации дата-центров, своими технологиями и методологиями и, что важнее всего, методами контроля и управления своими объектами. Будь то доклады на отраслевых событиях, приглашение клиентов на наши конференции, которые посвящены центрам обработки данных MicroSoft, и проводятся в этих самых дата-центрах, или использование других средств, например, блоги и спецификации, мы уверены, что обмен передовым опытом имеет первостепенное значение и будет продвигать отрасль вперед.

Today we are sharing our Generation 4 Modular Data Center plan. This is our vision and will be the foundation of our cloud data center infrastructure in the next five years. We believe it is one of the most revolutionary changes to happen to data centers in the last 30 years. Joining me, in writing this blog are Daniel Costello, my director of Data Center Research and Engineering and Christian Belady, principal power and cooling architect. I feel their voices will add significant value to driving understanding around the many benefits included in this new design paradigm.

Сейчас мы хотим поделиться своим планом модульного дата-центра четвертого поколения. Это наше видение и оно будет основанием для инфраструктуры наших облачных дата-центров в ближайшие пять лет. Мы считаем, что это одно из самых революционных изменений в дата-центрах за последние 30 лет. Вместе со мной в написании этого блога участвовали Дэниел Костелло, директор по исследованиям и инжинирингу дата-центров, и Кристиан Белади, главный архитектор систем энергоснабжения и охлаждения. Мне кажется, что их авторитет придаст больше веса большому количеству преимуществ, включенных в эту новую парадигму проектирования.

Our “Gen 4” modular data centers will take the flexibility of containerized servers—like those in our Chicago data center—and apply it across the entire facility. So what do we mean by modular? Think of it like “building blocks”, where the data center will be composed of modular units of prefabricated mechanical, electrical, security components, etc., in addition to containerized servers.

Was there a key driver for the Generation 4 Data Center?

Наши модульные дата-центры “Gen 4” будут гибкими с контейнерами серверов – как серверы в нашем чикагском дата-центре. И гибкость будет применяться ко всему ЦОД. Итак, что мы подразумеваем под модульностью? Мы думаем о ней как о “строительных блоках”, где дата-центр будет состоять из модульных блоков изготовленных в заводских условиях электрических систем и систем охлаждения, а также систем безопасности и т.п., в дополнение к контейнеризованным серверам.
Был ли ключевой стимул для разработки дата-центра четвертого поколения?

If we were to summarize the promise of our Gen 4 design into a single sentence it would be something like this: “A highly modular, scalable, efficient, just-in-time data center capacity program that can be delivered anywhere in the world very quickly and cheaply, while allowing for continued growth as required.” Sounds too good to be true, doesn’t it? Well, keep in mind that these concepts have been in initial development and prototyping for over a year and are based on cumulative knowledge of previous facility generations and the advances we have made since we began our investments in earnest on this new design.

Если бы нам нужно было обобщить достоинства нашего проекта Gen 4 в одном предложении, это выглядело бы следующим образом: “Центр обработки данных с высоким уровнем модульности, расширяемости, и энергетической эффективности, а также возможностью постоянного расширения, в случае необходимости, который можно очень быстро и дешево развертывать в любом месте мира”. Звучит слишком хорошо для того чтобы быть правдой, не так ли? Ну, не забывайте, что эти концепции находились в процессе начальной разработки и создания опытного образца в течение более одного года и основываются на опыте, накопленном в ходе развития предыдущих поколений ЦОД, а также успехах, сделанных нами со времени, когда мы начали вкладывать серьезные средства в этот новый проект.

One of the biggest challenges we’ve had at Microsoft is something Mike likes to call the ‘Goldilock’s Problem’. In a nutshell, the problem can be stated as:

The worst thing we can do in delivering facilities for the business is not have enough capacity online, thus limiting the growth of our products and services.

Одну из самых больших проблем, с которыми приходилось сталкиваться Майкрософт, Майк любит называть ‘Проблемой Лютика’. Вкратце, эту проблему можно выразить следующим образом:

Самое худшее, что может быть при строительстве ЦОД для бизнеса, это не располагать достаточными производственными мощностями, и тем самым ограничивать рост наших продуктов и сервисов.

The second worst thing we can do in delivering facilities for the business is to have too much capacity online.

А вторым самым худшим моментом в этой сфере может слишком большое количество производственных мощностей.

This has led to a focus on smart, intelligent growth for the business — refining our overall demand picture. It can’t be too hot. It can’t be too cold. It has to be ‘Just Right!’ The capital dollars of investment are too large to make without long term planning. As we struggled to master these interesting challenges, we had to ensure that our technological plan also included solutions for the business and operational challenges we faced as well.
So let’s take a high level look at our Generation 4 design

Это заставило нас сосредоточиваться на интеллектуальном росте для бизнеса — refining our overall demand picture. Это не должно быть слишком горячим. И это не должно быть слишком холодным. Это должно быть ‘как раз, таким как надо!’ Нельзя делать такие большие капиталовложения без долгосрочного планирования. Пока мы старались решить эти интересные проблемы, мы должны были гарантировать, что наш технологический план будет также включать решения для коммерческих и эксплуатационных проблем, с которыми нам также приходилось сталкиваться.
Давайте рассмотрим наш проект дата-центра четвертого поколения

Are you ready for some great visuals? Check out this video at Soapbox. Click here for the Microsoft 4th Gen Video.

It’s a concept video that came out of my Data Center Research and Engineering team, under Daniel Costello, that will give you a view into what we think is the future.

From a configuration, construct-ability and time to market perspective, our primary goals and objectives are to modularize the whole data center. Not just the server side (like the Chicago facility), but the mechanical and electrical space as well. This means using the same kind of parts in pre-manufactured modules, the ability to use containers, skids, or rack-based deployments and the ability to tailor the Redundancy and Reliability requirements to the application at a very specific level.

Посмотрите это видео, перейдите по ссылке для просмотра видео о Microsoft 4th Gen:

Это концептуальное видео, созданное командой отдела Data Center Research and Engineering, возглавляемого Дэниелом Костелло, которое даст вам наше представление о будущем.

С точки зрения конфигурации, строительной технологичности и времени вывода на рынок, нашими главными целями и задачами агрегатирование всего дата-центра. Не только серверную часть, как дата-центр в Чикаго, но также системы охлаждения и электрические системы. Это означает применение деталей одного типа в сборных модулях, возможность использования контейнеров, салазок, или стоечных систем, а также возможность подстраивать требования избыточности и надежности для данного приложения на очень специфичном уровне.

Our goals from a cost perspective were simple in concept but tough to deliver. First and foremost, we had to reduce the capital cost per critical Mega Watt by the class of use. Some applications can run with N-level redundancy in the infrastructure, others require a little more infrastructure for support. These different classes of infrastructure requirements meant that optimizing for all cost classes was paramount. At Microsoft, we are not a one trick pony and have many Online products and services (240+) that require different levels of operational support. We understand that and ensured that we addressed it in our design which will allow us to reduce capital costs by 20%-40% or greater depending upon class.

