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.

据加拿大自然资源部称，数据中心支持加拿大经济的各个部门，消耗的电力约占加拿大总电力的百分之一。在调整数据中心规模时，它们通常旨在满足高峰需求。其结果是，大多数时间，绝大多数服务器处于空闲状态，但仍在消耗能源。如果能够确定如何有效地关闭（和打开）服务器，那么显然可以取得显着的收益，不仅可以为数据中心运营商节省直接成本，而且可以通过相应减少温室气体排放来为加拿大社会带来好处。需要考虑的权衡是，如果需要打开服务器来应对不断增长的需求，则性能可能会下降（就等待时间而言），并且频繁打开和关闭服务器可能会缩短设备的使用寿命。 刚刚描述的数据中心问题是一个更普遍的问题的示例，系统由具有可变需求的多个资源组成，其中可能会出现关闭或删除资源的机会，同时仍然有效地满足当前提供的需求。 这个机会可以让组织在需求较低时降低成本，但如果在需求增加时部署资源出现一些延迟（例如，任何外包决策中都可能发生），则存在系统性能下降的风险。可能会降低到不可接受的水平。人们可能也不希望过于频繁地关闭或部署资源，因为可能会产生与此类决策相关的大量开销。 受控排队模型是研究此类问题的自然方法。我们将首先在所有资源（服务器）都是同质的、存在单一需求流并且决策者拥有关于系统的完美信息的情况下研究这个问题。结合使用马尔可夫决策过程、计算稳态性能测量的最新方法以及检查限制随机过程等技术，我们将描述尽可能多的最佳控制策略 - 确定有多少服务器应始终保持开启状态以及如何开启和关闭关闭其余服务器。然后将研究当服务器异构且存在不同需求流时这些策略如何变化。 在该领域的应用中很少实现完美信息的假设。然而，了解最优政策的形式既可以为可以实现的目标提供基准，也可以衡量使用较少信息的政策的有效性。更深入的洞察是可能的——了解具有完美信息的最优策略的行为可能允许人们将这种行为的近似值构建到具有较少信息的策略中。我们将投入大量精力来制定此类政策，并通过构建适当的限制流程和模拟来对这些政策进行分析评估。