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A multi-objective workflow scheduling algorithm for cloud environment

机译：云环境下的多目标工作流调度算法

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The workflow scheduling problems involve the task-resource mapping satisfying some functional and nonfunctionalquality of service. Workflow applications require high computational power and often involve a large amount of data transfer from one place to another. Furthermore, due to dependencies existed among tasks; schedules must be brought forth according to given precedence constraints. Cloud computing is a new business-oriented platform service that facilitates an infinite number of services by providing heterogeneous, virtualized resources to users based on a pay-as-you-go model with the distinctive quality of service (QoS) Constraints. Due to its market-oriented approach, conventional workflow scheduling strategies are facing new challengeslike on-demand payment,unprecendented openness and autonomy. This paperpresents anadaptive privileged multi-objective workflow scheduling algorithm (APMWSA) which optimally run the workflow execution process for minimization of total cost and makespan. This algorithm uses the concept of novel adaptive elite-based particle swarm optimization (NAEB-PSO) for taskresource mapping.A comparative study of presented algorithm is also made with some existing algorithms.
机译：工作流调度问题涉及满足某些功能性和非功能性服务质量的任务-资源映射。工作流应用程序需要很高的计算能力，并且通常涉及从一个地方到另一个地方的大量数据传输。此外，由于任务之间存在依赖关系；必须根据给定的优先级约束制定时间表。云计算是一种新的面向业务的平台服务，它通过基于即付即用模型并具有独特的服务质量（QoS）约束，为用户提供异构的虚拟化资源，从而促进了无限数量的服务。由于其面向市场的方法，传统的工作流调度策略面临着新的挑战，例如按需付款，前所未有的开放性和自治性。本文提出了一种自适应特权多目标工作流调度算法（APMWSA），该算法可以优化运行工作流执行过程，以最大程度地降低总成本和制造期。该算法采用新颖的基于精英的自适应粒子群优化（NAEB-PSO）的概念进行任务资源映射。还对现有算法进行了比较研究。

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