Нашими целями в области затрат были концептуально простыми, но трудно реализуемыми. В первую очередь мы должны были снизить капитальные затраты в пересчете на один мегаватт, в зависимости от класса резервирования. Некоторые приложения могут вполне работать на базе инфраструктуры с резервированием на уровне N, то есть без резервирования, а для работы других приложений требуется больше инфраструктуры. Эти разные классы требований инфраструктуры подразумевали, что оптимизация всех классов затрат имеет преобладающее значение. В Майкрософт мы не ограничиваемся одним решением и располагаем большим количеством интерактивных продуктов и сервисов (240+), которым требуются разные уровни эксплуатационной поддержки. Мы понимаем это, и учитываем это в своем проекте, который позволит нам сокращать капитальные затраты на 20%-40% или более в зависимости от класса.

For example, non-critical or geo redundant applications have low hardware reliability requirements on a location basis. As a result, Gen 4 can be configured to provide stripped down, low-cost infrastructure with little or no redundancy and/or temperature control. Let’s say an Online service team decides that due to the dramatically lower cost, they will simply use uncontrolled outside air with temperatures ranging 10-35 C and 20-80% RH. The reality is we are already spec-ing this for all of our servers today and working with server vendors to broaden that range even further as Gen 4 becomes a reality. For this class of infrastructure, we eliminate generators, chillers, UPSs, and possibly lower costs relative to traditional infrastructure.

Например, некритичные или гео-избыточные системы имеют низкие требования к аппаратной надежности на основе местоположения. В результате этого, Gen 4 можно конфигурировать для упрощенной, недорогой инфраструктуры с низким уровнем (или вообще без резервирования) резервирования и / или температурного контроля. Скажем, команда интерактивного сервиса решает, что, в связи с намного меньшими затратами, они будут просто использовать некондиционированный наружный воздух с температурой 10-35°C и влажностью 20-80% RH. В реальности мы уже сегодня предъявляем эти требования к своим серверам и работаем с поставщиками серверов над еще большим расширением диапазона температур, так как наш модуль и подход Gen 4 становится реальностью. Для подобного класса инфраструктуры мы удаляем генераторы, чиллеры, ИБП, и, возможно, будем предлагать более низкие затраты, по сравнению с традиционной инфраструктурой.

Applications that demand higher level of redundancy or temperature control will use configurations of Gen 4 to meet those needs, however, they will also cost more (but still less than traditional data centers). We see this cost difference driving engineering behavioral change in that we predict more applications will drive towards Geo redundancy to lower costs.

Системы, которым требуется более высокий уровень резервирования или температурного контроля, будут использовать конфигурации Gen 4, отвечающие этим требованиям, однако, они будут также стоить больше. Но все равно они будут стоить меньше, чем традиционные дата-центры. Мы предвидим, что эти различия в затратах будут вызывать изменения в методах инжиниринга, и по нашим прогнозам, это будет выражаться в переходе все большего числа систем на гео-избыточность и меньшие затраты.

Another cool thing about Gen 4 is that it allows us to deploy capacity when our demand dictates it. Once finalized, we will no longer need to make large upfront investments. Imagine driving capital costs more closely in-line with actual demand, thus greatly reducing time-to-market and adding the capacity Online inherent in the design. Also reduced is the amount of construction labor required to put these “building blocks” together. Since the entire platform requires pre-manufacture of its core components, on-site construction costs are lowered. This allows us to maximize our return on invested capital.

Еще одно достоинство Gen 4 состоит в том, что он позволяет нам разворачивать дополнительные мощности, когда нам это необходимо. Как только мы закончим проект, нам больше не нужно будет делать большие начальные капиталовложения. Представьте себе возможность более точного согласования капитальных затрат с реальными требованиями, и тем самым значительного снижения времени вывода на рынок и интерактивного добавления мощностей, предусматриваемого проектом. Также снижен объем строительных работ, требуемых для сборки этих “строительных блоков”. Поскольку вся платформа требует предварительного изготовления ее базовых компонентов, затраты на сборку также снижены. Это позволит нам увеличить до максимума окупаемость своих капиталовложений.
Мы все подвергаем сомнению

In our design process, we questioned everything. You may notice there is no roof and some might be uncomfortable with this. We explored the need of one and throughout our research we got some surprising (positive) results that showed one wasn’t needed.

В своем процессе проектирования мы все подвергаем сомнению. Вы, наверное, обратили внимание на отсутствие крыши, и некоторым специалистам это могло не понравиться. Мы изучили необходимость в крыше и в ходе своих исследований получили удивительные результаты, которые показали, что крыша не нужна.
Серийное производство дата центров

In short, we are striving to bring Henry Ford’s Model T factory to the data center. http://en.wikipedia.org/wiki/Henry_Ford#Model_T. Gen 4 will move data centers from a custom design and build model to a commoditized manufacturing approach. We intend to have our components built in factories and then assemble them in one location (the data center site) very quickly. Think about how a computer, car or plane is built today. Components are manufactured by different companies all over the world to a predefined spec and then integrated in one location based on demands and feature requirements. And just like Henry Ford’s assembly line drove the cost of building and the time-to-market down dramatically for the automobile industry, we expect Gen 4 to do the same for data centers. Everything will be pre-manufactured and assembled on the pad.

Мы хотим применить модель автомобильной фабрики Генри Форда к дата-центру. Проект Gen 4 будет способствовать переходу от модели специализированного проектирования и строительства к товарно-производственному, серийному подходу. Мы намерены изготавливать свои компоненты на заводах, а затем очень быстро собирать их в одном месте, в месте строительства дата-центра. Подумайте о том, как сегодня изготавливается компьютер, автомобиль или самолет. Компоненты изготавливаются по заранее определенным спецификациям разными компаниями во всем мире, затем собираются в одном месте на основе спроса и требуемых характеристик. И точно так же как сборочный конвейер Генри Форда привел к значительному уменьшению затрат на производство и времени вывода на рынок в автомобильной промышленности, мы надеемся, что Gen 4 сделает то же самое для дата-центров. Все будет предварительно изготавливаться и собираться на месте.
Невероятно энергоэффективный ЦОД

And did we mention that this platform will be, overall, incredibly energy efficient? From a total energy perspective not only will we have remarkable PUE values, but the total cost of energy going into the facility will be greatly reduced as well. How much energy goes into making concrete? Will we need as much of it? How much energy goes into the fuel of the construction vehicles? This will also be greatly reduced! A key driver is our goal to achieve an average PUE at or below 1.125 by 2012 across our data centers. More than that, we are on a mission to reduce the overall amount of copper and water used in these facilities. We believe these will be the next areas of industry attention when and if the energy problem is solved. So we are asking today…“how can we build a data center with less building”?

