International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 1, No. 10, October 2020 | pp. 73–80

Research Article

Title: Optimizing Container Placement and Migration in Kubernetes Clusters Using Multi-Objective Particle Swarm Optimization

Names of Authors: Sven Lindqvist¹, Astrid Holm², and Frederik Jensen³

Authors’ Affiliations:
¹Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
²Department of Informatics, University of Oslo, Oslo, Norway
³Department of Information Technology, Technical University of Denmark, Kongens Lyngby, Denmark

Abstract: Containerization technologies managed by Kubernetes orchestration platforms have become the industry standard for deploying microservices and cloud-native applications. However, efficient container scheduling—the assignment of container pods to heterogeneous physical worker nodes—remains a complex NP-hard optimization problem. Default Kubernetes schedulers rely on basic heuristic bin-packing or resource-spreading strategies that often result in server load imbalances, high thermal hotspots, and excessive cross-node network latency. This paper introduces a multi-objective container placement and live migration framework utilizing Multi-Objective Particle Swarm Optimization (MOPSO) to optimize resource utilization across large-scale enterprise clusters. The optimization formulation simultaneously minimizes CPU and memory allocation variance across physical nodes, reduces inter-node network communication overhead measured in megabytes per second (MB/s), and limits total pod migration counts during workload shifts. A custom Kubernetes controller intercepts scheduling requests and feeds cluster telemetry metrics into the MOPSO engine to compute Pareto-optimal deployment configurations. Experimental results conducted on a test cluster running 500 microservice pods across 50 nodes show that the proposed approach improves overall cluster resource utilization balance by 29.3% and reduces inter-node network latency spikes by 35.7% compared to the default Kubernetes kube-scheduler. The findings establish that metaheuristic swarm intelligence effectively handles complex scheduling trade-offs in dynamic cloud environments.

Keywords: Container orchestration, Kubernetes, Particle swarm optimization, Resource allocation, Cloud computing, Microservices migration

Manuscript Timeline: Received: July 12, 2020; Revised: August 18, 2020; Accepted: September 14, 2020; Published: October 1, 2020

Citation: Lindqvist, S., Holm, A., & Jensen, F. (2020). Optimizing container placement and migration in Kubernetes clusters using multi-objective particle swarm optimization. International Journal of Computer Science and Technology, 1(10), 73–80. DOI: 10.46882/2020/IJCST/000010

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