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Fine-grained resource provisioning and task scheduling for heterogeneous applications in distributed green clouds
IEEE/CAA Journal of Automatica Sinica ( IF 11.8 ) Pub Date : 2020-06-02 , DOI: 10.1109/jas.2020.1003177
Haitao Yuan , MengChu Zhou , Qing Liu , Abdullah Abusorrah

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud ( DGC ) systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G / G / 1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm ( SBA ) to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

中文翻译:

分布式绿色云中异构应用程序的细粒度资源供应和任务调度

近年来,越来越多的企业采用云计算来管理其在分布式绿色云(DGC)系统中的重要业务应用程序,以缩短响应时间并提高成本效益。DGC中的任务调度和资源分配在学术界和工业界都受到了越来越多的关注,因为它们的能耗很高,因此管理成本很高。DGC中的许多因素,例如电网价格和绿色能源的数量,都表现出强烈的空间差异。在上述因素都具有空间变化的市场中,到达任务的急剧增加带来了最大的挑战,即如何最大程度地降低DGC提供者的能源成本。这项工作采用G / G / 1排队系统来分析DGC中服务器的性能。基于此,通过提出的基于模拟退火的蜜蜂算法(SBA)提出并解决了一个单目标约束优化问题,以发现SBA可以通过在多个DGC之间最佳地分配异构应用程序的任务并指定每个服务器的运行速度和每个GC中已打开电源的服务器的数量,同时严格满足所有应用程序任务的响应时间限制。基于实际数据的实验结果证明,SBA的能源成本低于几种基准调度方法。在严格满足所有应用程序任务的响应时间限制的同时,指定每个服务器的运行速度和每个GC中已打开电源的服务器的数量。基于实际数据的实验结果证明,SBA的能源成本低于几种基准调度方法。在严格满足所有应用程序任务的响应时间限制的同时,指定每个服务器的运行速度和每个GC中已打开电源的服务器的数量。基于实际数据的实验结果证明,与几种基准调度方法相比,SBA的能源成本更低。
更新日期:2020-08-04
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