CAJ | 학술논문

资源使用一直都是研究的重点，由于云计算中需要处理的任务量与数据量都十分巨大，为了能够更好地合理分配云计算资源，有效地调度任务执行，使得用户能够在短时间和低成本中完成目标，本文提出了一种基于时间和成本的遗传算法，通过对任务完成的总时间、总成本以及任务平均时间和任务平均成本四个主要因素进行分析，构建了相应的适应度函数，改进后的算法能够有效的解决云计算的时间和成本的因素，仿真实验中通过与近年来几个较新的其他改进云计算算法相比，本文算法具有更高的调度效率。
자원사용일직도시연구적중점，유우운계산중수요처리적임무량여수거량도십분거대，위료능구경호지합리분배운계산자원，유효지조도임무집행，사득용호능구재단시간화저성본중완성목표，본문제출료일충기우시간화성본적유전산법，통과대임무완성적총시간、총성본이급임무평균시간화임무평균성본사개주요인소진행분석，구건료상응적괄응도함수，개진후적산법능구유효적해결운계산적시간화성본적인소，방진실험중통과여근년래궤개교신적기타개진운계산산법상비，본문산법구유경고적조도효솔。
The use of resources has always been a focus in research. As there are large quantities of tasks and data to be processed, in order to better and reasonably allocate cloud computing resources, effectively schedule and execute tasks, and enable users to achieve their goals in a short time and at low cost, a time and cost genetic algorithm is presented in his paper. Through analyzing the four main factors including total time and cost and the average time and cost spent in finishing tasks, a corresponding fitness function is built, which allows the improved algorithm to effectively solve the time and cost factors of cloud computing. In the simulation experiment, through comparison with several other relatively new Cloud algorithms in recent years, this algorithm has higher scheduling efficiency.