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A Particle Swarm Optimization for Workflow Scheduling based on Energy-Awareness in a Cloud Computing Environment

机译：基于云计算环境中的能量意识的工作流程调度的粒子群优化

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Cloud computing is viewed as a major logical step in the evolution of the internet as a source of remote computing services. With many companies now hosting infrastructure tools and services, more and more businesses are using cloud computing, which helps user applications dynamically provision as many compute resources at specified locations as and when required. These distributed applications come from domains such as high-energy physics, bioinformatics, image processing, etc., which is a complex duty. In order to efficiently schedule the workflow application onto these cloud computing environments, workflow scheduling algorithms are used which may be classified into two types: 1. Best-effort based scheduling: attempts to minimize the execution time ignoring other factors such as cost for access to resources and levels of satisfaction of users quality of service (QoS); and 2. QoS based scheduling: attempts to improve performance under QoS constraints, for example, minimizing the time under budget constraints or cost minimization under time constraints.
机译：云计算被视为互联网演变为远程计算服务的主要逻辑步骤。由于许多公司目前托管基础架构工具和服务，越来越多的企业正在使用云计算，这有助于用户应用程序在必要时动态配置指定位置的计算资源。这些分布式应用来自域，例如高能量物理，生物信息学，图像处理等，这是一种复杂的职责。为了有效地将工作流程应用程序安排到这些云计算环境中，使用工作流程调度算法，其可以分为两种类型：1。基于最佳的计划调度：尝试最小化执行时间忽略其他因素，例如可访问的成本资源和用户服务质量的满意度（QoS）; 2.基于QoS的调度：例如，在QoS限制下提高性能，例如，最小化预算约束下的时间或在时间约束下的成本最小化。

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