А мы упоминали, что эта платформа будет, в общем, невероятно энергоэффективной? С точки зрения общей энергии, мы получим не только поразительные значения PUE, но общая стоимость энергии, затраченной на объект будет также значительно снижена. Сколько энергии идет на производство бетона? Нам нужно будет столько энергии? Сколько энергии идет на питание инженерных строительных машин? Это тоже будет значительно снижено! Главным стимулом является достижение среднего PUE не больше 1.125 для всех наших дата-центров к 2012 году. Более того, у нас есть задача сокращения общего количества меди и воды в дата-центрах. Мы думаем, что эти задачи станут следующей заботой отрасли после того как будет решена энергетическая проблема. Итак, сегодня мы спрашиваем себя…“как можно построить дата-центр с меньшим объемом строительных работ”?
Строительство дата центров без чиллеров

We have talked openly and publicly about building chiller-less data centers and running our facilities using aggressive outside economization. Our sincerest hope is that Gen 4 will completely eliminate the use of water. Today’s data centers use massive amounts of water and we see water as the next scarce resource and have decided to take a proactive stance on making water conservation part of our plan.

Мы открыто и публично говорили о строительстве дата-центров без чиллеров и активном использовании в наших центрах обработки данных технологий свободного охлаждения или фрикулинга. Мы искренне надеемся, что Gen 4 позволит полностью отказаться от использования воды. Современные дата-центры расходуют большие объемы воды и так как мы считаем воду следующим редким ресурсом, мы решили принять упреждающие меры и включить экономию воды в свой план.

By sharing this with the industry, we believe everyone can benefit from our methodology. While this concept and approach may be intimidating (or downright frightening) to some in the industry, disclosure ultimately is better for all of us.

Делясь этим опытом с отраслью, мы считаем, что каждый сможет извлечь выгоду из нашей методологией. Хотя эта концепция и подход могут показаться пугающими (или откровенно страшными) для некоторых отраслевых специалистов, раскрывая свои планы мы, в конечном счете, делаем лучше для всех нас.

Gen 4 design (even more than just containers), could reduce the ‘religious’ debates in our industry. With the central spine infrastructure in place, containers or pre-manufactured server halls can be either AC or DC, air-side economized or water-side economized, or not economized at all (though the sanity of that might be questioned). Gen 4 will allow us to decommission, repair and upgrade quickly because everything is modular. No longer will we be governed by the initial decisions made when constructing the facility. We will have almost unlimited use and re-use of the facility and site. We will also be able to use power in an ultra-fluid fashion moving load from critical to non-critical as use and capacity requirements dictate.

Проект Gen 4 позволит уменьшить ‘религиозные’ споры в нашей отрасли. Располагая базовой инфраструктурой, контейнеры или сборные серверные могут оборудоваться системами переменного или постоянного тока, воздушными или водяными экономайзерами, или вообще не использовать экономайзеры. Хотя можно подвергать сомнению разумность такого решения. Gen 4 позволит нам быстро выполнять работы по выводу из эксплуатации, ремонту и модернизации, поскольку все будет модульным. Мы больше не будем руководствоваться начальными решениями, принятыми во время строительства дата-центра. Мы сможем использовать этот дата-центр и инфраструктуру в течение почти неограниченного периода времени. Мы также сможем применять сверхгибкие методы использования электрической энергии, переводя оборудование в режимы критической или некритической нагрузки в соответствии с требуемой мощностью.
Gen 4 – это стандартная платформа

Finally, we believe this is a big game changer. Gen 4 will provide a standard platform that our industry can innovate around. For example, all modules in our Gen 4 will have common interfaces clearly defined by our specs and any vendor that meets these specifications will be able to plug into our infrastructure. Whether you are a computer vendor, UPS vendor, generator vendor, etc., you will be able to plug and play into our infrastructure. This means we can also source anyone, anywhere on the globe to minimize costs and maximize performance. We want to help motivate the industry to further innovate—with innovations from which everyone can reap the benefits.

Наконец, мы уверены, что это будет фактором, который значительно изменит ситуацию. Gen 4 будет представлять собой стандартную платформу, которую отрасль сможет обновлять. Например, все модули в нашем Gen 4 будут иметь общепринятые интерфейсы, четко определяемые нашими спецификациями, и оборудование любого поставщика, которое отвечает этим спецификациям можно будет включать в нашу инфраструктуру. Независимо от того производите вы компьютеры, ИБП, генераторы и т.п., вы сможете включать свое оборудование нашу инфраструктуру. Это означает, что мы также сможем обеспечивать всех, в любом месте земного шара, тем самым сводя до минимума затраты и максимальной увеличивая производительность. Мы хотим создать в отрасли мотивацию для дальнейших инноваций – инноваций, от которых каждый сможет получать выгоду.
Главные характеристики дата-центров четвертого поколения Gen4

To summarize, the key characteristics of our Generation 4 data centers are:

Scalable
Plug-and-play spine infrastructure
Factory pre-assembled: Pre-Assembled Containers (PACs) & Pre-Manufactured Buildings (PMBs)
Rapid deployment
De-mountable
Reduce TTM
Reduced construction
Sustainable measures

Ниже приведены главные характеристики дата-центров четвертого поколения Gen 4:

Расширяемость;
Готовая к использованию базовая инфраструктура;
Изготовление в заводских условиях: сборные контейнеры (PAC) и сборные здания (PMB);
Быстрота развертывания;
Возможность демонтажа;
Снижение времени вывода на рынок (TTM);
Сокращение сроков строительства;
Экологичность;

Map applications to DC Class

We hope you join us on this incredible journey of change and innovation!

Long hours of research and engineering time are invested into this process. There are still some long days and nights ahead, but the vision is clear. Rest assured however, that we as refine Generation 4, the team will soon be looking to Generation 5 (even if it is a bit farther out). There is always room to get better.

Использование систем электропитания постоянного тока.

Мы надеемся, что вы присоединитесь к нам в этом невероятном путешествии по миру изменений и инноваций!

На этот проект уже потрачены долгие часы исследований и проектирования. И еще предстоит потратить много дней и ночей, но мы имеем четкое представление о конечной цели. Однако будьте уверены, что как только мы доведем до конца проект модульного дата-центра четвертого поколения, мы вскоре начнем думать о проекте дата-центра пятого поколения. Всегда есть возможность для улучшений.

So if you happen to come across Goldilocks in the forest, and you are curious as to why she is smiling you will know that she feels very good about getting very close to ‘JUST RIGHT’.

Generations of Evolution – some background on our data center designs

Так что, если вы встретите в лесу девочку по имени Лютик, и вам станет любопытно, почему она улыбается, вы будете знать, что она очень довольна тем, что очень близко подошла к ‘ОПИМАЛЬНОМУ РЕШЕНИЮ’.
Поколения эволюции – история развития наших дата-центров

We thought you might be interested in understanding what happened in the first three generations of our data center designs. When Ray Ozzie wrote his Software plus Services memo it posed a very interesting challenge to us. The winds of change were at ‘tornado’ proportions. That “plus Services” tag had some significant (and unstated) challenges inherent to it. The first was that Microsoft was going to evolve even further into an operations company. While we had been running large scale Internet services since 1995, this development lead us to an entirely new level. Additionally, these “services” would span across both Internet and Enterprise businesses. To those of you who have to operate “stuff”, you know that these are two very different worlds in operational models and challenges. It also meant that, to achieve the same level of reliability and performance required our infrastructure was going to have to scale globally and in a significant way.

