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首页> 外文期刊>IEEE Transactions on Computers >Load balancing problems for multiclass jobs in distributed/parallel computer systems
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Load balancing problems for multiclass jobs in distributed/parallel computer systems

机译:分布式/并行计算机系统中多类作业的负载平衡问题

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摘要

Load balancing problems for multiclass jobs in distributed/parallel computer systems with general network configurations are considered. We construct a general model of such a distributed/parallel computer system. The system consists of heterogeneous host computers/processors (nodes) which are interconnected by a generally configured communication/interconnection network wherein there are several classes of jobs, each of which has its distinct delay function at each host and each communication link. This model is used to formulate the multiclass job load balancing problem as a nonlinear optimization problem in which the goal is to minimize the mean response time of a job. A number of simple and intuitive theoretical results on the solution of the optimization problem are derived. On the basis of these results, we propose an effective load balancing algorithm for balancing the load over an entire distributed/parallel system. The proposed algorithm has two attractive features. One is that the algorithm can be implemented in a decentralized fashion. Another feature is simple and straightforward structure. Models of nodes, communication networks, and a numerical example are illustrated. The proposed algorithm is compared with a well-known standard steepest-descent algorithm, the FD algorithm. By using numerical experiments, we show that the proposed algorithm has much faster convergence in terms of computational time than the FD algorithm.
机译:考虑具有常规网络配置的分布式/并行计算机系统中多类作业的负载平衡问题。我们构建了这种分布式/并行计算机系统的通用模型。该系统由通过通用配置的通信/互连网络互连的异构主机计算机/处理器(节点)组成,其中存在几类作业,每类作业在每个主机和每个通信链路上具有其独特的延迟功能。该模型用于将多类作业负载平衡问题表述为非线性优化问题,其目标是最大程度地减少作业的平均响应时间。得出了有关优化问题解决方案的许多简单直观的理论结果。基于这些结果,我们提出了一种有效的负载平衡算法,用于平衡整个分布式/并行系统上的负载。所提出的算法具有两个吸引人的特征。一个是该算法可以以分散方式实现。另一个特点是结构简单明了。说明了节点模型,通信网络和数值示例。将该算法与著名的标准最速下降算法FD算法进行了比较。通过数值实验,我们证明了该算法在计算时间上比FD算法具有更快的收敛速度。

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