Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 98, Issue -, Pages 672-681Publisher
ELSEVIER
DOI: 10.1016/j.future.2018.09.009
Keywords
Autoscaling; Container; Elasticity
Categories
Funding
- IBM Watson Group, IBM Massachusetts Lab
- National Science Foundation
- National Natural Science Foundation of China (NSFC) [41671441, 71171121]
- National '863' High Technology Research & Development Program of China (863 Project) [2012AA09A408]
- Collaborative Innovation Project of Shenzhen [GJHS20120702113359427]
- Shenzhen Science and Technology Project [JCYJ20151117173236192, CXZZ20140902110505864]
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Containers have been a pervasive approach to help rapidly develop, test and update the Internet of Things applications (IoT). The autoscaling of containers can adaptively allocate computing resources for various data volumes over time. Therefore, elasticity, a critical feature of a cloud platform, is significant to measure the performance of lightweight containers. In this paper, we propose a framework with container auto-scaler. It monitors containers resource usage and accordingly scales in or scales out containers in need. Further, we define elasticity mathematically in order to quantify the cloud elasticity using the proposed framework. Extensive experiments are carried out with different workload modes, workload durations, and scaling cool-down period of times. Experiment results show that the framework captures the workload variation firmly with a very short delay. We also find out that the cloud platform shows the best elasticity in repeat workload mode due to its recurring and predictable feature. Finally, we discover the length of the cool-down period should be properly set up in order to balance system stability and good elasticity. (C) 2018 Published by Elsevier B.V.
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