Cloud computing maps tasks to resources in a scalable fashion. The scheduling is an NP-hard problem; thus, the scheduler chooses one solution from among many. This is the reason why finding the best optimal solution, especially at a high scale of the system, is not possible. Applying metaheuristic algorithms to find a near-to-optimal solution, not the best one, could be the right approach. Dragonfly metaheuristic algorithm explores and exploits a solution space by the inspiration of hunting and emigration behavior of dragonflies in nature. But it suffers from the premature convergence of the algorithm to an undesirable when explores the solution space. In this research, an improved dragonfly algorithm (applying biogeography-based algorithm, Mexican hat wavelet and dragonfly algorithm—BMDA) is presented to resolve the premature convergence by applying a mutation phase that is the combination of the biogeography-based optimization (BBO) migration process and Mexican hat wavelet transform in dragonfly algorithm. Then, it is applied for dynamically scheduling tasks under the BMDDSF framework (BBO-Mexican hat wavelet-dragonfly dynamic scheduling framework) in the cloud computing environment. The purpose is customizing a metaheuristic algorithm to be applied in the resource manager of cloud computing to improve its performance. The BMDA algorithm was firstly evaluated for the mean error in comparison with the baseline algorithms using the CEC2017 benchmark functions. Then, the performance of the BMDDSF framework in cloud computing was tested using NASA parallel workload compared with baseline methods in terms of execution time, response time and reduction of service-level agreement violation. The experiments showed that the presented method outweighed the baseline approaches.

中文翻译：

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应用蜻蜓算法、基于生物地理学的优化算法和墨西哥帽小波的云计算任务动态调度

云计算以可扩展的方式将任务映射到资源。调度是一个 NP-hard 问题；因此，调度程序从许多解决方案中选择一个。这就是为什么不可能找到最佳解决方案的原因，尤其是在系统的大规模系统中。应用元启发式算法来寻找接近最优的解决方案，而不是最好的解决方案，可能是正确的方法。蜻蜓元启发式算法受自然界中蜻蜓的狩猎和迁徙行为的启发，探索和利用了一个解空间。但是在探索解空间时，它会受到算法过早收敛的影响。在本研究中，一种改进的蜻蜓算法（应用基于生物地理学的算法，提出了墨西哥帽小波和蜻蜓算法——BMDA）通过应用突变阶段来解决早熟收敛问题，该突变阶段是蜻蜓算法中基于生物地理学的优化（BBO）迁移过程和墨西哥帽小波变换的结合。然后，将其应用于云计算环境下BMDDSF框架（BBO-Mexican hat小波-蜻蜓动态调度框架）下的动态调度任务。目的是定制一种元启发式算法，应用于云计算的资源管理器中以提高其性能。与使用 CEC2017 基准函数的基线算法相比，首先评估 BMDA 算法的平均误差。然后，BMDDSF 框架在云计算中的性能使用 NASA 并行工作负载与基线方法在执行时间、响应时间和减少违反服务级别协议方面进行了比较。实验表明，所提出的方法超过了基线方法。