Minimizing Cost in IaaS Clouds Via Scheduled Instance Reservation
Minimizing Cost in IaaS Clouds Via Scheduled Instance Reservation
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DOI：
10.1109/icdcs.2017.16
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发表时间：
2017-06
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影响因子：
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通讯作者：
Qiushi Wang;Ming Ming Tan-Ming;Xueyan Tang;Wentong Cai
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文献类型：
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作者：
Qiushi Wang;Ming Ming Tan-Ming;Xueyan Tang;Wentong Cai
在基于云的在线应用程序的工作负载中经常可以看到有规律的昼夜模式。这种非静态工作负载会随着时间的推移而改变处理需求。为了以最小的成本运行应用服务，可以根据工作负载的变化动态调整云实例的数量。最近，一种新型的调度实例出现在网络结构即服务市场中，以促进此类配置。定期实例可以根据定期计划进行预订，并提供价格折扣。同时，云计算供应商要求最小的计划持续时间，以避免频繁启动和终止云实例的开销。再加上传统的按需和预留实例，用户要找到这三种定价选项的最佳组合以最大限度地降低其货币成本变得更加复杂。对于新调度的实例，不仅需要确定实例的数量，还需要确定实例的开始和停止时间。在本文中，我们开发了一种快速有效的策略来解决这个问题。基于每小时的工作负载分布，我们首先计算每个定价选项要获取的最佳实例数量。然后，我们设计了一个调度算法来安排调度的实例，遵守其调度持续时间的限制。使用LOL在线游戏和维基百科移动的服务的工作负载作为两个案例研究，我们的策略的有效性进行了证明。
Regular diurnal patterns are often seen in the workloads of cloud-based online applications. This kind of non-stationary workloads changes the processing demands over time. To run application services with minimum costs, the number of cloud instances can be dynamically adjusted according to the workload variations. Recently, a new type of scheduled instances has emerged in the Infrastructure-as-a-Service market to facilitate such configurations. Scheduled instances can be reserved based on a recurring schedule and they offer price discounts. Meanwhile, cloud vendors require minimum scheduled durations to avoid the overhead of frequently launching and terminating cloud instances. Coupled with traditional on-demand and reserved instances, it becomes more complicated for users to find the optimal combination of these three pricing options to minimize their monetary costs. For the new scheduled instances, not only the number of instances but also their start and stop times have to be decided. In this paper, we develop a fast and effective strategy to solve this problem. Based on the hourly workload distributions, we first compute the optimal number of instances to acquire for each pricing option. Then, we design a scheduling algorithm to arrange the scheduled instances in compliance with the restriction of their scheduled durations. Using the workloads of the LOL online game and the Wikipedia Mobile service as two case studies, the efficacy of our strategy is demonstrated.