Мы подумали, что может быть вам будет интересно узнать историю первых трех поколений наших центров обработки данных. Когда Рэй Оззи написал свою памятную записку Software plus Services, он поставил перед нами очень интересную задачу. Ветра перемен двигались с ураганной скоростью. Это окончание “plus Services” скрывало в себе какие-то значительные и неопределенные задачи. Первая заключалась в том, что Майкрософт собиралась в еще большей степени стать операционной компанией. Несмотря на то, что мы управляли большими интернет-сервисами, начиная с 1995 г., эта разработка подняла нас на абсолютно новый уровень. Кроме того, эти “сервисы” охватывали интернет-компании и корпорации. Тем, кому приходится всем этим управлять, известно, что есть два очень разных мира в области операционных моделей и задач. Это также означало, что для достижения такого же уровня надежности и производительности требовалось, чтобы наша инфраструктура располагала значительными возможностями расширения в глобальных масштабах.

It was that intense atmosphere of change that we first started re-evaluating data center technology and processes in general and our ideas began to reach farther than what was accepted by the industry at large. This was the era of Generation 1. As we look at where most of the world’s data centers are today (and where our facilities were), it represented all the known learning and design requirements that had been in place since IBM built the first purpose-built computer room. These facilities focused more around uptime, reliability and redundancy. Big infrastructure was held accountable to solve all potential environmental shortfalls. This is where the majority of infrastructure in the industry still is today.

Именно в этой атмосфере серьезных изменений мы впервые начали переоценку ЦОД-технологий и технологий вообще, и наши идеи начали выходить за пределы общепринятых в отрасли представлений. Это была эпоха ЦОД первого поколения. Когда мы узнали, где сегодня располагается большинство мировых дата-центров и где находятся наши предприятия, это представляло весь опыт и навыки проектирования, накопленные со времени, когда IBM построила первую серверную. В этих ЦОД больше внимания уделялось бесперебойной работе, надежности и резервированию. Большая инфраструктура была призвана решать все потенциальные экологические проблемы. Сегодня большая часть инфраструктуры все еще находится на этом этапе своего развития.

We soon realized that traditional data centers were quickly becoming outdated. They were not keeping up with the demands of what was happening technologically and environmentally. That’s when we kicked off our Generation 2 design. Gen 2 facilities started taking into account sustainability, energy efficiency, and really looking at the total cost of energy and operations.

Очень быстро мы поняли, что стандартные дата-центры очень быстро становятся устаревшими. Они не поспевали за темпами изменений технологических и экологических требований. Именно тогда мы стали разрабатывать ЦОД второго поколения. В этих дата-центрах Gen 2 стали принимать во внимание такие факторы как устойчивое развитие, энергетическая эффективность, а также общие энергетические и эксплуатационные.

No longer did we view data centers just for the upfront capital costs, but we took a hard look at the facility over the course of its life. Our Quincy, Washington and San Antonio, Texas facilities are examples of our Gen 2 data centers where we explored and implemented new ways to lessen the impact on the environment. These facilities are considered two leading industry examples, based on their energy efficiency and ability to run and operate at new levels of scale and performance by leveraging clean hydro power (Quincy) and recycled waste water (San Antonio) to cool the facility during peak cooling months.

Мы больше не рассматривали дата-центры только с точки зрения начальных капитальных затрат, а внимательно следили за работой ЦОД на протяжении его срока службы. Наши объекты в Куинси, Вашингтоне, и Сан-Антонио, Техас, являются образцами наших ЦОД второго поколения, в которых мы изучали и применяли на практике новые способы снижения воздействия на окружающую среду. Эти объекты считаются двумя ведущими отраслевыми примерами, исходя из их энергетической эффективности и способности работать на новых уровнях производительности, основанных на использовании чистой энергии воды (Куинси) и рециклирования отработанной воды (Сан-Антонио) для охлаждения объекта в самых жарких месяцах.

As we were delivering our Gen 2 facilities into steel and concrete, our Generation 3 facilities were rapidly driving the evolution of the program. The key concepts for our Gen 3 design are increased modularity and greater concentration around energy efficiency and scale. The Gen 3 facility will be best represented by the Chicago, Illinois facility currently under construction. This facility will seem very foreign compared to the traditional data center concepts most of the industry is comfortable with. In fact, if you ever sit around in our container hanger in Chicago it will look incredibly different from a traditional raised-floor data center. We anticipate this modularization will drive huge efficiencies in terms of cost and operations for our business. We will also introduce significant changes in the environmental systems used to run our facilities. These concepts and processes (where applicable) will help us gain even greater efficiencies in our existing footprint, allowing us to further maximize infrastructure investments.

Так как наши ЦОД второго поколения строились из стали и бетона, наши центры обработки данных третьего поколения начали их быстро вытеснять. Главными концептуальными особенностями ЦОД третьего поколения Gen 3 являются повышенная модульность и большее внимание к энергетической эффективности и масштабированию. Дата-центры третьего поколения лучше всего представлены объектом, который в настоящее время строится в Чикаго, Иллинойс. Этот ЦОД будет выглядеть очень необычно, по сравнению с общепринятыми в отрасли представлениями о дата-центре. Действительно, если вам когда-либо удастся побывать в нашем контейнерном ангаре в Чикаго, он покажется вам совершенно непохожим на обычный дата-центр с фальшполом. Мы предполагаем, что этот модульный подход будет способствовать значительному повышению эффективности нашего бизнеса в отношении затрат и операций. Мы также внесем существенные изменения в климатические системы, используемые в наших ЦОД. Эти концепции и технологии, если применимо, позволят нам добиться еще большей эффективности наших существующих дата-центров, и тем самым еще больше увеличивать капиталовложения в инфраструктуру.

This is definitely a journey, not a destination industry. In fact, our Generation 4 design has been under heavy engineering for viability and cost for over a year. While the demand of our commercial growth required us to make investments as we grew, we treated each step in the learning as a process for further innovation in data centers. The design for our future Gen 4 facilities enabled us to make visionary advances that addressed the challenges of building, running, and operating facilities all in one concerted effort.

Это определенно путешествие, а не конечный пункт назначения. На самом деле, наш проект ЦОД четвертого поколения подвергался серьезным испытаниям на жизнеспособность и затраты на протяжении целого года. Хотя необходимость в коммерческом росте требовала от нас постоянных капиталовложений, мы рассматривали каждый этап своего развития как шаг к будущим инновациям в области дата-центров. Проект наших будущих ЦОД четвертого поколения Gen 4 позволил нам делать фантастические предположения, которые касались задач строительства, управления и эксплуатации объектов как единого упорядоченного процесса.

Тематики

• ЦОДы (центры обработки данных)

Синонимы

• модульный ЦОД

EN

• modular data center

get

❢ This much-used verb has no multi-purpose equivalent in French and therefore is very often translated by choosing a synonym: to get lunch = to prepare lunch = préparer le déjeuner. get is used in many idiomatic expressions ( to get something off one's chest etc) and translations will be found in the appropriate entry (chest etc). This is also true of offensive comments ( get stuffed etc) where the appropriate entry would be stuff. Remember that when get is used to express the idea that a job is done not by you but by somebody else ( to get a room painted etc) faire is used in French followed by an infinitive ( faire repeindre une pièce etc). When get has the meaning of become and is followed by an adjective (to get rich/drunk etc) devenir is sometimes useful but check the appropriate entry (rich, drunk etc) as a single verb often suffices ( s'enrichir, s'enivrer etc). For examples and further uses of get see the entry below.

