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。这一问题的紧迫性已经被政府部门和行业都暴露出来。虽然技术文献对考虑数据中心物理设计的策略进行了相当彻底的研究，但数据中心的操作特征（例如，执行的应用程序所需的性能，例如所支持的服务和应用程序的类型、它们的QoS要求、相关的后台服务器维护过程等）很少得到考虑。特别是，有必要调查在电源管理政策的背景下利用数据中心的操作特性所带来的影响，因为这些特性可能会导致计算机云的潜在显著节能和运营成本降低。智力优势：在拟建项目中，我们将研究应用程序的特点及其需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兴趣将是在分布式数据中心的背景下设计这样的策略，正如云计算范例所提供的那样。特别是，我们将从作业分析的角度，如故障和延迟容忍度，研究独立/单个数据中心的异构数据中心中的电源管理策略问题。我们将研究将数据中心的峰值负载分解为高延迟容忍度请求的可能性，这些请求需要迁移到不同的、不那么繁忙的数据中心，或者高容错性请求，在峰值时间需要较少的服务器资源被激活。同样，我们将研究分组作业的方法，以减少服务器上的需求可变性。这样的分解和分组将被动态执行。更广泛的影响：该结果有望显著改善数据中心电源管理的其他方法，并代表更接近实际系统的估计。教育方面的影响包括研究生和本科生的培训以及外展活动。

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.