Abstract

Cloud computing makes extensive use of virtual machines that permit for workloads, as well as resource usage, to be isolated from one another, and a hypervisor can be used by a virtual machine to construct cloud computing infrastructure. However, the hypervisor has high resource usage when constructing virtual machines, which results in a waste of allocated resources when not activated. Docker provides a more light-weight method to obtain agile computing resources based on a container technique that handles this problem. In this study, we have chosen this specific tool due to the increasing popularity of MapReduce and cloud container technologies such as Docker. This study aims to automatically configure Twister workloads for container-driven clouds. Basically, this is the first attempt towards automatic configuration of Twister jobs on a container-based cloud platform VM for many workloads.