A placement architecture for a container as a service (CaaS) in a cloud environment

Journal of Cloud Computing: Advances, Systems and Applications

Mohamed H. Mousa¹  Mohamed K. Hussein¹  Mohamed A. Alqarni² 

[1] Faculty of Computers and Informatics, Suez Canal University;Faculty of Computers and Information Technology, University of Jeddah;

关键词: Cloud container;    Virtual machine placement;    Container placement;    Two-tier placement algorithm;    Ant colony;    Best fit;   

DOI  :  10.1186/s13677-019-0131-1

来源: DOAJ

【 摘 要 】

Abstract Unlike a traditional virtual machine (VM), a container is an emerging lightweight virtualization technology that operates at the operating system level to encapsulate a task and its library dependencies for execution. The Container as a Service (CaaS) strategy is gaining in popularity and is likely to become a prominent type of cloud service model. Placing container instances on virtual machine instances is a classical scheduling problem. Previous research has focused separately on either virtual machine placement on physical machines (PMs) or container, or only tasks without containerization, placement on virtual machines. However, this approach leads to underutilized or overutilized PMs as well as underutilized or overutilized VMs. Thus, there is a growing research interest in developing a container placement algorithm that considers the utilization of both instantiated VMs and used PMs simultaneously. The goal of this study is to improve resource utilization, in terms of number of CPU cores and memory size for both VMs and PMs, and to minimize the number of instantiated VMs and active PMs in a cloud environment. The proposed placement architecture employs scheduling heuristics, namely, Best Fit (BF) and Max Fit (MF), based on a fitness function that simultaneously evaluates the remaining resource waste of both PMs and VMs. In addition, another meta-heuristic placement algorithm is proposed that uses Ant Colony Optimization based on Best Fit (ACO-BF) with the proposed fitness function. Experimental results show that the proposed ACO-BF placement algorithm outperforms the BF and MF heuristics and maintains significant improvement of the resource utilization of both VMs and PMs.

【 授权许可】

Unknown