ETOSP: Energy-efficient task offloading strategy based on partial offloading in mobile edge computing framework for efficient resource management

Abstract

<p>Mobile edge computing (MEC) is a very promising paradigm that facilitates efficient processing and analysis of Internet of Things (IoT) data at the network edge. MEC is a cloud-based platform that offers a range of online resources and sophisticated mobile apps to users of mobile devices. The continuing trend among mobile users is the growing need for accessing current apps and cloud-based services on their mobile computing devices (MCDs) with high data transfer rates and low latency. MCD’s frequently experience situations where they are either overwhelmed with excessive resource demands or insufficiently used due to imbalanced requests for resources. Offloading strategies play a crucial role in optimizing the efficiency of real-time data processing and analysis. This study proposes an ETOSP: Energy-efficient Task Offloading Strategy based on Partial Offloading in Mobile Edge Computing framework for efficient resource management as a solution to address the aforementioned difficulty. The application of the genetic algorithm is employed to produce strategies that provide balanced resource allocation for the purpose of identifying the most optimum offloading approach. The performance evaluation of ETOSP is shown through simulated experiments.</p>