CAJ | 학술논문

对云计算中任务调度进行了研究，针对云计算的编程模型框架，提出一种融合遗传算法与蚁群算法的混合调度算法。在该求解方法中，遗传算法采用任务-资源的间接编码方式，每条染色体代表一种具体调度方案；选取任务平均完成时间作为适应度函数，再利用遗传算法生成的优化解，初始化蚁群信息素分布。既克服了蚁群算法初期信息素缺乏，导致求解速度慢的问题，又充分利用遗传算法的快速随机全局搜索能力和蚁群算法能模拟资源负载情况的优势。通过仿真实验将该算法和遗传算法进行比较，实验结果表明，该算法是一种云计算环境下有效的任务调度算法。
대운계산중임무조도진행료연구，침대운계산적편정모형광가，제출일충융합유전산법여의군산법적혼합조도산법。재해구해방법중，유전산법채용임무-자원적간접편마방식，매조염색체대표일충구체조도방안；선취임무평균완성시간작위괄응도함수，재이용유전산법생성적우화해，초시화의군신식소분포。기극복료의군산법초기신식소결핍，도치구해속도만적문제，우충분이용유전산법적쾌속수궤전국수색능력화의군산법능모의자원부재정황적우세。통과방진실험장해산법화유전산법진행비교，실험결과표명，해산법시일충운계산배경하유효적임무조도산법。
How to schedule masses of tasks efficiently is an important issue to be resolved in cloud computing environ-ment. An algorithm combining Genetic Algorithm(GA)and Ant Colony algorithm(ACO)is brought up for the program-ming framework of cloud computing. In the algorithm, the GA adopts task-worker coding method, every chromosome rep-resenting a specific scheduling scheme, and chooses the average completing time of all tasks as its fitness function. Then the ACO adopts Genetic Algorithm to give initial information pheromone to distribute. This combination not only over-comes the slow speed of ACO caused by lack of information pheromone on the path early, but also takes full use of GA, that is fast-speed, randomly and global search. There is a contrast between GA and the combined algorithm through simu-lation experiment, and the result shows the proposed algorithm is efficient in the cloud computing environment.