The Impact of Escalator Performance on Passenger Experience
- Learn about the impact of escalator performance on passenger experience in terms of response time and throughput.
- Discover how the degree of parallelism affects the escalator's capacity to carry passengers.
- Understand the relationship between the number of passengers boarding the escalator and its degree of parallelism.
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卒論に使用するため、翻訳をお願いしたいです。コンピューター関係の書物の
卒論に使用するため、翻訳をお願いしたいです。コンピューター関係の書物の文章です。 Consider a building that has two floors, with an escalator to carry people from one floor to the other. Ignoring queuing delays, the response time for a passenger is the mean time taken by the escalator to ascend or descend one floor. The throughput (bandwidth) is the mean number of passengers that can be loaded or per second. Suppose that an average of five people step on the escalator in one second, and that the escalator takes an average of 10 seconds to go up one floor. The response time for a passenger, therefore, is 10 seconds, and the throughput of the escalator is 5 passengers/second. Thus, the degree of parallelism, which is the mean number of passengers carried simultaneously, is 5*10=50. To see this, mark a passenger with a daub of red paint as she steps on the escalator. In the ten seconds that she takes to reach the top, we expect that fifty more passengers boarded the escalator. Thus, when she steps off, the escalator carries an average of fifty passengers, which is its degree of parallelism.
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。
翻訳をお願いしたいです。コンピューター関係の書物の文章です。 Consider a building that has two floors, with an escalator to carry people from one floor to the other. Ignoring queuing delays, the response time for a passenger is the mean time taken by the escalator to ascend or descend one floor. The throughput (bandwidth) is the mean number of passengers that can be loaded or per second. Suppose that an average of five people step on the escalator in one second, and that the escalator takes an average of 10 seconds to go up one floor. The response time for a passenger, therefore, is 10 seconds, and the throughput of the escalator is 5 passengers/second. Thus, the degree of parallelism, which is the mean number of passengers carried simultaneously, is 5*10=50. To see this, mark a passenger with a daub of red paint as she steps on the escalator. In the ten seconds that she takes to reach the top, we expect that fifty more passengers boarded the escalator. Thus, when she steps off, the escalator carries an average of fifty passengers, which is its degree of parallelism.
- ベストアンサー
- 英語
- 卒論に使用するため、翻訳をお願いしたいです。コンピューター関係の書物の
卒論に使用するため、翻訳をお願いしたいです。コンピューター関係の書物の文章です。 If, in a system, on an average, 20 tasks complete in 10 seconds, and each task takes 3 seconds, what is the degree of parallelism? Solution : The throughput is 20/10 task/second, and the response time is 3 seconds. Thus, the degree of parallelism = 20/10*3=6. In other words, an average of six tasks execute in parallel in the system.
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- 卒論に使用するため、翻訳をお願いしたいです。コンピューター関係の書物の
卒論に使用するため、翻訳をお願いしたいです。コンピューター関係の書物の文章です。 We call the mean time to complete a task its response time and the mean number of tasks that can be completed in a unit time the throughput. There is an important relationship between throughput and response time that we will use often in this book: the mean number of concurrent activities in a system, also called its degree of parallelism, is the product of the throughput and the response time.
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。
翻訳をお願いしたいです。コンピューター関係の書物の文章です。 A system designer must typically optimize one or more performance metrics given a set of resource constrains. A performance metric measures some aspect of a system's performance, such as throughput, response time, cost development time, or mean time between failures(we will define these metrics more formally in Section 6.2). A resource constraint is a limitation on a resource, such as time, bandwidth, or computing power, that the design must obey.
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。
翻訳をお願いしたいです。コンピューター関係の書物の文章です。 If we could quantify and control every aspect of a system, then system design would be a relatively simple matter. Unfortunately there are several practical reasons why system design is both an art and a science. First, although we can quantitatively measure some aspects of system performance, such as throughput or response time, we cannot measure others, such as simplicity, scalability, modularity, and elegance. Yet a designer must make a series of trade- offs among these intangible quantities, appealing as much to good sense and personal choice as performance measurements. Second, rapid technological change can make constraint assumptions obsolete. A designer must not only meet the current set of design constraints, but also anticipate how future changes in technology might affect the design. The future is hard to predict, and a designer must appeal to instinct and intuition to make a design "future-proof." Third, market conditions may dictate that design requirements change when part of the design is already complete. Finally, international standards, which themselves change over time, may impose irksome and arbitrary constraints. These factors imply that, in real life, a designer is usually confronted with a complex, underspecified, multifactor optimization problem. In the face of these uncertainties, prescribing the one true path to system design is impossible.
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイト
翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイトのコピペはご遠慮ください。 An interesting view of multiplexing is to think of a multiplexed shared resource as an unshared virtual resource. Consider a customer using the services of a bank teller, as in Example6.7. While the teller is helping the customer, the fact that other customers are waiting in line is of no consequence. If we magically put a customer in suspended animation when she is waiting in line, and wake her up when the teller becomes available, then from her perspective, the teller is never unavailable. From this perspective, the bank teller is, therefore, an unshared virtual resource.
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイト
翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイトのコピペはご遠慮ください。 Consider an airline reservation system, where agents from any part of the world can make a reservation for seats on any flight on any airline. One design for this system is to send all reservation requests to a single central computer. This design is simple, but has two problems. First, if the central computer crashes, every agent is affected. Second, as the number of agents increases, we need to expand the capacity of the central computer. However, the number of reservation requests, particularly during peak travel periods, may increase beyond the capacity of the largest computer that we can build or buy. Then, the response time suffers, and system performance degrades. We can solve this problem by replacing the central computer with a set of regional reservation center that coordinate among themselves to maintain a consistent view of the system state(such as whether a night is full or not). Then, as the number of reservation requests increases, we can just add another reservation center. We must, however, pay for this with a communication overhead for coordination, and a complex network to interconnect the regional reservation centers.
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。
翻訳をお願いしたいです。コンピューター関係の書物の文章です。 System design is important not only in computer systems, but also in other areas, such as automobile design. For example, a car designer might try to maximize the reliability of a car(measured in the mean time between equipment failures) that costs less than $10,000 to build. In this example, the mean time between failures measures performance, and the resource constraint is money. In real life, of course, designs must try to simultaneously optimize many, possibly conflicting metrics (such as reliability, performance, and recyclability) while satisfying many constraints (such as the price of the car and the time allowed for the design).
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイト
翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイトのコピペはご遠慮ください。 The last constraint, scaling, is different from the others in that it constrains, not resources, but design elements. A centralized design, that is, a design that incorporates a single point of storage or control, is limited to the storage or processing capacity of the central point. Thus, an arbitrarily scalable design must minimize its use of centralized elements. This forces such designs to use distributed algorithms for control and coordination. Of course, a distributed system has its own problems, such as communication overhead and complex interconnection. Scaling is hard to measure, and arguments about whether a design can scale verge on the religious. Nevertheless, history shows that a system must scale well if it is to succeed.
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- 翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイト
翻訳をお願いしたいです。コンピューター関係の書物の文章です。翻訳サイトのコピペはご遠慮ください。 The last constraint, scaling, is different from the others in that it constrains, not resources, but design elements. A centralized design, that is, a design that incorporates a single point of storage or control, is limited to the storage or processing capacity of the central point. Thus, an arbitrarily scalable design must minimize its use of centralized elements. This forces such designs to use distributed algorithms for control and coordination. Of course, a distributed system has its own problems, such as communication overhead and complex interconnection. Scaling is hard to measure, and arguments about whether a design can scale verge on the religious. Nevertheless, history shows that a system must scale well if it is to succeed.
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