针对已有的任务卸载及调度方案大多计算过程复杂、易忽略任务多属性及子任务间可并行性等问题,设计了一种云-边-端协作网络中的细粒度任务调度策略。构建了一个涵盖云-边-端三层的网络架构并引入SDN(software defined network)来实现高...针对已有的任务卸载及调度方案大多计算过程复杂、易忽略任务多属性及子任务间可并行性等问题,设计了一种云-边-端协作网络中的细粒度任务调度策略。构建了一个涵盖云-边-端三层的网络架构并引入SDN(software defined network)来实现高效的网络信息管理和边缘负载均衡。提出了包含两种任务服务模式(整体服务模式、分割服务模式)及三种执行机制(云执行、边执行、端执行)的任务调度策略:对于整体服务模式下的任务,设计了一个时延与能耗联合优化的优化问题来获取对应任务的最佳执行机制;对于分割服务模式下的任务,利用改进的动态列表调度方法设计了一种子任务调度并行度最大化(subtask scheduling parallelism maximization,SSPM)算法来最大化分割后被调度子任务的并行度。在NS-3平台上进行仿真实验,仿真结果表明该策略的性能表现优于其他对比方案。展开更多
This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Pr...This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task allocation.Utilizing Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task completion.It advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue lengths.The Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative analysis.The research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project(No.PNURSP2023R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task allocation.Utilizing Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task completion.It advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue lengths.The Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative analysis.The research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.