A vtr ( p prés - tt- ; prét got ; pp got, gotten US)

1 ( receive) recevoir [letter, school report, grant] ; recevoir, percevoir [salary, pension] ; TV, Radio capter [channel, programme] ; did you get much for it? est-ce que tu en as tiré beaucoup d'argent? ; what did you get for your car? combien as-tu revendu ta voiture? ; we get a lot of rain il pleut beaucoup ici ; our garden gets a lot of sun notre jardin est bien ensoleillé ; we get a lot of tourists nous avons beaucoup de touristes ; you get lots of attachments with this cleaner il y a beaucoup d'accessoires fournis avec cet aspirateur ; you get what you pay for il faut y mettre le prix ; he's getting help with his science il se fait aider en sciences ;

2 ( inherit) to get sth from sb lit hériter qch de qn [article, money] ; fig tenir qch de qn [trait, feature] ;

3 ( obtain) ( by applying) obtenir [permission, divorce, custody, licence] ; trouver [job] ; ( by contacting) trouver [plumber, accountant] ; appeler [taxi] ; ( by buying) acheter [food item, clothing] (from chez) ; avoir [theatre seat, ticket] ; to get something for nothing/at a discount avoir qch gratuitement/avec une réduction ; to get sb sth, to get sth for sb ( by buying) acheter qch à qn ; I'll get sth to eat at the airport je mangerai qch à l'aéroport ;

4 ( subscribe to) acheter [newspaper] ;

5 ( acquire) se faire [reputation] ; he got his money in oil il s'est fait de l'argent dans le pétrole ;

6 ( achieve) obtenir [grade, mark, answer] ; he got it right ( of calculation) il a obtenu le bon résultat ; ( of answer) il a répondu juste ; how many do I need to get? ( when scoring) il me faut combien? ; he's got four more points to get il faut encore qu'il obtienne quatre points ;

7 ( fetch) chercher [object, person, help] ; go and get a chair/Mr Matthews va chercher une chaise/M. Matthews ; to get sb sth, to get sth for sb aller chercher qch pour qn ; get her a chair va lui chercher une chaise ; can I get you your coat? est-ce que je peux vous apporter votre manteau? ;

8 (manoeuvre, move) to get sb/sth upstairs/downstairs faire monter/descendre qn/qch ; a car to me is just something to get me from A to B pour moi une voiture ne sert qu'à aller de A à B ; I'll get them there somehow je les ferai parvenir d'une façon ou d'une autre ; can you get between the truck and the wall? est-ce que tu peux te glisser entre le camion et le mur? ;

9 ( help progress) is this discussion getting us anywhere? est-ce que cette discussion est bien utile? ; I listened to him and where has it got me? je l'ai écouté mais à quoi ça m'a avancé? ; this is getting us nowhere ça ne nous avance à rien ; where will that get you? à quoi ça t'avancera? ;

10 ( contact) did you manage to get Harry on the phone? tu as réussi à avoir Harry au téléphone? ;

11 ( deal with) I'll get it ( of phone) je réponds ; ( of doorbell) j'y vais ;

12 ( prepare) préparer [breakfast, lunch etc] ;

13 ( take hold of) attraper [person] (by à) ; I've got you, don't worry je te tiens, ne t'inquiète pas ; to get sth from ou off prendre qch sur [shelf, table] ; to get sth from ou out of prendre qch dans [drawer, cupboard] ;

14 ○ ( oblige to give) to get sth from ou out of sb faire sortir qch à qn [money] ; fig obtenir qch de qn [truth] ;

15 ○ ( catch) gen arrêter [escapee] ; got you! gen je t'ai eu! ; ( caught in act) vu! ; a shark got him un requin l'a eu ; when I get you, you won't find it so funny quand tu auras affaire ○ à moi, tu trouveras ça moins drôle ;

16 Med attraper [disease] ; he got the measles from his sister sa sœur lui a passé la rougeole ;

17 ( use as transport) prendre [bus, train] ;

18 ( have) to have got avoir [object, money, friend etc] ; I've got a headache/bad back j'ai mal à la tête/au dos ;

19 ( start to have) to get (hold of) the idea ou impression that se mettre dans la tête que ;

20 ( suffer) to get a surprise être surpris ; to get a shock avoir un choc ; to get a bang on the head recevoir un coup sur la tête ;

21 ( be given as punishment) prendre [five years etc] ; avoir [fine] ; to get (a) detention être collé ○ ;

22 ( hit) to get sb/sth with toucher qn/qch avec [stone, arrow, ball] ; got it! ( of target) touché! ; the arrow got him in the heel la flèche l'a touché au talon ;

23 (understand, hear) comprendre ; I didn't get what you said/his last name je n'ai pas compris ce que tu as dit/son nom de famille ; did you get it? tu as compris? ; now let me get this right… alors si je comprends bien… ; ‘where did you hear that?’-‘I got it from Paul’ ‘où est-ce que tu as entendu ça?’-‘c'est Paul qui me l'a dit’ ; get this! he was arrested this morning tiens-toi bien! il a été arrêté ce matin ;

24 ○ (annoy, affect) what gets me is… ce qui m'agace c'est que… ; what really got me was… ce que je n'aimais pas c'était… ;

25 (learn, learn of) to get to do ○ finir par faire ; to get to like sb finir par apprécier qn ; how did you get to know ou hear of our organization? comment avez-vous entendu parler de notre organisation? ; we got to know them last year on a fait leur connaissance l'année dernière ;

26 ( have opportunity) to get to do avoir l'occasion de faire ; do you get to use the computer? est-ce que tu as l'occasion d'utiliser l'ordinateur? ; it's not fair, I never get to drive the tractor ce n'est pas juste, on ne me laisse jamais conduire le tracteur ; when do we get to eat the cake? quand est-ce qu'on va pouvoir manger le gâteau? ;

27 ( start) to get (to be) commencer à devenir ; he's getting to be proficient ou an expert il commence à devenir expert ; it got to be quite unpleasant ça a commencé à devenir plutôt désagréable ; he's getting to be a big boy now c'est un grand garçon maintenant ; to get to doing ○ commencer à faire ; we got to talking/dreaming about the holidays on a commencé à parler/rêver des vacances ; then I got to thinking that puis je me suis dit que ; we'll have to get going il va falloir y aller ;

28 ( must) to have got to do devoir faire [homework, chore] ; it's got to be done il faut le faire ; you've got to realize that il faut que tu te rendes compte que ; if I've got to go, I will s'il faut que j'y aille, j'irai ; there's got to be a reason il doit y avoir une raison ;

29 ( persuade) to get sb to do demander à qn de faire ; I got her to talk about her problems j'ai réussi à la faire parler de ses problèmes ; did you get anything out of her? est-ce que tu as réussi à la faire parler? ;

30 ( have somebody do) to get sth done faire faire qch ; to get the car repaired/valeted faire réparer/nettoyer la voiture ; to get one's hair cut se faire couper les cheveux ; how do you ever get anything done? comment est-ce que tu arrives à travailler? ;

31 ( cause) to get the car going faire démarrer la voiture ; to get the dishes washed faire la vaisselle ; this won't get the dishes washed! la vaisselle ne se fera pas toute seule! ; to get sb pregnant ○ mettre qn enceinte ○ ; as hot/cold as you can get it aussi chaud/froid que possible ; to get one's socks wet mouiller ses chaussettes ; to get one's finger trapped se coincer le doigt.

