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Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

机译:基于合作游戏方法的自适应数据中心管理算法

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Recently, data centers (DCs) have become an indispensable part of modern computing infrastructures. However, DCs often consume a significant amount of energy and lead to the workload unbalance with increasing service requests. Keeping focus on this point, in this paper, we propose a novel energy-aware DC management scheme. To design an efficient DC control algorithm, the main challenge is uncertainties such as uncertain energy price and unpredictable users’ demands. In response to these uncertainties, we adopt the idea of cooperative game theory, and introduce a new two-phase bargaining model to get the mutual advantage. To decide the energy price, we formulate the Stackelberg bargaining game while adapting the current system situation. To balance the workloads among DCs, the migration bargaining game is developed. These two game models are tightly coupled to achieve greater and reciprocal advantages during dynamic DC operations. The main novelty of our proposed two-phase bargaining approach is to handle comprehensively contradictory requirements for the DC management. Finally, extensive experiment results validate the efficiency of our proposed algorithm by comparing with the existing state-of-the-art DC management protocols in terms of average payoff of all DCs, system throughput and fairness among DCs.
机译:最近,数据中心(DCS)已成为现代计算基础架构不可或缺的一部分。然而,DCS经常消耗大量能量,并导致工作量不平衡随着越来越多的服务请求。在这一点上,专注于本文,我们提出了一种新颖的能量感知直流管理方案。为了设计高效的DC控制算法,主要挑战是不确定的能源价格和不可预测的用户需求等不确定性。为了应对这些不确定性,我们采用合作博弈论的思想,并引入了一种新的两相讨价还价模型来获得相互优势。要决定能源价格,我们制定了Crackelberg谈判游戏,同时调整了当前的系统情况。为了平衡DCS之间的工作量,开发了迁移讨价还价游戏。这两个游戏模型紧密耦合,以在动态直流操作期间实现更大和互惠的优点。我们提出的两阶段谈判方法的主要新颖性是为DC管理处理全面矛盾的要求。最后,广泛的实验结果通过在所有DC的平均支付,系统吞吐量和DCS之间的公平性方面的平均收益方面,验证我们所提出的算法的效率。

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