Unequal‐interval based loosely coupled control method for auto‐scaling heterogeneous cloud resources for web applications. (8th July 2020)

• Record Type: Journal Article
• Authors: Cai, Zhicheng; Liu, Duan; Lu, Yifei; Buyya, Rajkumar
• Other Names: Xu Zheng guestEditor.; Zhou Qingyuan guestEditor.; Dong Fang guestEditor.; Yong Jianming guestEditor.; Fei Xiang guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 23(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-08
• Subjects: Cloud computing -- feedback control -- queuing model -- resource provisioning -- Spot VM; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5926
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Most existing quality of service (QoS) control algorithms of Web applications take into account Web Server or database connections which can be released immediately. However, many applications are deployed on virtual machines (VMs) or even Spot VMs elastically rented from public Clouds. To save costs, interval‐priced VMs are not released until the ends of rented intervals. Such delays of control effects make existing methods rent or release excess VMs leading to overcontrol. Fluctuated prices make Spot VMs unreliable due to unexpected termination which makes fault‐tolerant strategies crucial. In this article, an unequal‐interval‐based loosely coupled control method is proposed to improve the quality of service (QoS) control ability of fault‐tolerant strategies. A queuing model with arrival‐rate‐adjustment coefficient is used to predict required capacity as a feedforward controller. Another two‐threshold and queuing‐model‐based method is applied to update the coefficient as a loosely coupled feedback controller. Meanwhile, unequal‐interval controller collaborating method is proposed to avoid overcontrol and react quickly to workload changes. Our approach is evaluated on both a simulation platform and a real Kubernetes Cluster. Experimental results illustrate that our approach decreases the percentage of waiting times larger than service level agreements with similar or lower rental costs compared with existing algorithms.