B vi ( p prés - tt- ; prét got ; pp got, gotten US)

1 ( become) devenir [suspicious, rich, old] ; how lucky/stupid can you get! il y en a qui ont de la chance/qui sont vraiment stupides! ; it's getting late il se fait tard ; how did he get like that? comment est-ce qu'il en est arrivé là? ;

2 ( forming passive) to get (oneself) killed/trapped se faire tuer/coincer ; to get hurt être blessé ;

3 ( become involved in) to get into ○ ( as hobby) se mettre à [astrology etc] ; ( as job) commencer dans [teaching, publishing] ; fig to get into a fight se battre ;

4 ( arrive) to get there arriver ; to get to the airport/Switzerland arriver à l'aéroport/en Suisse ; to get (up) to the top ( of hill etc) arriver au sommet ; how did your coat get here? comment est-ce que ton manteau est arrivé là? ; how did you get here? ( by what miracle) comment est-ce que tu es arrivé là? ; ( by what means) comment est-ce que tu es venu? ; where did you get to? où est-ce que tu étais passé? ; we've got to page 5 nous en sommes à la page 5 ;

5 ( progress) it got to 7 o'clock il était plus de 7 heures ; I'd got as far as underlining the title j'en étais à souligner le titre ; I'm getting nowhere with this essay je n'avance pas dans ma dissertation ; are you getting anywhere with your investigation? est-ce que votre enquête avance? ; now we're getting somewhere ( making progress) on avance vraiment ; ( receiving fresh lead) voilà quelque chose d'intéressant ; it's a slow process but we're getting there c'est un processus lent, mais on avance ; it's not perfect yet but we're getting there ce n'est pas encore parfait mais on avance ;

6 ○ ( put on) to get into mettre [pyjamas, overalls].

get ○ ! fiche-moi le camp ○ ! ; get along with you ○ ! ne sois pas ridicule! ; get away with you ○ ! arrête de raconter n'importe quoi ○ ! ; get her ○ ! regarde-moi ça! ; get him ○ in that hat! regarde-le avec ce chapeau! ; he got his ○ ( was killed) il a cassé sa pipe ○ ; I'll get you ○ for that je vais te le faire payer ○ ; I'm getting there je progresse ; it gets me right here! tu vas me faire pleurer! ; I've/he's got it bad ○ je suis/il est vraiment mordu ; I've got it je sais ; to get above oneself commencer à avoir la grosse tête ○ ; to get it together ○ se ressaisir ; to get it up ● bander ●, avoir une érection ; to get one's in ○ US prendre sa revanche ; to tell sb where to get off envoyer qn promener ; to get with it ○ se mettre dans le coup ○ ; what's got into her/them? qu'est-ce qui lui/leur a pris? ; where does he get off ○ ? pour qui se prend-il? ; you've got me there! alors là tu me poses une colle ○ !

■ get about

1 ( manage to move) se déplacer (by doing en faisant) ; she doesn't get about very well now elle a du mal à se déplacer maintenant ;

2 ( travel) voyager, se déplacer ; do you get about much in your job? vous voyagez beaucoup pour votre travail? ; he gets about a bit ( travels) il voyage pas mal ; ( knows people) il connaît du monde ;

3 ( be spread) [news] se répandre ; [rumour] courir, se répandre ; it got about that la nouvelle s'est répandue que, le bruit a couru que.

■ get across:

▶ get across

1 ( pass to other side) traverser ;

2 ( be communicated) [message] passer ;

▶ get across [sth] ( cross) traverser [river, road etc] ;

▶ get [sth] across

1 ( transport) how will we get it across? (over stream, gap etc) comment est-ce qu'on le/la fera passer de l'autre côté? ; I'll get a copy across to you (in separate office, building etc) je vous en ferai parvenir un exemplaire ;

2 ( communicate) faire passer [message, meaning] (to à) ;

▶ get across [sb] ○ US ( annoy) se mettre [qn] à dos ○ [person].

■ get ahead

1 ( make progress) [person] progresser ; to get ahead of prendre de l'avance sur [competitor] ;

2 ( go too fast) let's not get ahead of ourselves n'anticipons pas.

■ get along

1 ( progress) how's the project getting along? comment est-ce que le projet se présente? ; how are you getting along? ( in job) comment ça se passe? ; ( to sick or old person) comment ça va? ; ( in school subject) comment est-ce que ça se passe? ;

2 ( cope) s'en sortir ; we can't get along without a computer/him on ne s'en sortira pas sans ordinateur/lui ;

3 ( be suited as friends) bien s'entendre (with avec) ;

4 (go) I must be getting along il faut que j'y aille.

■ get around:

▶ get around

1 (move, spread) = get about ;

2 to get around to doing: she'll get around to visiting us eventually elle va bien finir par venir nous voir ; I must get around to reading his article il faut vraiment que je lise son article ; I haven't got around to it yet je n'ai pas encore eu le temps de m'en occuper ;

▶ get around [sth] ( circumvent) contourner [problem, law] ; there's no getting around it il n'y a rien à faire.

■ get at ○:

▶ get at [sb /sth]

1 ( reach) atteindre [object] ; arriver jusqu'à [person] ; fig découvrir [truth] ; let me get at her ( in anger) laissez-moi lui régler son compte ○ ;

2 ( spoil) the ants have got at the sugar les fourmis ont attaqué le sucre ;

3 ( criticize) être après [person] ;

4 ( intimidate) intimider [witness] ;

5 ( insinuate) what are you getting at? où est-ce que tu veux en venir?

■ get away:

▶ get away

1 ( leave) partir ;

2 ( escape) [person] s'échapper ; the fish got away le poisson s'est échappé ;

3 fig ( escape unpunished) to get away with a crime échapper à la justice ; you'll never get away with it! tu ne vas pas t'en tirer comme ça! ; he mustn't be allowed to get away with it il ne faut pas qu'il s'en tire à si bon compte ; she can get away with bright colours elle peut se permettre de porter des couleurs vives ;

▶ get [sb/sth] away ( for break) emmener [qn] se changer les idées ; to get sb away from a bad influence tenir qn à l'écart d'une mauvaise influence ; to get sth away from sb retirer qch à qn [weapon, dangerous object].

