EAGER: Job-Centered Power Management Policies for Data Centers

能源消耗是与云计算数据中心相关的最重要的实际和及时的问题之一。这个问题的紧迫性已经被政府机构和行业所暴露。虽然在技术文献中已经相当彻底地研究了考虑数据中心的物理设计的策略，但是数据中心的操作特性（例如，所执行的应用程序的所需性能，例如所支持的服务和应用程序的类型、它们的QoS要求、相关联的后台服务器维护过程等）。很少有人知道特别是，有必要调查的影响，利用数据中心的运行特性的背景下，电源管理政策，因为这些可能会导致潜在的显着节能和降低运营成本的计算机cloud.Intellectual优点：在拟议的项目中，我们将研究的应用程序的特点和他们的要求对数据中心的设计效果？电源管理策略。我们的研究的一个特别的兴趣将是在分布式数据中心的背景下，云计算范式提供的这些政策的设计。特别是，我们将调查的异构数据中心的情况下，独立/个人的数据中心从作业分析，如故障和延迟容忍的电源管理策略的问题。我们将研究将数据中心的峰值负载分解为需要迁移到不同的、不那么忙碌的数据中心的高延迟容限请求或需要在峰值时间激活较少服务器资源的高故障容限请求的可能性。同样，我们将研究分组作业的方法，以减少服务器上的需求变化。这种分解和分组将动态执行。更广泛的影响：预计结果将显着改善数据中心的电源管理的其他方法，以及代表更接近实际系统的估计。教育影响包括培训研究生和本科生以及外联活动。

Energy consumption is one of the most important practical and timely problem associated with data centers for cloud computing. The urgency of this problem has been exposed by both, the governmental agencies and the industry. While policies that consider the physical design of the data centers have been studied in the technical literature rather thoroughly, the operational characteristics of the data centers (e.g., the required performance of the executed applications, such as the type of services and the applications being supported, their QoS requirements, associated background server maintenance processes, etc.) have rarely been accounted for. In particular, there is a need to investigate the implications of exploiting the operational characteristics of the data centers in the context of power management policies, as those could lead to potentially significant energy savings and operational cost reduction of a computer cloud.Intellectual Merit: In the proposed project, we will study the effect of the characteristics of the applications and their requirements on the design of the data centers? power management policies. A particular interest of our study will be the design of such policies in the context of distributed data centers, as provided by the cloud-computing paradigm. In particular, we will investigate the problem of Power Management Policies in heterogeneous data centers for the case of independent/individual data centers from the perspective of job profiling, such as failure and latency tolerance. We will study the possibility of decomposition of the peak loads to a data center into high latency tolerant requests that needs to be migrated to different, less busy data centers, or high failure tolerant requests that require less server resources being activated at peak times. Similarly, we will study the approach of grouping jobs, as to reduce the demand variability on servers. Such decomposition and grouping will be dynamically performed.Broader Impacts: The results are expected to significantly improve over the other approaches of power management of data centers, as well as represent estimations which closer match practical systems. The educational impact includes training of graduate and undergraduate students and outreach activities.