Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers
Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers
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DOI：
10.1016/j.comnet.2009.06.008
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
T. Mukherjee;Ayan Banerjee;Georgios Varsamopoulos;S. Gupta;S. Rungta
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文献类型：
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作者：
T. Mukherjee;Ayan Banerjee;Georgios Varsamopoulos;S. Gupta;S. Rungta
数据中心中的作业调度可以从网络物理的角度来考虑，因为它影响数据中心的计算性能（即网络方面）和能源效率（物理方面）。随着对绿色数据中心需求的不断增长，本文利用数据中心虚拟化技术的最新进展，提出了基于网络物理、时空（即启动时间和服务器分配）、热感知的作业调度算法，该算法在性能约束（即截止期限）下最大限度地降低数据中心的能耗。通过能够暂时“分散”工作量，将其分配给节能计算设备，并进一步减少热量再循环，从而减少冷却系统上的负载，可以实现节约。本文提出了三类热感知节能调度技术：（a）FCFS-Backfill-XInt和FCFS-Backfill-LRH，对热感知作业安排中流行的先到先服务进行了改进，采用了回填（B）EDF-LRH，具有热感知布局的在线最早期限优先调度算法;以及（c）一种最小化热交叉干扰的离线遗传调度算法（SCINT），它适用于积压件的批量调度。基于ASU富尔顿HPC数据中心的真实的作业日志的模拟结果显示，与采用首次匹配放置的FCFS回填相比，FCFS回填的热感知增强可实现高达25%的节省，具体取决于传入工作负载的强度，而SCINT可实现高达60%的节省。在低负载下，EDF-LRH的性能接近离线SCINT，在高负载下，其性能下降到FCFS-回填的性能。然而，EDF-LRH需要毫秒级的操作，这比SCINT快得多，后者需要长达数小时的运行时间，具体取决于提交作业的数量和大小。类似地，FCFS-Backfill-LRH比FCFS-Backfill-XInt快得多，但它只实现了FCFS-Backfill-XInt的部分节省。
Job scheduling in data centers can be considered from a cyber–physical point of view, as it affects the data center’s computing performance (i.e. the cyber aspect) and energy efficiency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent technological advances in data center virtualization and proposes cyber–physical, spatio-temporal (i.e. start time and servers assigned), thermal-aware job scheduling algorithms that minimize the energy consumption of the data center under performance constraints (i.e. deadlines). Savings are possible by being able to temporally “spread” the workload, assign it to energy-efficient computing equipment, and further reduce the heat recirculation and therefore the load on the cooling systems. This paper provides three categories of thermal-aware energy-saving scheduling techniques: (a) FCFS-Backfill-XInt and FCFS-Backfill-LRH, thermal-aware job placement enhancements to the popular first-come first-serve with back-filling (FCFS-backfill) scheduling policy; (b) EDF-LRH, an online earliest deadline first scheduling algorithm with thermal-aware placement; and (c) an offline genetic algorithm for SCheduling to minimize thermal cross-INTerference (SCINT), which is suited for batch scheduling of backlogs. Simulation results, based on real job logs from the ASU Fulton HPC data center, show that the thermal-aware enhancements to FCFS-backfill achieve up to 25% savings compared to FCFS-backfill with first-fit placement, depending on the intensity of the incoming workload, while SCINT achieves up to 60% savings. The performance of EDF-LRH nears that of the offline SCINT for low loads, and it degrades to the performance of FCFS-backfill for high loads. However, EDF-LRH requires milliseconds of operation, which is significantly faster than SCINT, the latter requiring up to hours of runtime depending upon the number and size of submitted jobs. Similarly, FCFS-Backfill-LRH is much faster than FCFS-Backfill-XInt, but it achieves only part of FCFS-Backfill-XInt’s savings.