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Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments

Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments

Maria Rodriguez, Rajkumar Buyya
Copyright: © 2020 |Pages: 24
ISBN13: 9781799827016|ISBN10: 1799827011|EISBN13: 9781799827023
DOI: 10.4018/978-1-7998-2701-6.ch010
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MLA

Rodriguez, Maria, and Rajkumar Buyya. "Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments." Handbook of Research on Multimedia Cyber Security, edited by Brij B. Gupta and Deepak Gupta, IGI Global, 2020, pp. 190-213. https://doi.org/10.4018/978-1-7998-2701-6.ch010

APA

Rodriguez, M. & Buyya, R. (2020). Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments. In B. Gupta & D. Gupta (Eds.), Handbook of Research on Multimedia Cyber Security (pp. 190-213). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-7998-2701-6.ch010

Chicago

Rodriguez, Maria, and Rajkumar Buyya. "Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments." In Handbook of Research on Multimedia Cyber Security, edited by Brij B. Gupta and Deepak Gupta, 190-213. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2701-6.ch010

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Abstract

Containers are widely used by organizations to deploy diverse workloads such as web services, big data, and IoT applications. Container orchestration platforms are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms optimize the scheduling of containers on a fixed-sized cluster and are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. This chapter presents a resource management approach with three objectives: 1) optimize the initial placement of containers by efficiently scheduling them on existing resources, 2) autoscale the number of resources at runtime based on the cluster's workload, and 3) consolidate applications into fewer VMs at runtime. The framework was implemented as a Kubernetes plugin and its efficiency was evaluated on an Australian cloud infrastructure. The experiments demonstrate that a reduction of 58% in cost can be achieved by dynamically managing the cluster size and placement of applications.

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