分布式系统中基于非合作博弈的调度算法  

Scheduling Algorithm in Distributed Systems Based on Non-Cooperative Game

作　　者：童钊[1] 肖正[2] 李肯立[2] 刘宏[1] 李俊[1] 

机构地区：[1]湖南师范大学数学与计算机科学学院,湖南长沙410012 [2]湖南大学信息科学与工程学院,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2016年第10期139-147,共9页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(61370095;61502165);湖南师范大学大学生创新性实验项目(201501023)~~

摘　　要：针对分布式系统中任务调度问题,根据分布式环境下的任务调度特性,建立了一个非合作博弈的多角色任务调度框架,在此基础上提出了一种基于纳什均衡联合调度策略的分布式强化学习算法.相比于静态调度算法,该算法需要更少的系统知识.能使调度器主动学习任务到达和执行的相关先验知识,以适应相邻调度器的分配策略,目标是使得调度器的策略趋向纳什均衡.模拟实验结果表明:所提出的算法在任务的预期时间和公平性上相对于OLB(机会主义负载均衡)、MET(最小执行时间)、MCT(最小完成时间)等同类调度算法具有更好的调度性能.To address the task scheduling problem in distributed systems, based on an important feature of task scheduling in distributed computing environment, we have established a non-cooperative game framework for multi-layer multi-role, and put forward a distributed reinforcement learning algorithm of the joint scheduling strategy of Nash equilibrium. Compared with static scheduling algorithm, the proposed al- gorithm needs less system information. It enables the scheduler to actively learn task arrival, perform re- lated knowledge and adapt to the adjacent scheduler allocation policy. The target is to move the schedulers strategy toward Nash equilibrium. Simulation experiments show that the proposed algorithm achieves excellent performance in expected response time of tasks and fairness, compared with classical scheduling algorithms such as OLB, MET and MCT.

关 键 词：分布式计算 强化学习 任务调度 负载均衡 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]