Enhancing investigations in data migration and security using sequence cover cat and cover particle swarm optimization in the fog paradigm

Abstract

In recent years, fog and mobile edge computing have grown rapidly due to the large amount of data generated by the Internet of Thing (IoT) devices. It provides a variety of services within the end user IoT environment, but suffers from inefficient scheduling, which results in more significant delays than cloud computing. In this research we propose a data migration procedure that beats the metrics of delay, response time, and load balancing rate in the fog computing paradigm. It is possible to reduce the amount of replicated and integrated data by using Sequence Cover Cat Swarm Optimization (SCCSO) and Sequence Cover Particle Swarm Optimization (SCPSO) by using appropriate Virtual machines (VMs) which lock highly used machines and give service to low used machines in cloud communication storage.; this allows us to obtain resources efficiently in the fog environment. After being tested in the iFogsim climate, The protocols performed better in the iFogsim climate in terms of response time, scheduling time load balancing rate and delay than the other scheduling algorithms.

Keywords

• Optimization technique
• Scheduling algorithms
• Data migration
• Fog devices
• Edge computing
• Cloud layers and virtual machines