EAGER: Job-Centered Power Management Policies for Data Centers

能耗是云计算数据中心面临的最重要的现实和及时的问题之一。这个问题的紧迫性已经被政府机构和行业暴露出来。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征(例如，所执行的应用的所需性能，诸如服务的类型和所支持的应用、它们的服务质量要求、相关联的后台服务器维护过程等)。很少被计算在内。特别是，有必要在电源管理政策的背景下调查利用数据中心的操作特征的影响，因为这些可能导致潜在的显著节能和计算机云的操作成本降低。智力优点：在拟议的项目中，我们将研究应用程序的特征及其要求对数据中心设计的影响？电源管理策略。我们研究的一个特别有趣的问题是在云计算范例提供的分布式数据中心环境中设计此类策略。特别是，我们将从任务配置文件的角度来研究独立/独立数据中心情况下的异构数据中心电源管理策略问题，如故障容错和延迟容错。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、不太繁忙的数据中心的高延迟容忍请求或在高峰时间需要激活较少服务器资源的高容错请求的可能性。同样，我们将研究作业分组的方法，以降低服务器上的需求变化性。这种分解和分组将动态执行。广泛的影响：预计结果将比其他数据中心电源管理方法显著改进，并代表更接近实际系统的估计。教育影响包括对研究生和本科生的培训以及外联活动。

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.