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%的时间处于繁忙状态，但它们经常处于空闲状态，占用峰值功率的60%或更多。该项目由卡内基梅隆大学（Carnegie Mellon University）进行，研究数据中心容量的简单负载无关、分布式自动扩展，以降低功耗。我们的策略在不事先知道负载的情况下，通过改变负载、关闭服务器、将服务器置于睡眠状态或改变服务器运行的频率来扩展正在运行的服务器数量。我们的工作将在23台服务器的多层数据中心中全面实施，并结合排队理论分析，以减少解决方案的搜索空间，并指导算法的开发。我们期望开发出能够显著降低功耗的算法，同时仍然满足响应时间服务水平协议。这项工作的广泛影响包括与英特尔匹兹堡实验室的直接合作，出版了一本关于排队理论建模及其应用的教科书，每周指导和管理美国西部地区数学联盟，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.