An Enhanced Binary Particle-Swarm Optimization (E-BPSO) Algorithm for Service Placement in Hybrid Cloud Platforms
Wissem Abbes, Zied Kechaou, Amir Hussain, Abdulrahman M. Qahtani, Omar, Aimutiry, Habib Dhahri, Adel M. Alimi

TL;DR
This paper introduces an improved binary particle swarm optimization algorithm designed to optimize service placement in hybrid cloud platforms, reducing costs and execution time more effectively than existing methods.
Contribution
The paper presents a novel E-BPSO algorithm with a modified position update rule, enhancing robustness and avoiding local optima in service placement problems.
Findings
E-BPSO outperforms state-of-the-art algorithms in cost reduction.
E-BPSO achieves faster execution times.
The proposed method demonstrates improved robustness in complex solution spaces.
Abstract
Nowadays, hybrid cloud platforms stand as an attractive solution for organizations intending to implement combined private and public cloud applications, in order to meet their profitability requirements. However, this can only be achieved through the utilization of available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud solution, while allocating others to the public cloud. In this context, the present work is set to help minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. Several evolutionary algorithms have been applied to solve the service placement problem and are used when dealing with complex solution spaces to provide an optimal placement and often produce a short execution time. The standard BPSO…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Advanced Optical Network Technologies
