CAJ | 학술논문

针对云计算服务集群任务调度和负载平衡的优化问题，提出一种粒子群结合遗传算法（PSO-GA）的云计算任务调度方法。PSO-GA算法在遗传算法的基础上对种群进行分隔，用粒子群算法来构造变异算子，避免了变异算子的随机性和盲目性，很好地保持种群的多样性，克服了早熟现象。在CloudSim平台进行模拟测试。实验结果表明，与同类算法相比，该调度方法能够缩短云计算下任务执行总时间，提高资源利用率。
침대운계산복무집군임무조도화부재평형적우화문제，제출일충입자군결합유전산법（PSO-GA）적운계산임무조도방법。PSO-GA산법재유전산법적기출상대충군진행분격，용입자군산법래구조변이산자，피면료변이산자적수궤성화맹목성，흔호지보지충군적다양성，극복료조숙현상。재CloudSim평태진행모의측시。실험결과표명，여동류산법상비，해조도방법능구축단운계산하임무집행총시간，제고자원이용솔。
Focusing on Cloud Computing services for task scheduling and load balancing cluster optimization problem,a PSO algorithm is proposed,combined with genetic algorithm (PSO-GA)in Cloud Computing task scheduling.Based on genetic algorithm separates populations,particle swarm algorithm is used to construct the mutation operator in PSO-GA algorithm.As a result,the mutation operator randomness and blindness are avoided,with population diversity maintained very well and premature phenomenon overcome.Extending the cloud computing emulator CloudSim platform to test the simulation,the re-sults show that PSO-GA can shorten the total cloud computing task execution time and improve resource utilization.