Toward Optimal Load Prediction and Customizable Autoscaling Scheme for Kubernetes
-
Published:2023-06-12
Issue:12
Volume:11
Page:2675
-
ISSN:2227-7390
-
Container-title:Mathematics
-
language:en
-
Short-container-title:Mathematics
Author:
Mondal Subrota Kumar1ORCID, Wu Xiaohai1, Kabir Hussain Mohammed Dipu2, Dai Hong-Ning3ORCID, Ni Kan1, Yuan Honggang4, Wang Ting4
Affiliation:
1. School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China 2. Deakin University, Geelong, VIC 3216, Australia 3. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China 4. Software Engineering Institute, East China Normal University, Shanghai 200062, China
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.
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference67 articles.
1. A survey on virtualization technologies;Chiueh;Rpe Rep.,2005 2. Intel virtualization technology;Uhlig;Computer,2005 3. Mao, M., and Humphrey, M. (2012, January 24–29). A performance study on the vm startup time in the cloud. Proceedings of the 2012 IEEE 5th International Conference on Cloud Computing, Honolulu, HI, USA. 4. Xavier, M.G., Neves, M.V., Rossi, F.D., Ferreto, T.C., Lange, T., and De Rose, C.A. (March, January 27). Performance evaluation of container-based virtualization for high performance computing environments. Proceedings of the 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Belfast, UK. 5. Soltesz, S., Pötzl, H., Fiuczynski, M.E., Bavier, A., and Peterson, L. (2007, January 21–23). Container-based operating system virtualization: A scalable, high-performance alternative to hypervisors. Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, Lisbon, Portugal.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|