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Green MapReduce for heterogeneous data centers

机译：适用于异构数据中心的Green MapReduce

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MapReduce has emerged as one of the key workloads in today's data centers, which constantly strive for an optimal tradeoff between energy consumption and performance. MapReduce alternates between computation and communication intensive phases with bursty workloads. The challenge to make execution of MapReduce green, lies in controlling server and network resources simultaneously. The related work offers various good solutions for homogenous systems, with the central theme of packing tasks into as small number of servers as possible and thus overlooking the possibility to "sleep" servers and network components. This paper considers a very bursty MapReduce workload with distinct CPU, memory and network requirements executed on heterogenous data centers, where servers have various CPU/memory capacities and execute request in a process-sharing manner. To reduce energy consumption while maintaining a low task response time, we propose an online energy minimization path algorithm, termed GEMS, to schedule MapReduce tasks, in cooperation with sleeping policies on servers as well as the switches. Using Google MapReduce traces, our simulation experiments show that our proposed solution gains a significant energy saving of 35% and meanwhile improves task response times by 35% on heterogenous data centers, compared to policies which are network agnostic or adopt no sleeping schedule. Overall, we achieve greener and faster MapReduce with (surprisingly) only a slightly higher number of servers, by considering energy consumption rather than conventional approach of considering power values only.
机译：MapReduce已成为当今数据中心中的关键工作负载之一，这些数据中心不断努力在能耗和性能之间寻求最佳平衡。 MapReduce在具有大量工作负载的计算和通信密集型阶段之间切换。使MapReduce的执行绿色化的挑战在于同时控制服务器和网络资源。相关工作为同构系统提供了各种好的解决方案，其中心主题是将任务打包到尽可能少的服务器中，从而忽略了“休眠”服务器和网络组件的可能性。本文考虑了一个非常突发的MapReduce工作负载，在异构数据中心上执行了不同的CPU，内存和网络要求，其中服务器具有各种CPU /内存容量并以进程共享的方式执行请求。为了降低能耗同时保持较低的任务响应时间，我们提出了一种在线能源最小化路径算法（称为GEMS），与服务器和交换机上的睡眠策略配合使用，以调度MapReduce任务。通过使用Google MapReduce跟踪，我们的仿真实验表明，与网络不可知或不采用休眠计划的策略相比，我们提出的解决方案可在异构数据中心上节省35％的能源，同时将任务响应时间缩短35％。总体而言，我们仅通过考虑能源消耗，而不是仅考虑功耗值的传统方法，（仅使用数量略多的服务器）即可实现绿色，更快的MapReduce。

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