Energy-efficient adaptive networked datacenters for the QoS support of real-time applications
Energy-efficient adaptive networked datacenters for the QoS support of real-time applications
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DOI：
10.1007/s11227-014-1305-8
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发表时间：
2015-02-01
影响因子：
3.3
通讯作者：
Baccarelli, Enzo
中科院分区：
文献类型：
--
作者：
Cordeschi, Nicola;Shojafar, Mohammad;Baccarelli, Enzo
在本文中，我们开发了最佳的最小能量调度程序，用于在每个作业延迟严格约束下运行的虚拟化网络数据中心（VNetDC）中任务大小、计算速率、通信速率和通信功率的自适应联合分配。所考虑的VNetDC平台工作在底层协议栈的中间件层。它旨在通过采用软件即服务（SaaS）计算模型来支持实时流服务（例如新兴的大数据流计算（BDSC）服务）。我们的目标是最大限度地减少计算加通信的整体能耗。本文的主要新贡献如下：（i）通过实时考虑所提供工作负载的（可能不可预测的）时间波动和所考虑的 VNetDC 平台的重新配置成本，以自适应方式联合分配计算加通信资源； (ii) 对总体允许的计算加通信延迟实施严格的按作业延迟限制； (iii) 为了处理由此产生的资源优化问题的固有非凸性质，开发了一种新颖的解决方法，该方法可以将所提供的问题无损分解为两个更简单的子问题的级联。所提出的调度程序的能耗对允许的处理延迟的敏感度，以及所提供工作负载的峰均比（PMR）和相关系数（即平滑度）都在综合生成的和实际工作负载轨迹下进行了数值测试。最后，作为所达到的能源效率的指标，我们将所提出的调度器的能耗与一些基准静态、混合和顺序调度器的相应能耗进行比较，并以数值方式评估所得到的能量间隙百分比。
In this paper, we develop the optimal minimum-energy scheduler for the adaptive joint allocation of the task sizes, computing rates, communication rates and communication powers in virtualized networked data centers (VNetDCs) that operate under hard per-job delay-constraints. The considered VNetDC platform works at the Middleware layer of the underlying protocol stack. It aims at supporting real-time stream service (such as, for example, the emerging big data stream computing (BDSC) services) by adopting the software-as-a-service (SaaS) computing model. Our objective is the minimization of the overall computing-plus-communication energy consumption. The main new contributions of the paper are the following ones: (i) the computing-plus-communication resources are jointly allotted in an adaptive fashion by accounting in real-time for both the (possibly, unpredictable) time fluctuations of the offered workload and the reconfiguration costs of the considered VNetDC platform; (ii) hard per-job delay-constraints on the overall allowed computing-plus-communication latencies are enforced; and, (iii) to deal with the inherently nonconvex nature of the resulting resource optimization problem, a novel solving approach is developed, that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The sensitivity of the energy consumption of the proposed scheduler on the allowed processing latency, as well as the peak-to-mean ratio (PMR) and the correlation coefficient (i.e., the smoothness) of the offered workload is numerically tested under both synthetically generated and real-world workload traces. Finally, as an index of the attained energy efficiency, we compare the energy consumption of the proposed scheduler with the corresponding ones of some benchmark static, hybrid and sequential schedulers and numerically evaluate the resulting percent energy gaps.