Waiting Game: Optimally Provisioning Fixed Resources for Cloud-Enabled Schedulers
Waiting Game: Optimally Provisioning Fixed Resources for Cloud-Enabled Schedulers
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DOI：
10.1109/sc41405.2020.00071
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Pradeep Ambati;Noman Bashir;D. Irwin;Prashant J. Shenoy
中科院分区：
文献类型：
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作者：
Pradeep Ambati;Noman Bashir;D. Irwin;Prashant J. Shenoy
虽然云平台使用户能够按需租用计算资源来执行他们的工作，但如果使用率很高，购买固定资源仍然比租用便宜得多。因此，优化云计算成本需要用户根据其工作负载确定购买多少固定资源，而不是租用多少固定资源。在本文中，我们引入了等待政策的概念，云使能的制造商，这是一个调度策略的对偶，并表明，最佳成本取决于它，我们定义了多个等待政策和开发简单的分析模型，以揭示其固定资源供应，成本和作业等待时间之间的权衡。我们评估了这些等待策略对一个运行在14. 3k核集群上的14M作业的一年之久的生产批量工作负载的影响，并表明与当前大小的固定集群相比，复合等待策略降低了成本（5%）和平均作业等待时间（7倍）。
While cloud platforms enable users to rent computing resources on demand to execute their jobs, buying fixed resources is still much cheaper than renting if their utilization is high. Thus, optimizing cloud costs requires users to determine how many fixed resources to buy versus rent based on their workload. In this paper, we introduce the concept of a waiting policy for cloud-enabled schedulers, which is the dual of a scheduling policy, and show that the optimal cost depends on it. We define multiple waiting policies and develop simple analytical models to reveal their tradeoff between fixed resource provisioning, cost, and job waiting time. We evaluate the impact of these waiting policies on a year-long production batch workload consisting of 14Mjobs run on a 14.3k-core cluster, and show that a compound waiting policy decreases the cost (by 5%) and mean job waiting time (by 7×) compared to a fixed cluster of the current size.