ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (3): 597-608.doi: 10.7544/issn1000-1239.2017.20151043

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Elastic Resource Provisioning Approach for Container in Micro-Service Architecture

Hao Tingyi1,2,3, Wu Heng1, Wu Guoquan1,2,Zhang Wenbo1   

  1. 1(Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190); 2(State Key Laboratory of Computer Science (Institute of Software, Chinese Academy of Sciences), Beijing 100190); 3(University of Chinese Academy of Sciences, Beijing 100049)
  • Online:2017-03-01

Abstract: As a logical abstraction of physical resources, container-based virtualization has been adopted widely in cloud computing environment for elastic resource provisioning, which is lower overhead and potentially better performance. Nowadays, more and more enterprises seek to move large-scale Internet-based applications with micro-service architecture into the container-based infrastructure, and focus on efficient resource management. Unfortunately, many existing approaches were restricted by physical machine or virtual environment, the resources are hard to be elastically or timely provisioning. Therefore, Internet-based applications may suffer from frequent service-level agreement(SLA) violations under flash-crowd conditions. To address this limitation, this thesis proposes a quality of service(QoS) sensitive resource provisioning approach for containers in micro-service architecture based on the feed-forward control. We employ a performance model based on queuing theory. Firstly, we capture the relationship among workload, resource utilization and response time. Secondly, we predict the response time with fuzzy federal adaptive Kalman filtering based on the feed-forward control, and if the prediction result is against pre-defined QoS, elastic resource scheduling process is triggered. Experimental results based on CloudStone show that the feed-forward algorithm converges quickly. The prediction result of the response time has only maximum error of 10%, and is more effective and accurate compared with existing approaches. Furthermore, our approach can effectively protect resource provisioning for flash-crowds workload.

Key words: container virtualization, fuzzy adaptive Kalman filtering, elastic resource provisioning, micro-service architecture, flash-crowds

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