Chen Dujing, Zhang Yanyan
School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Entropy (Basel). 2023 Feb 2;25(2):285. doi: 10.3390/e25020285.
With the increase in cloud users and internet of things (IoT) applications, advanced task scheduling (TS) methods are required to reasonably schedule tasks in cloud computing. This study proposes a diversity-aware marine predators algorithm (DAMPA) for solving TS in cloud computing. In DAMPA, to enhance the premature convergence avoidance ability, the predator crowding degree ranking and comprehensive learning strategies were adopted in the second stage to maintain the population diversity and thereby inhibit premature convergence. Additionally, a stage-independent control of the stepsize-scaling strategy that uses different control parameters in three stages was designed to balance the exploration and exploitation abilities. Two case experiments were conducted to evaluate the proposed algorithm. Compared with the latest algorithm, in the first case, DAMPA reduced the makespan and energy consumption by 21.06% and 23.47% at most, respectively. In the second case, the makespan and energy consumption are reduced by 34.35% and 38.60% on average, respectively. Meanwhile, the algorithm achieved greater throughput in both cases.
随着云用户和物联网（IoT）应用的增加，需要先进的任务调度（TS）方法来在云计算中合理地调度任务。本研究提出了一种用于解决云计算中任务调度问题的多样性感知海洋捕食者算法（DAMPA）。在DAMPA中，为了增强避免早熟收敛的能力，在第二阶段采用了捕食者拥挤度排名和综合学习策略来维持种群多样性，从而抑制早熟收敛。此外，设计了一种在三个阶段使用不同控制参数的步长缩放策略的阶段独立控制，以平衡探索和利用能力。进行了两个案例实验来评估所提出的算法。与最新算法相比，在第一个案例中，DAMPA最多分别将完工时间和能耗降低了21.06%和23.47%。在第二个案例中，完工时间和能耗平均分别降低了34.35%和38.60%。同时，该算法在两种情况下都实现了更高的吞吐量。
