Distributed Coordination of Internet Data Centers Under Multiregional Electricity Markets
Distributed Coordination of Internet Data Centers Under Multiregional Electricity Markets
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DOI：
10.1109/jproc.2011.2161236
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发表时间：
2012
影响因子：
20.6
通讯作者：
Lei Rao;Xue Liu;M. Ilić;Jie Liu
中科院分区：
文献类型：
--
作者：
Lei Rao;Xue Liu;M. Ilić;Jie Liu
本文研究了互联网服务提供商的电力成本管理问题。近年来，随着互联网服务和云计算需求的不断增长，与IDC运营相关的电力使用量大幅上升。IDC的网络和物理方面相互影响，并在电源管理方面带来前所未有的挑战。虽然大多数现有的研究集中在减少在一个特定的位置的IDC的功耗，降低总的电力成本的问题已经被忽视。这是一个重要的问题所面临的服务提供商，特别是在目前的多电力市场环境下，电力价格可能会表现出时间和空间的差异。此外，对于这些服务提供商，保证服务质量（QoS;即，服务水平目标），例如对终端用户的服务延迟保证是至关重要的。研究了多区域电力市场下面向QoS约束的总电费最小化问题以及电价的位置差异和时间差异问题。我们共同考虑IDC的网络和物理管理能力，并在一个统一的方案中利用中心级的负载平衡和服务器级的功率控制。基于广义Benders分解（GBD）技术，我们将问题建模为一个带约束的混合整数规划。基于多个IDC位置的真实电价数据的广泛评估证明了我们的计划的有效性。
This paper addresses the problem of electricity cost management for Internet service providers with a collection of spatially distributed data centers. As the demand on Internet services and cloud computing has kept increasing in recent years, the power usage associated with IDC operations has been uprising significantly. The cyber and physical aspects of IDCs interact with each other, and bring unprecedented challenges in power management. While most existing research focuses on reducing power consumptions of IDCs at one specific location, the problem of reducing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the present multielectricity-market environment, where the price of electricity may exhibit temporal and spatial diversities. Further, for these service providers, guaranteeing the quality of service (QoS; i.e., service level objectives) such as service delay guarantees to the end users is of critical importance. This paper studies the problem of minimizing the total electricity cost geared to QoS constraint as well as the location diversity and time diversity of electricity price under multiregional electricity markets. We jointly consider both the cyber and physical management capabilities of IDCs, and exploit both the center-level load balancing and the server-level power control in a unified scheme. We model the problem as a constrained mixed integer programming based on generalized benders decomposition (GBD) technique. Extensive evaluations based on real-life electricity price data for multiple IDC locations demonstrate the effectiveness of our scheme.