Service Rate Control of On/Off Servers

开/关服务器的服务速率控制

• 批准号: RGPIN-2016-04518
• 负责人: Down, Douglas
• 金额: $ 2.62万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709232
• 关键词: Service; Rate; Control; Off; Servers

According to Natural Resources Canada, data centres support every sector of the Canadian economy and consume roughly one percent of the total electricity consumed in Canada. When sizing data centres, they are typically designed to accommodate peak demand. A consequence is that much of the time, the vast majority of servers are idle yet still consuming energy. If one could determine how to effectively turn servers off (and on), there are clearly significant gains to be made, both in terms of direct savings for data centre operators, but also for Canadian society due to the corresponding reduction in greenhouse gas emissions. The tradeoff to consider is that performance may decrease (in terms of waiting times) if servers need to be turned on to cope with rising demand, and that frequently turning servers on and off may reduce the lifetime of the equipment. The data centre problem just described is an example of a more general problem, one of systems consisting of multiple resources with variable demand, where the opportunity may present itself to shut down or remove resources while still effectively satisfying the current offered demand. This opportunity may allow an organization to reduce costs when demand is low, but if there is some delay to deploy resources when the demand increases (as may occur in any outsourcing decision, for example), there is a risk that the performance of the system may degrade to an unacceptable level. One may also not want to shut down or deploy resources too frequently, as there may be significant overhead associated with such decisions. Controlled queueing models are a natural means to study such problems. We will first study this problem when all resources (servers) are homogeneous, there is a single demand stream, and the decision maker has perfect information about the system. Using a combination of techniques including Markov Decision Processes, recent approaches to computing steady-state performance measures and examining limiting stochastic processes, we will characterize as much as possible optimal control policies - determining how many servers should always remain on and how to turn on and off the remaining servers. How these policies change when servers are heterogeneous and there are differing demand streams will then be studied. The assumption of perfect information is rarely achieved in applications in the field. However, knowing the form of optimal policies provides both a benchmark for what can be achieved, to measure the efficacy of policies which use less information. Deeper insights are possible - knowing the behaviour of optimal policies with perfect information may allow one to build an approximation of such behaviour into policies with less information. Significant effort will be spent on developing such policies, where they will be evaluated both analytically by constructing appropriate limiting processes and through simulation.

根据加拿大自然资源的数据，数据中心支持加拿大经济的每个部门，并消耗加拿大消耗的总电量的百分之一。当尺寸的数据中心尺寸时，它们通常是为满足高峰需求而设计的。结果是，在很多时候，绝大多数服务器都闲置了，但仍在消耗能量。如果人们能够确定如何有效关闭服务器（和ON），那么在直接节省了数据中心运营商的角度，以及加拿大社会，由于温室气体排放的相应减少，也可以取得显着收益。要考虑的权衡是，如果需要打开服务器以应对需求的增加，性能可能会降低（就等待时间而言），并且经常打开和关闭服务器可能会降低设备的寿命。 刚刚描述的数据中心问题是一个更普遍的问题的一个示例，是一个由有多种需求的多个资源组成的系统之一，在这种系统中，机会可能会出现自身关闭或删除资源，同时仍然有效地满足当前所提供的需求。 这个机会可能会使组织在需求较低时降低成本，但是如果在需求增加时有一些延迟来部署资源（例如，在任何外包决定中可能发生），则系统的性能可能会降低不可接受的水平。一个人也可能不想频繁地关闭或部署资源，因为可能与此类决策相关的大量开销。 受控的排队模型是研究此类问题的自然手段。当所有资源（服务器）均匀，有一个需求流，并且决策者具有有关系统的完美信息时，我们将首先研究此问题。使用包括马尔可夫决策过程在内的技术组合，计算稳态绩效指标并检查限制随机过程的最新方法，我们将尽可能多地表征最佳的最佳控制策略 - 确定应始终保留多少服务器以及如何打开和关闭其余的服务器。当服务器是异质的并且需要不同的需求流时，这些策略将如何变化。 在现场应用中，很少实现完美信息的假设。但是，了解最佳策略的形式既可以为可以实现的目标提供基准，以衡量使用较少信息的策略的功效。更深入的见解是可能的 - 了解具有完美信息的最佳策略的行为可能会使人们可以将这种行为近似地构建成具有更少信息的策略。将花费大量精力来制定此类政策，在该政策中，将通过构建适当的限制过程和模拟来分析评估它们。