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A Load Balancing Algorithm Based on Maximum Entropy Methods in Homogeneous Clusters

机译:同质集群中基于最大熵方法的负载均衡算法

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In order to solve the problems of ill-balanced task allocation, long response time, low throughput rate and poor performance when the cluster system is assigning tasks, we introduce the concept of entropy in thermodynamics into load balancing algorithms. This paper proposes a new load balancing algorithm for homogeneous clusters based on the Maximum Entropy Method (MEM). By calculating the entropy of the system and using the maximum entropy principle to ensure that each scheduling and migration is performed following the increasing tendency of the entropy, the system can achieve the load balancing status as soon as possible, shorten the task execution time and enable high performance. The result of simulation experiments show that this algorithm is more advanced when it comes to the time and extent of the load balance of the homogeneous cluster system compared with traditional algorithms. It also provides novel thoughts of solutions for the load balancing problem of the homogeneous cluster system.
机译:为了解决集群系统分配任务时任务分配不均衡,响应时间长,吞吐率低,性能差的问题,我们将热力学中的熵概念引入负载均衡算法中。提出了一种基于最大熵方法的同构集群负载均衡算法。通过计算系统的熵并使用最大熵原理来确保每次调度和迁移都遵循熵的增长趋势,系统可以尽快达到负载平衡状态,缩短任务执行时间并启用高性能。仿真实验结果表明,与同类算法相比,该算法在同类集群系统负载均衡的时间和程度上更为先进。它还为同类集群系统的负载平衡问题提供了解决方案的新颖思路。

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