■ get away from:

▶ get away from [sth]

1 ( leave) quitter [town] ; I must get away from here ou this place! il faut que je parte d'ici! ; ‘get away from it all’ ( in advert) ‘évadez-vous de votre quotidien’ ;

2 fig ( deny) nier [fact] ; there's no getting away from it on ne peut pas le nier ;

3 fig ( leave behind) abandonner [practice, method] ;

▶ get away from [sb] lit, fig échapper à.

■ get back:

▶ get back

1 ( return) gen rentrer ; ( after short time) revenir ; when we get back à notre retour ;

2 ( move backwards) reculer ; get back! reculez! ;

3 ( take revenge) to get back at se venger de [aggressor] ;

▶ get back to [sth]

1 ( return to) rentrer à [house, city] ; revenir à [office, centre, point] ; we got back to Belgium nous sommes rentrés en Belgique ; when we get back to London à notre retour à Londres ;

2 ( return to former condition) revenir à [teaching, publishing] ; to get back to sleep se rendormir ; to get back to normal redevenir normal ;

3 ( return to earlier stage) revenir à [main topic, former point] ; to get back to your problem,… pour en revenir à votre problème,… ;

▶ get back to [sb]

1 ( return to) revenir à [group, person] ;

2 ( on telephone) I'll get right back to you je vous rappelle tout de suite ;

▶ get [sb/sth] back

1 ( return) ( personally) ramener [object, person] ; ( by post etc) renvoyer ; Sport ( in tennis etc) renvoyer [ball] ; when they got him back to his cell quand ils l'ont ramené dans sa cellule ;

2 ( regain) récupérer [lost object, loaned item] ; fig reprendre [strength] ; she got her money back elle a été remboursée ; she got her old job back on lui a redonné son travail ; he got his girlfriend back il s'est remis avec sa petite amie ○.

■ get behind:

▶ get behind ( delayed) prendre du retard ;

▶ get behind [sth] se mettre derrière [hedge, sofa etc].

■ get by

1 ( pass) passer ;

2 ( survive) se débrouiller (on, with avec) ; we'll never get by without him/them nous ne nous en sortirons jamais sans lui/eux.

■ get down:

▶ get down

1 ( descend) descendre (from, out of de) ;

2 ( leave table) quitter la table ;

3 ( lower oneself) ( to floor) se coucher ; ( to crouching position) se baisser ; to get down on one's knees s'agenouiller ; to get down to ( descend to reach) arriver à [lower level etc] ; atteindre [trapped person etc] ; ( apply oneself to) se mettre à [work] ; to get down to the pupils' level fig se mettre à la portée des élèves ; let's get down to business parlons affaires ; when you get right down to it quand on regarde d'un peu plus près ; to get down to doing se mettre à faire ;

▶ get down [sth] descendre [slope] ; if we get down the mountain alive si nous arrivons vivants en bas de la montagne ; when we got down the hill quand nous nous sommes retrouvés en bas de la colline ;

▶ get [sth] down, get down [sth]

1 ( from height) descendre [book, jar etc] ;

2 ( swallow) avaler [medicine, pill] ;

3 ( record) noter [speech, dictation] ;

▶ get [sb] down

1 ( from height) faire descendre [person] ;

2 ○ ( depress) déprimer [person].

■ get in:

▶ get in

1 lit ( to building) entrer ; ( to vehicle) monter ;

2 fig ( participate) to get in on réussir à s'introduire dans [project, scheme] ; to get in on the deal ○ faire partie du coup ;

3 ( return home) rentrer ;

4 ( arrive at destination) [train, coach] arriver ;

5 ( penetrate) [water, sunlight] pénétrer ;

6 Pol [Labour, Tories etc] passer ; [candidate] être élu ;

7 Sch, Univ [applicant] être admis ;

8 ( associate) to get in with se mettre bien avec [person] ; he's got in with a bad crowd il traîne avec des gens peu recommandables ;

▶ get [sth] in, get in [sth]

1 ( buy in) acheter [supplies] ;

2 ( fit into space) I can't get the drawer in je n'arrive pas à faire rentrer le tiroir ;

3 Agric ( harvest) rentrer [crop] ;

4 Hort ( plant) planter [bulbs etc] ;

5 (deliver, hand in) rendre [essay, competition entry] ;

6 ( include) (in article, book) placer [section, remark, anecdote] ; he got in a few punches il a distribué quelques coups ;

7 ( fit into schedule) faire [tennis, golf] ; I'll try to get in a bit of tennis ○ j'essayerai de faire un peu de tennis ;

▶ get [sb] in faire entrer [person].

■ get into:

▶ get into [sth]

1 ( enter) entrer dans [building] ; monter dans [vehicle] ;

2 ( be admitted) ( as member) devenir membre de [club] ; ( as student) être admis à [school, university] ; I didn't know what I was getting into fig je ne savais pas dans quoi je m'embarquais ;

3 ( squeeze into) rentrer dans [garment, size] ; ⇒ debt, habit, trouble ;

▶ get [sb/sth] into faire entrer [qn/qch] dans [good school, building, room, space].

■ get off:

▶ get off

1 ( from bus etc) descendre (at à) ;

2 ( start on journey) partir ;

3 ( leave work) finir ;

4 ○ ( escape punishment) s'en tirer (with avec) ;

5 to get off to partir pour [destination] ; did they get off to school OK? est-ce qu'ils sont partis sans problèmes pour l'école? ; ( make headway) to get off to a good/poor start prendre un bon/mauvais départ ; to get off to sleep s'endormir ; to get off on doing ○ péj ( get buzz from) prendre plaisir à faire ; to get off with, GB rencontrer, ramasser ○ pej [person] ;

▶ get off [sth]

1 ( climb down from) descendre de [wall, ledge] ;

2 ( alight from) descendre de [bus etc] ;

3 ( remove oneself from) get off my nice clean floor/the grass ne marche pas sur mon sol tout propre/la pelouse ;

4 fig ( depart from) s'écarter de [subject] ;

▶ get off ○ [sb] ( leave hold) get off me! lâche-moi! ;

▶ get [sb/sth] off

1 ( lift down) descendre [object] ; faire descendre [person] ;

2 ( dispatch) envoyer [parcel, letter, person] ; I've got the children off to school j'ai envoyé les enfants à l'école ;

3 ( remove) enlever [stain] ;

4 ○ ( send to sleep) endormir [baby].

■ get on:

▶ get on

1 ( climb aboard) monter (at à) ;

2 ( work) get on a bit faster/more sensibly travaille un peu plus vite/plus sérieusement ;

3 ( continue with work) let's get on! continuons! ;

4 GB ( like each other) bien s'entendre ;

5 ( fare) how did you get on? comment est-ce que ça s'est passé? ;

6 ( cope) how are you getting on? comment est-ce que tu t'en sors? ;

7 GB ( approach) he's getting on for 40 il approche des quarante ans ; it's getting on for midnight il est presque minuit ; there are getting on for 80 people ○ il y a presque 80 personnes ;

8 ( grow late) time's getting on le temps passe ;

9 ( grow old) to be getting on a bit commencer à vieillir ;

▶ get on [sth] ( board) monter dans [vehicle] ;

▶ get [sth] on, get on [sth] ( put on) mettre [boots, clothing] ; monter [tyre] ; mettre [lid, tap washer etc].

