Task grouping and optimized deep learning based VM sizing for hosting containers as a service

Abstract

Containers as a service (CaaS) are a kind of services that permits the organization to handle the containers more effectively. Containers are lightweight, require less computing resources, portable, and facilitate better support for microservices. In the CaaS model, containers are deployed in virtual machines, and the virtual machine runs on the physical machine. The objective of this paper is to estimate the resource required by a VM to execute a number of containers. VM sizing is a directorial process where the system administrators have to optimize the allocated resources within the permitted virtualized space. In this work, the VM sizing is carried out using the Deep Convolutional Long Short Term Memory Network (Deep-ConvLSTM), where the weights are updated by Fractional Pelican Optimization (FPO) Algorithm.