Elastic deployment of container clusters across geographically distributed cloud data centers for web applications

• Published in: Concurrency and computation 2021-11, Vol.33 (21), p.n/a
• Main Authors: Aldwyan, Yasser, Sinnott, Richard O., Jayaputera, Glenn T.
• Format: Article
• Language: English
• Subjects: Adaptation; Applications programs; Cloud computing; Clusters; Containers; Data centers; Docker; dynamic deployment; Geographical distribution; Kubernetes; multi‐cluster; Placement; Provisioning
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6436

Containers such as Docker provide a lightweight virtualization technology. They have gained popularity in developing, deploying and managing applications in and across Cloud platforms. Container management and orchestration platforms such as Kubernetes run application containers in virtual clusters that the overheads in managing the underlying infrastructures to simplify the deployment of container solutions. These platforms are well suited for modern web applications that can give rise to geographic fluctuations in use based on the location of users. Such fluctuations often require dynamic global deployment solutions. A key issue is to decide how to adapt the number and placement of clusters to maintain performance, whilst incurring minimum operating and adaptation costs. Manual decisions are naive and can give rise to: over‐provisioning and hence cost issues; improper placement and performance issues, and/or unnecessary relocations resulting in adaptation issues. Elastic deployment solutions are essential to support automated and intelligent adaptation of container clusters in geographically distributed Clouds. In this article, we propose an approach that continuously makes elastic deployment plans aimed at optimizing cost and performance, even during adaptation processes, to meet service level objectives (SLOs) at lower costs. Meta‐heuristics are used for cluster placement and adjustment. We conduct experiments on the Australia‐wide National eResearch Collaboration Tools and Resources Research Cloud using Docker and Kubernetes. Results show that with only a 0.5 ms sacrifice in SLO for the 95th percentile of response times we are able to achieve up to 44.44% improvement (reduction) in cost compared to a naive over‐provisioning deployment approach.