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Evolutionary Multi-Objective Workflow Scheduling in Cloud

机译：云中的进化多目标工作流调度

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摘要

Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an evolutionary multi-objective optimization (EMO)-based algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for problem-specific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.
机译：云计算为执行具有大量计算资源的大型应用程序提供了有希望的平台，可按需提供。在云模型中，将根据用户的资源使用情况和所需的服务质量（QoS）规范向用户收费。尽管在传统的分布式或异构计算环境中存在许多现有的工作流调度算法，但是由于Cloud通过基于服务的资源管理方法和按使用量定价策略与传统的异构环境不同，因此它们难以直接应用于Cloud环境。 。在本文中，我们着重指出了这些困难，并针对云环境中的可优化制造时间和成本的工作流调度问题建模，作为多目标优化问题（MOP）。我们提出了一种基于进化多目标优化（EMO）的算法，以解决基础架构即服务（IaaS）平台上的工作流调度问题。该算法提出了针对问题的编码和种群初始化，适应度评估和遗传算子的新方案。在现实世界中的工作流和随机生成的工作流上进行的大量实验表明，使用基于实例的IaaS计算和定价模型，由我们的进化算法生成的时间表在大多数工作流上都具有更高的稳定性。结果还表明，在大多数情况下，与现有的最新QoS优化调度算法相比，我们的算法可以获得更好的解决方案。进行的实验基于Amazon EC2的按需实例类型。但是，该算法易于扩展到其他IaaS服务的资源和定价模型。

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