A context aware resource augmentation framework for computation intensive task offloading in mobile edge computing

Abstract

Nowadays, the exploitation of resources available on Internet through mobile devices is increasing drastically among mobile internet users. Hence, the development of resource-rich and also IoT based mobile application are growing in the commercial market. However, resource constraints such as finite storage, restricted battery power, and reduced processing capability hinder the advancement of resource-intensive mobile applications. Several investigations are employing a predominant resource-saving technique called offloading to outsource heavy-processing and storage tasks from mobile devices to outside entities. In Mobile Cloud Computing (MCC), resource-hungry tasks are offloaded to resource-rich clouds. But the vast number of intermediate hops between mobile devices and remote cloud services also raises the network traffic, response time and delay. To solve the problems with MCC, mobile edge computing is introduced which provides processing closer to the mobile device thereby renders reduced network traffic, low execution time, faster response time and reduced consumption of energy. In the proposed research, an abstract model is designed with five key components such as mobile device, offloading decision engine, augmentation engine, scheduler, and synchronizer. Availability and scalability of native resources like CPU, RAM, and battery capacity are the significant parameters from the perspective of a mobile user. newline newline newline