Energy conserving cost selection for fine-grained computational offloading in mobile edge computing networks

Abstract

In mobile edge computing networks, total or partial computational offloading of delay sensitive and compute intensive applications to the nearby edge servers is a promising solution to reduce transmission delay and energy consumption of mobile nodes (MNs). However, in computational offloading of an application to edge server, considerations of energy consumption and time delay are not sufficient for the decision of minimum cost of application. In this article, a novel battery dependent cost selection strategy for computational offloading of an application capable for sequential execution of dependent sub tasks is proposed. The strategy considers current battery level of MN in the cost function for optimal selection of offloading policy based on the amount of data, computational resources, and radio resources of the server. Our comprehensive cost function takes into account all the said parameters, calculates all the costs for possible offloading policies, and chooses the policy with minimum cost from a huge dataset. The cost selection is energy efficient for low battery level and prioritizes faster execution for full battery level of MNs. A deep neural network is trained on the generated dataset to minimize the overhead of computations. Simulation results reveal that our proposed strategy outperforms the benchmark strategies in terms of deep neural network accuracy for different sizes of dataset, energy consumption, time delay, and cost for different sizes of applications.