Optimization of Resource Allocation in Mobile Edge Computing using Bio-Inspired Algorithm

Abstract

Cloud computing provides consumers with a forum for accessing services and data over the Internet. With an increasing number of machine-type networking systems, machine-to-machine (M2M) networking has piqued the attention of both academia and industry (MTCDs). Unlike traditional networking networks, M2M communications data links are typically narrow yet high bandwidth, necessitating capacity control of both energy usage and computation. Task offloading, congestion management, resource sharing, protection and privacy issues, mobility, and standardization are the key problems in mobile edge computing. Our research focuses on offloading-based resource utilization and security problems in cloud environments by analyzing network parameters such as latency reduction and bandwidth optimization. We present an Ant bee colony algorithm, which have been modified with tracking and trace back procedures, to reduce execution time and maximize computational resource distribution while also improving computing capabilities.