一种面向不可分任务需求和部署约束的动态多维资源公平分配机制
A fair multi-resource allocation mechanism for time-varying discrete jobs with placement constraints
李杰 1汪建洲2
作者信息
- 1. 昆明理工大学信息工程与自动化学院,云南 昆明 650504;昆明理工大学云南省计算机技术应用重点实验室,云南 昆明 650504
- 2. 昆明理工大学信息工程与自动化学院,云南 昆明 650504
- 折叠
摘要
如何公平、高效地将多维资源分配给需求变化的用户是云计算资源共享的关键问题.该场景下的动态资源分配通常面临着用户任务最小粒度资源需求难以再被分割、任务需求与服务器配置不匹配等问题.现有资源公平分配机制多基于用户任务需求无限可分或任务执行与服务器配置均匹配的理想前提,难以保证分配可行.通过深入分析时变不可分任务需求和任务部署约束的特点,设计了一种基于累计任务份额公平的时变任务份额公平分配机制,以保证资源分配的公平性和效率.理论结果表明,该机制满足激励共享、相差一个任务资源的无嫉妒和帕累托最优属性.基于阿里云数据集的实验结果表明,与现有的公平分配机制相比,该方法有效地减少了用户的等待、作业排队和作业完成时间.
Abstract
A key issue in resource sharing in cloud computing is how to fairly and efficiently allocate the multi-resources to users with dynamic demand.Multi-resource fair allocation in a cloud computing system usually faces problems,such as subdividing the minimum granularity of users'resource requirements,and the mismatch between task requirements and server configurations.Most of the existing mechanisms for multi-resource fair allocation are based on the ideal assump-tion that the task demands of user are infinitely divisible or that the task execution and server configuration are matched,which makes it difficult to guarantee that the allocation is feasible.By analyzing the characteristics of time-varying indi-visible task demands and task placement constraints,a time-varying task share fairness allocation mechanism based on cu-mulative task share fairness was designed to ensure the fairness and efficiency of resource allocation.Theoretical analysis shows that the TV-TSF mechanism satisfies the sharing incentive,envy-freeness up to one item,and Pareto optimal prop-erties.Simulation results based on the Alibaba cluster dataset show that,compared with the existing fair allocation mecha-nisms,the TV-TSF mechanism proposed can effectively reduce the waiting time,job queuing time,and job completion time of users.
关键词
动态多维资源分配/不可分任务需求/任务部署约束/累计任务份额公平Key words
dynamic multi-resource allocation/indivisible task demand/task placement constraint/cumulative task share fairness引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据