CSR: Small: Simple Dynamic Traffic-Oblivious Power Management for Multi-Tier Web Clusters

虽然数据中心的能源成本每五年继续翻一番，但最令人失望的是，大部分电力都被浪费了。服务器平均只有10%-30%的时间忙碌，但它们经常处于空闲状态，利用60%或更多的峰值功率。该项目由卡内基梅隆大学实施，旨在研究数据中心容量的简单分布式自动扩展，以降低功耗。我们的策略根据更改负载、关闭服务器、将服务器置于休眠状态或更改运行频率来扩展打开的服务器数量，而无需事先知道负载。我们的工作结合了在23台服务器的多层数据中心的全面实施和排队论分析，以减少解的搜索空间，并指导算法的发展。我们希望开发出在满足响应时间服务级别协议的同时显著降低功耗的算法。这项工作的更广泛影响包括与英特尔匹兹堡实验室的直接合作，出版关于排队理论建模及其应用的教科书，以及每周指导和管理西宾夕法尼亚美国地区数学联盟，以培养未来最聪明的年轻人。

While energy costs of data centers continue to double every five years, what is most disappointing is that most of this power is wasted. Servers are only busy 10-30% of the time on average, but they are often left on, while idle, utilizing 60% or more of peak power. This project, being conducted at Carnegie Mellon University, investigates simple load-oblivious, distributed auto-scaling of the data center capacity to reduce power consumption. Our policies scale the number of servers that are on, as a function of changing load, shutting down servers, or putting them into sleep states, or changing the frequency at which they are run, without knowing the load in advance. Our work combines full-scale implementation in a 23-server multi-tier data center together with queueing-theoretic analysis to reduce the search space of solutions and guide the algorithmic development. We expect to develop algorithms which significantly reduce power consumption, while still meeting response time service level agreements. Broader impacts of this work include direct collaboration with Intel Pittsburgh labs, publication of a textbook on queueing-theoretic modeling and its applications, and weekly coaching and management of the Western PA American Regions Mathematics League, to train the smartest young minds of tomorrow.