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首页> 外文期刊>IEEE transactions on industrial informatics >Multiqueue Scheduling of Heterogeneous Tasks With Bounded Response Time in Hybrid Green IaaS Clouds
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Multiqueue Scheduling of Heterogeneous Tasks With Bounded Response Time in Hybrid Green IaaS Clouds

机译：混合绿色IaaS云中具有有限响应时间的异构任务的多队列调度

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摘要

Cost-effective task scheduling is an important operation in green infrastructure-as-a-service clouds (GICs) as the energy consumed by users' tasks is drastic. The irregular task arrival forces private GIC to adopt hybrid clouds to outsource some tasks to dynamic and reliable virtual machines (VMs) of public external clouds. However, temporal differences in revenue, electricity prices, wind and solar energy, and VM running prices of public external clouds make it difficult to dispatch all tasks in a cost-effective way while satisfying users' specified response time constraints. Unlike existing methods, we propose a multiqueue scheduling (MQS) method that investigates such temporal differences in hybrid GICs (HGICs). Specially, this work first gives mathematical relations between rejected tasks and service rates of servers in private GIC. In each iteration of MQS, this paper formulates a profit maximization problem for HGIC and solves it by a novel meta-heuristic optimization method by combing simulated annealing, particle swarm optimization, and genetic algorithm. Trace-driven experiments based on real-life data demonstrate that profit and throughput of MQS are larger than typical task scheduling algorithms while meeting tasks' response time constraints.
机译：具有成本效益的任务调度是绿色基础架构即服务云（GIC）中的一项重要操作，因为用户任务消耗的能源非常大。不定期的任务到达迫使私有GIC采用混合云将某些任务外包给公共外部云的动态且可靠的虚拟机（VM）。但是，由于收入，电价，风能和太阳能以及公共外部云的VM运行价格的时间差异，使得在满足用户指定的响应时间限制的同时，难以以经济高效的方式调度所有任务。与现有方法不同，我们提出了一种多队列调度（MQS）方法，该方法研究混合GIC（HGIC）中的此类时间差异。特别是，这项工作首先给出了私有GIC中拒绝任务和服务器服务速率之间的数学关系。在MQS的每次迭代中，本文都提出了HGIC的利润最大化问题，并通过结合模拟退火，粒子群优化和遗传算法的新颖的元启发式优化方法进行求解。基于现实生活数据的跟踪驱动实验表明，在满足任务响应时间约束的同时，MQS的利润和吞吐量比典型的任务调度算法要大。

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