基於服務層級協議及應用效能之容器大小及數量調整系統

蔡佑隆

doi:10.6342/NTU201601866

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基於服務層級協議及應用效能之容器大小及數量調整系統

Container Number and Size Management System Based on SLA and Application Performance

蔡佑隆 , Masters　　Advisor：劉邦鋒　　Co-advisor ：吳真貞

英文 DOI： 10.6342/NTU201601866

雲端計算；客戶端- 服務器系統；容器；服務層級協議； Cloud computing ； Client-sever systems ； Container ； Service level agreement

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In this paper we present a cloud resource management framework that determines a proper container size for each application in order to reduce the container cost, then dynamically adjusts the number of containers based on resource usage and application service level agreement.  We propose a cost model that consider starting/stopping/running/adjusting containers, and propose a dynamic programming algorithm to adjust the number of running containers so as to minimize the total cost when the resource requirement changes dynamically.  We also propose a greedy method that effectively adjusts the number of containers as the dynamic programming does, but requires much less computation time.  Our experiment confirms that the greedy method is both effective and efficient.  We also confirm that the a good container size for the same hour yesterday is a good approximation for the same hour today.  That is, the number of best container size exhibits temporal similarity, according to the historical trace we obtained.

Reference ( 25 ) 〈TOP〉

[1] The internet traffic archive. http://ita.ee.lbl.gov/.
[2] Michael Armbrust, O Fox, Rean Griffith, Anthony D Joseph, Y Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, et al. M.: Above the clouds: a berkeley view of cloud computing. 2009.
[3] Raymond Keith Clark. Scheduling dependent real-time activities. PhD thesis, Carnegie Mellon University, 1990.
[4] Tharam Dillon, Chen Wu, and Elizabeth Chang. Cloud computing: issues and challenges. In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on, pages 27–33. Ieee, 2010.
[5] Matangini Chattopadhyay Diptangshu Pandit, Samiran Chattopadhyay and Nabendu Chaki. Resource allocation in cloud using simulated annealing. In Applications and Innovations in Mobile Computing (AIMoC), pages 21–27. IEEE, 2014.

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