Computational Offloading and Time Allocation Policies in Mobile Edge Computing and Mobile Cloudlet

Wireless-powered Mobile Edge Computing (MEC) has been proving to be an auspicious paradigm to enhance the data processing competency of low-powered networks in light of the increasing need for diagnostic information retrieval. Applications of dividing a given load into smaller units and then executing each unit independently by different processors are a class of tasks that require a pressing need of parallel and distributed processing. However, it is challenging to decide whether the units will be offloaded to the edges of a cloud (MEC) or through a concept of Mobile Device Cloudlet (MDC) where a User Equipage (UE) with finite resources prefer to offload its units to a foreign UE. The network of the client UE and foreign UE is known as a cloudlet. Furthermore, the cost function developed assigns equal weights to the factors to optimise the analysis of policies which in actual fact is not persuasive. There is a need to improve energy efficiency of UEs and consider the latency dilema in cloud computing due to distant communication between UEs and remote cloud centres. To address these problems, our paper proposes an Offloading and Time Allocation Policy using MDC and MEC (OTPMDC) that implements whether a task should be offloaded through MEC or MDC in conjunction with the time allocation judgement for the UE to harvest energy and transmit information. To address the gap of the second issue, our goal is to train an intelligent deep learning-based decision-making algorithm that will choose an optimal set of applications based on the energy in the UE. We have formulated a cost function by considering the above policies that will generate an extensive dataset from which the algorithm will select the optimal sets and train a deep learning network. The obtained simulation results depict that performance of UEs is improved.