CSR: Small: Joint Power Optimization of Data Center Network and Servers with Correlation Analysis and Scalability

CSR：小型：通过相关性分析和可扩展性对数据中心网络和服务器进行联合功率优化

• 批准号: 1421452
• 负责人: Xiaorui Wang
• 金额: $ 40万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421452&HistoricalAwards=false
• 关键词: CSR; Small; Joint; Power; Optimization

Power optimization has become a key challenge in the design of today's data centers. Many recent studies have shown that there are typically three major power consumers in a data center: servers, cooling systems, and the data center network (DCN). While the power efficiency of data center cooling has been significantly improved in the recent years, it is foreseeable that servers and DCN are becoming the two most significant power consumers in the future. Unfortunately, while existing research efforts focus mainly on computer servers to lower their power consumption, only few studies have tried to address the power consumption of DCN, which can account for about 20% of the total power consumption of a data center. This project aims to design a correlation-aware power optimization framework that jointly minimizes the total power consumption of the DCN and servers in a data center. The success of this timely project can greatly impact the data center design by significantly reducing DCN power consumption.The main technical approach of the power optimization framework is correlation-aware server and traffic consolidations. Similar to servers, a DCN is also often underutilized. As a result, traffic flows can be consolidated onto a small set of links and switches, such that unused network devices can be shut down for power savings. Server and traffic consolidations should be conducted jointly because server consolidation without considering the DCN may cause traffic congestion and thus degraded network performance. On the other hand, server consolidation may change the DCN topology, allowing new opportunities for power savings. This framework is designed based on a key observation that the utilizations of different servers or the bandwidth demands of different flows usually do not peak at exactly the same time. Therefore, if the correlations among servers and traffic flows are considered, more power savings can be achieved during server and traffic consolidations. The power optimization framework also includes multi-dimensional DCN power optimization with flow completion time guarantees and highly scalable optimization algorithms for large-scale data centers.

电源优化已成为当今数据中心设计中的关键挑战。最近的许多研究表明，数据中心通常有三个主要的电力消耗者：服务器、冷却系统和数据中心网络（DCN）。虽然近年来数据中心冷却的功率效率已经显著提高，但可以预见，服务器和DCN正在成为未来最重要的两个功率消耗者。不幸的是，虽然现有的研究工作主要集中在计算机服务器上以降低其功耗，但只有很少的研究试图解决DCN的功耗，DCN的功耗约占数据中心总功耗的20%。该项目旨在设计一个相关性感知的功耗优化框架，共同最大限度地减少数据中心的DCN和服务器的总功耗。这个项目的成功可以通过显著降低DCN功耗来极大地影响数据中心的设计。功耗优化框架的主要技术方法是相关感知服务器和流量整合。与服务器类似，DCN也经常未得到充分利用。因此，可以将业务流合并到一小组链路和交换机上，从而可以关闭未使用的网络设备以节省功率。服务器和流量整合应该联合进行，因为不考虑DCN的服务器整合可能会导致流量拥塞，从而降低网络性能。另一方面，服务器整合可能会改变DCN拓扑结构，从而带来新的节能机会。该框架的设计基于一个关键的观察，即不同服务器的利用率或不同流的带宽需求通常不会在完全相同的时间达到峰值。因此，如果考虑服务器和业务流之间的相关性，则可以在服务器和业务合并期间实现更多的功率节省。功率优化框架还包括具有流完成时间保证的多维DCN功率优化和用于大规模数据中心的高度可扩展的优化算法。