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DiBA: Distributed Power Budget Allocation for Large-Scale Computing Clusters

机译:DIBA:大型计算群集的分布式电力预算分配

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Power management has become a central issue inlarge-scale computing clusters where a considerable amount ofenergy is consumed and a large operational cost is incurredannually. Traditional power management techniques have a centralizeddesign that creates challenges for scalability of computingclusters. In this work, we develop a framework for distributedpower budget allocation that maximizes the utility of computingnodes subject to a total power budget constraint. To eliminate the role of central coordinator in the primaldualtechnique, we propose a distributed power budget allocationalgorithm (DiBA) which maximizes the combined performanceof a cluster subject to a power budget constraint in a distributedfashion. Specifically, DiBA is a consensus-based algorithm inwhich each server determines its optimal power consumptionlocally by communicating its state with neighbors (connectednodes) in a cluster. We characterize a synchronous primal-dualtechnique to obtain a benchmark for comparison with thedistributed algorithm that we propose. We demonstrate numericallythat DiBA is a scalable algorithm that outperforms theconventional primal-dual method on large scale clusters in termsof convergence time. Further, DiBA eliminates the communicationbottleneck in the primal-dual method. We thoroughly evaluatethe characteristics of DiBA through simulations of large-scaleclusters. Furthermore, we provide results from a proof-of-conceptimplementation on a real experimental cluster.
机译:电源管理已成为核心问题的核心问题,其中尺寸计算群集群,其中广泛的每种食用量,均有大量的运营成本。传统的电源管理技术具有集中式指导,可为计算规范器的可扩展性创造挑战。在这项工作中,我们开发了一个分布式预算分配的框架,最大化计算节点的效用,这对总功率预算约束进行了影响。为了消除中央协调员在PrimaldualTechnique中的作用,我们提出了一种分布式电力预算互动算法(DIBA),其最大化了群集的组合性能,这在分布式中的电源预算约束。具体地,DIBA是一种基于共识的算法,每个服务器通过在群集中与邻居(连接Nodes)通信其状态来确定其最佳功耗。我们的特征是一个同步原始 - DualTechnique,以获得与我们提出的分段算法进行比较的基准。我们证明了Diba是一种可扩展的算法,可以在收敛时间方面优于大规模集群上的强调原始方法。此外,DIBA消除了Primal-Dual方法中的CommunicationBottleNeck。我们通过大型标签模拟彻底评估DIBA的特征。此外,我们提供了在真实实验群体上的概念验证验证的结果。

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