An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms
An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms
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DOI：
10.1007/s00521-022-07839-5
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发表时间：
2018-06
影响因子：
6
通讯作者：
Wissem Abbes;Zied Kechaou;Amir Hussain;A. Qahtani;Omar Almutiry;Habib Dhahri;A. Alimi
中科院分区：
文献类型：
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作者：
Wissem Abbes;Zied Kechaou;Amir Hussain;A. Qahtani;Omar Almutiry;Habib Dhahri;A. Alimi
混合云平台为有意实施集成的私有云和公共云应用以满足其盈利要求的组织提供了一个有吸引力的解决方案。然而，这只能通过在加快执行过程的同时利用可用资源来实现。因此，部署新应用程序需要将其中一些进程专用于私有云，而将其他进程分配给公共云。在此背景下，目前的工作旨在最大限度地减少相关成本，并在最短的执行时间内提供最佳服务安置解决方案的有效选择。到目前为止，一些进化算法已经被应用于解决具有挑战性的服务布局问题，通过处理复杂的解空间来提供相对较短的执行时间的最优布局。特别是，标准的BPSO算法被发现存在一个显著的缺点，即陷入局部最优，并且在处理服务放置问题时表现出明显的缺乏稳健性。因此，为了克服标准BPSO算法的不足，提出了一种改进的二进制粒子群优化算法(E-BPSO)，该算法受粒子位置更新方程的启发对其进行了改进。使用实际基准任务，我们提出的E-BPSO算法在代价和执行时间方面都优于最先进的方法。
Hybrid cloud platforms offer an attractive solution to organizations interested in implementing integrated private and public cloud applications to meet their profitability requirements. However, this can only be achieved by utilizing available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud while allocating others to the public cloud. In this context, the current work aims to minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. To date, several evolutionary algorithms have been applied to solve the challenging service placement problem by dealing with complex solution spaces to provide an optimal placement with relatively short execution times. In particular, the standard BPSO algorithm has been found to display a significant disadvantage, namely getting trapped in local optima and demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome the critical shortcomings associated with the standard BPSO, an enhanced binary particle swarm optimization (E-BPSO) algorithm is proposed, comprising a modification inspired by the continuous PSO for the particle position updating equation. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches using a real benchmark task in terms of both cost and execution time.