Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes

Subrota Kumar Mondal (), Xiaohai Wu, Hussain Mohammed Dipu Kabir, Hong-Ning Dai, Kan Ni, Honggang Yuan and Ting Wang
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Subrota Kumar Mondal: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
Xiaohai Wu: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
Hussain Mohammed Dipu Kabir: Deakin University, Geelong, VIC 3216, Australia
Hong-Ning Dai: Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
Kan Ni: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
Honggang Yuan: Software Engineering Institute, East China Normal University, Shanghai 200062, China
Ting Wang: Software Engineering Institute, East China Normal University, Shanghai 200062, China

Mathematics, 2023, vol. 11, issue 12, 1-30

Abstract: Most enterprise customers now choose to divide a large monolithic service into large numbers of loosely-coupled, specialized microservices, which can be developed and deployed separately. Docker, as a light-weight virtualization technology, has been widely adopted to support diverse microservices. At the moment, Kubernetes is a portable, extensible, and open-source orchestration platform for managing these containerized microservice applications. To adapt to frequently changing user requests, it offers an automated scaling method, Horizontal Pod Autoscaler (HPA), that can scale itself based on the system’s current workload. The native reactive auto-scaling method, however, is unable to foresee the system workload scenario in the future to complete proactive scaling, leading to QoS (quality of service) violations, long tail latency, and insufficient server resource usage. In this paper, we suggest a new proactive scaling scheme based on deep learning approaches to make up for HPA’s inadequacies as the default autoscaler in Kubernetes. After meticulous experimental evaluation and comparative analysis, we use the Gated Recurrent Unit (GRU) model with higher prediction accuracy and efficiency as the prediction model, supplemented by a stability window mechanism to improve the accuracy and stability of the prediction model. Finally, with the third-party custom autoscaling framework, Custom Pod Autoscaler (CPA), we packaged our custom autoscaling algorithm into a framework and deployed the framework into the real Kubernetes cluster. Comprehensive experiment results prove the feasibility of our autoscaling scheme, which significantly outperforms the existing Horizontal Pod Autoscaler (HPA) approach.

Keywords: Docker; Kubernetes; cloud computing; load prediction; autoscaling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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