• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of computational and theoretical nanoscience >Enhanced Particle Swarm Optimization for Scientific Workflow Scheduling in Cloud Environments
【24h】

Enhanced Particle Swarm Optimization for Scientific Workflow Scheduling in Cloud Environments

机译：云环境中的科学工作流程调度增强粒子群优化

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Cloud Computing provides wide computation and resource facilities for execution of various application workflows. There are different resources involved in execution of single workflow. Cloud computing also offers highly dynamic environment in which the system load and status of resources changes frequently. However, users are charged on a pay-per-use basis. User applications may acquire large data retrieval and execution costs when they are scheduled taking into account only the 'execution time.' In addition, optimizing execution time, the cost from data transfers between resources as well as execution costs must also be taken into account. In this paper, Enhanced Particle Swarm Optimization (EPSO) is designed for mapping tasks to suitable resources that take into account both computation cost and data transmission cost. Such a way total cost for all compute resources is minimized.
机译：云计算提供了广泛的计算和资源设施，用于执行各种应用程序工作流程。 执行单个工作流程中有不同的资源。 云计算还提供高度动态的环境，其中系统负载和资源状态频繁变化。 但是，用户每次使用付费。 用户应用程序可以在计划仅考虑“执行时间”时获取大数据检索和执行成本。 此外，还必须考虑优化执行时间，从资源之间的数据传输以及执行成本之间的成本。 在本文中，增强的粒子群优化（EPSO）被设计用于将任务映射到考虑到计算成本和数据传输成本的合适资源。 这样的方式，所有计算资源的总成本都是最小化的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号