■ get onto:

▶ get onto [sth]

1 ( board) monter dans [vehicle] ;

2 ( be appointed) être nommé à [Board] ;

3 ( start to discuss) arriver à parler de [topic, subject] ;

4 GB ( contact) contacter ; I'll get on to the authorities je contacterai les autorités.

■ get on with:

▶ get on with [sth] ( continue to do) to get on with one's work/with preparing the meal continuer à travailler/à préparer le repas ; let's get on with the job! au travail! ;

▶ get on with [sb] GB s'entendre avec [person].

■ get out:

▶ get out

1 ( exit) sortir (through, by par) ; get out and don't come back! va-t'en et ne reviens pas! ; they'll never get out alive ils ne s'en sortiront jamais vivants ;

2 ( make social outing) sortir ; you should get out more tu devrais sortir plus ;

3 (resign, leave) partir ;

4 ( alight) descendre ;

5 ( be let out) [prisoner] être libéré ; he gets out on the 8th il sera libéré le 8 ;

6 ( leak) [news] être révélé ;

▶ get [sth] out, get out [sth]

1 ( bring out) sortir [handkerchief, ID card] ;

2 ( extract) retirer [cork, stuck object] ; extraire [tooth] ;

3 ( erase) enlever [stain] ;

4 ( take on loan) emprunter [library book] ;

5 ( produce) sortir [plans, product] ;

6 ( utter) I couldn't get the words out les mots ne voulaient pas sortir ;

7 ( solve) faire [puzzle] ;

▶ get [sb] out ( release) faire libérer [prisoner] ; to get sb out of sth ( free from detention) ( personally) libérer qn de qch ; ( by persuasion) faire libérer qn de qch [prisoner] ; to get sth out of sth ( bring out) sortir qch de qch [handkerchief etc] ; ( find and remove) récupérer qch dans qch [required object, stuck object] ; I can't get it out of my mind je ne peux pas l'effacer de mon esprit.

■ get out of:

▶ get out of [sth]

1 ( exit from) sortir de [building, bed] ;

2 ( alight from) descendre de [vehicle] ;

3 ( leave at end of) sortir de [meeting] ;

4 ( be freed from) être libéré de [prison] ;

5 ( withdraw from) quitter [organization] ; échapper à [responsibilities] ; he's got out of oil ○ ( as investment) il a vendu toutes ses actions dans le pétrole ;

6 ( avoid doing) s'arranger pour ne pas aller à [appointment, meeting] ; I'll try to get out of it j'essaierai de me libérer ; I accepted the invitation and now I can't get out of it j'ai accepté l'invitation et maintenant je ne peux pas me défiler ○ ; to get out of doing s'arranger pour ne pas faire ;

7 ( no longer do) perdre [habit] ;

8 ( gain from) what do you get out of your job? qu'est-ce que ton travail t'apporte? ; what will you get out of it? qu'est-ce que vous en retirerez?

■ get over:

▶ get over ( cross) passer ;

▶ get over [sth]

1 ( cross) traverser [bridge, stream] ;

2 ( recover from) se remettre de [illness, shock] ; to get over the fact that se remettre du fait que ; I can't get over it ( in amazement) je n'en reviens pas ; I couldn't get over how she looked ça m'a fait un choc de la voir comme ça ; I can't get over how you've grown je n'en reviens pas de ce que tu as grandi ;

3 ( surmount) surmonter [problem] ; to get sth over with en finir avec qch ; let's get it over with finissons-en ;

4 ( stop loving) oublier ; she never got over him elle ne l'a jamais oublié ;

▶ get [sb/sth] over

1 ( cause to cross) faire passer [injured person, object] ; faire passer [qn/ qch] au-dessus de [bridge, wall etc] ;

2 ( cause to arrive) get the plumber over here at once faites venir tout de suite le plombier ;

3 ( communicate) faire passer [message].

■ get round GB:

▶ get round = get around ;

▶ get round [sth] = get around [sth] ;

▶ get round ○ [sb] persuader [qn], avoir [qn] au sentiment ○ ; can't you get round him? est-ce que tu ne peux pas le persuader? ; she easily gets round her father elle fait tout ce qu'elle veut de son père.

■ get through:

▶ get through

1 ( squeeze through) passer ;

2 Telecom to get through to sb avoir qn au téléphone ; I couldn't get through je n'ai pas réussi à l'avoir ;

3 to get through to ( communicate with) convaincre [person] ;

4 ( arrive) [news, supplies] arriver ;

5 ( survive) s'en sortir (by doing en faisant) ;

6 Sch, Univ [examinee] réussir ;

▶ get through [sth]

1 ( make way through) traverser [checkpoint, mud] ;

2 ( reach end of) terminer [book, revision] ; finir [meal, task] ; [actor] finir [performance] ;

3 ( survive mentally) I thought I'd never get through the week j'ai cru que je ne tiendrais pas la semaine ;

4 ( complete successfully) [candidate, competitor] réussir à [exam, qualifying round] ; I got through the interview l'entretien s'est bien passé ;

5 (consume, use) manger [supply of food] ; boire [supply of drink] ; dépenser [money] ; I get through two notebooks a week il me faut or j'use deux carnets par semaine ;

▶ get [sb/sth] through

1 ( squeeze through) faire passer [car, object, person] ;

2 ( help to endure) [pills, encouragement, strength of character] aider [qn] à continuer ; her advice/these pills got me through the day ses conseils/ces comprimés m'ont aidé à tenir le coup ○ ;

3 ( help through frontier etc) faire passer [person, imported goods] ;

4 Sch, Univ ( help to pass) permettre à [qn] de réussir [candidate] ;

5 Pol faire passer [bill].

■ get together:

▶ get together ( assemble) se réunir (about, over pour discuter de) ;

▶ get [sb/sth] together, get together [sb/sth]

1 ( assemble) réunir [different people, groups] ;

2 ( accumulate) réunir [money] ; rassembler [food parcels, truckload] ;

3 ( form) former [company, action group].

■ get under:

▶ get under passer en-dessous ;

▶ get under [sth] passer sous [barrier, floorboards etc].

■ get up:

▶ get up

1 (from bed, chair etc) se lever (from de) ; get up off the grass! ne reste pas sur l'herbe! ;

2 (on horse, ledge etc) monter ; how did you get up there? comment est-ce que tu es monté là-haut? ;

3 Meteorol [storm] se préparer ; [wind] se lever ;

4 to get up to ( reach) arriver à [page, upper floor] ; what did you get up to? fig ( sth enjoyable) qu'est-ce que tu as fait de beau? ; ( sth mischievous) qu'est-ce que tu as fabriqué ○ ? ;

▶ get up [sth]

1 arriver en haut de [hill, ladder] ;

2 ( increase) augmenter [speed] ;

3 (start, muster) former [group] ; faire [petition] ; obtenir [support, sympathy] ;

▶ get [sth] up organiser ;

▶ get [oneself] up ○ in mettre [outfit].

1) проблема