CSR:Small:A Server-Network Cooperative Approach to Data Center Energy Optimization

云计算服务于从银行到国防的关键应用需求，随着云计算的增长，数据中心已经成为我们国家的重要组成部分。的基础设施。在数据中心内，服务器和互连网络占总能源的比例越来越大。这是因为许多服务器场被配置为以比必要的容量高得多的容量运行，并且服务器之间的数据移动量正在迅速增加。该项目研究服务器和网络之间的相互作用，并通过监控数据中心的工作负载模式来设计节能策略。随着数据中心应用及其通信模式的不断变化，该研究项目通过采用服务器和网络消耗的能源的整体视图提供了一个急需的研究。该研究成果将通过最大限度地减少数据中心能源为美国工业提供直接利益。该项目研究协调的服务器网络能源优化策略，明智地利用处理器和网络组件（微）架构中可用的低功耗状态，同时保持应用程序性能约束。该解决方案旨在设计特定于工作负载的能量管理策略，该策略选择最适合工作负载的低功率状态和动态电压/频率设置的最佳集合。为了提高功率效率，将利用异构组件之间的功率转移，使得可以在指定的功率预算内实现更高的性能。这项研究的成果将包括自适应算法，将调整自己不断变化的工作负载模式，和技术，最大限度地减少时间的解决方案。

Fueled by the growth in cloud computing which serves critical application needs ranging from banking to national defense, data centers have become a critical part of our nation?s infrastructure . Within data centers, servers and the interconnect networks account for an increasingly significant portion of the overall energy. This is because many server farms are configured to operate at a capacity much higher than necessary, and the data movement between servers is rapidly increasing in volume. This project studies the interplay between the servers and networks, and devises energy-saving strategies through monitoring data center workload patterns. With the changing landscape of data center applications and their communication patterns, this research project offers a much-needed study through adopting a holistic view of energy consumed by both servers and networks. The research outcomes will provide direct benefit to US industry through minimizing data center energy.This project investigates coordinated server-network energy optimization strategies that make judicious use of low-power states available in the processor and network component (micro) architectures while preserving application performance constraints. The solution approach seeks to design workload-specific energy management policies that select an optimal set of low power states and dynamic voltage/frequency settings that are best suited for the workload. To improve power efficiency, power shifting between heterogeneous components will be leveraged such that higher performance can be achieved within a specified power budget. The outcome of this research will include self-adaptive algorithms that will adjust themselves to the changing workload patterns, and techniques that minimize time-to-solution.