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Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework

Niazi, Mushtaq, Abbas, Sagheer, SOLIMAN, Abdel-Hamid, Alyas, Tahir, Asif, Shazia and Faiz, Tauqeer (2022) Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework. Computers, Materials & Continua, 74 (1). pp. 591-606. ISSN 1546-2226

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Abstract or description

Kubernetes is an open-source container management tool that automates container deployment, container load balancing, and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals besides the corporate sector. It provides cost-effective infrastructure, platform, and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing. As the global work environment is adapting constituents of industry 4.0 in terms of robotics, artificial intelligence, and IoT devices, it is becoming eminent that one emerging challenge is collaborative schematics. Provision of such autonomous mechanism that can develop, manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources, ensuring schedule completion. Collaborative schematics are also linked with Bigdata management produced by large-scale industry 4.0 setups. Different use cases and simulation results showed a significant improvement in Pod CPU utilization, latency, and throughput over Kubernetes environment.

Item Type: Article
Faculty: School of Digital, Technologies and Arts > Engineering
Depositing User: Abdel-Hamid SOLIMAN
Date Deposited: 03 Jan 2023 15:21
Last Modified: 03 Jan 2023 15:21
URI: https://eprints.staffs.ac.uk/id/eprint/7582

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