异构云中面向集群负载均衡的任务调度策略

负载均衡是提高资源利用率和系统稳定性的重要手段。基于改进的自适应变异粒子群算法，提出了一种异构环境下面向集群负载均衡的任务调度策略。在调度策略的设计中，融入了经济学“二八”定律，通过把握用户对集群节点安全性和可靠性的偏好程度并预估任务的负载信息，在保证系统负载尽量均衡的前提下，最小化任务执行时间的同时提高大客户满意度。仿真实验显示，改进的自适应变异粒子群算法比未改进的自适应变异粒子群算法和基本粒子群算法在收敛速度和跳出局部最优两个方面都有更好的表现。结果表明，改进的自适应变异粒子群算法在保证集群负载均衡的同时可以更好地提高云服务提供商的利润空间。

Task scheduling strategy based on load balance of cluster in heterogeneous cloud environment

Load balancing is an important means to improve resource utilization and system stability. Based on Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm, a new task scheduling model and strategy about load balancing for cluster in heterogeneous cloud environment were proposed. In order to maximize customer satisfaction degree and reduce the total execution time of a collection of tasks under ensuring the system load as much balanced as possible, a concept of user bias degree on cluster node performance such as safety and reliability and a method of grasping the degree of preference on security and reliability of cluster nodes and estimating the load information of the tasks were added into the design of scheduling policy. The simulation shows that the improved AMPSO algorithm performs better than the original AMPSO algorithm and the basic Particle Swarm Optimization (PSO) algorithm at convergence speed and the capacity of jumping out the local optimum. The results prove that the improved AMPSO can better improve the profit margins of the cloud service provider while ensuring the load balancing of the cluster.