Population-based learning of load balancing policies for a distributed computer system
Population-based learning of load balancing policies for a distributed computer system
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分布式计算机系统负载均衡策略的基于群体的学习
DOI：
10.2514/6.1993-4664
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
B. Wah
中科院分区：
文献类型：
--
作者：
P. Mehra;B. Wah
有效的负载平衡策略使用动态资源信息来调度分布式计算机系统中的任务。在本文中，我们提出了一种自动学习此类策略的新方法。在我们系统中的每个站点，我们使用比较器神经网络来预测传入任务的相对加速，只使用任务到达之前获得的资源利用模式。这些比较器网络的输出在分布式系统上周期性地广播，并且每个站点处的资源调度器使用这些值来确定用于执行传入任务的最佳站点。在将工作负载信息和任务从一个站点传播到另一个站点时所引起的延迟，以及多程序多处理器中工作负载的动态和不可预测的性质，可能导致执行时的工作负载模式不同于负载指数计算和决策时的模式。我们的负载平衡策略通过使用某些可调参数来适应这种不确定性。我们提出了一个基于人口的机器学习算法，调整这些参数，以实现高平均加速相对于本地执行。我们的学习算法克服了缺乏特定于任务的信息，通过学习比较相对于相同的网站，而不是试图预测绝对加速不同的网站的相对加速比。我们目前的实验结果的基础上，评估的基准程序在网络上的Sun工作站连接的局域网。我们的结果表明，我们的负载平衡策略，当结合比较器神经网络的工作负载characterizatioa是有效的利用空闲资源在分布式计算机系统。
Effective load-balancing policies use dynamic resource information to schedule tasks in a distributed computer system. In this paper, we present a novel method for automatically learning such policies. At each site in our system, we use a comparator neural network to predict the relative speedup of an incoming task using only the resource-utilization patterns obtainedprior to the task's arrival. Outputs of these comparator networks are broadcast periodically over the distributed system, and the resource schedulers at each site use these values to determine the best site for executing an incoming task. The delays incurred in propagating workload information and tasks from one site to another, as well as the dynamic and unpredictable nature of workloads in multiprogrammed multiprocessors, may cause the workload pattern at the time of execution to differ from patterns prevailing at the times of load-index computation and decision making. Our loadbalancing policy accommodates this uncertainty by using certain tunable parameters. We present a population-based machine-learning algorithm that adjusts these parameters in order to achieve high average speedups with respect to local execution. Our learning algorithm overcomes the lack of taskspecific information by learning to compare the relative speedups of different sites with respect to the same site, rather than attempting to predict absolute speedups. We present experimental results based on the evecution of benchmark programs on a network of Sun workstations connected by a local area network. Our results show that our load-balancing policy, when combined with the comparator neural network for workload characterizatioa is effective in exploiting idle resources in a distributed computer system.