Software Defined Resource Allocation in Mobile Edge Computing

Abstract

In the environment of multiple edges, an unbalanced distribution of offloaded tasks can result in a lack of edge resources, which in turn which in turn leads to lower performance. On the other hand, fast decisions regarding edge selection are crucial for efficient performance. Therefore, this paper suggests an edge-edge network based on software-defined networks to manage resources and tasks at the mobile edge of computing in the Internet of Things (IoT) environment. The proposed technique in this paper introduces an effective method for making selections concerning collaborative offloading tasks within edge computing environments based on Software-Defined Networks (SDN), which will decide where to offload and process tasks on the optimal Mobile Edge Computing (MEC) server among five MEC servers based on currently available resources when tasks need processing during a specific time using SDN controller that view the status of all network. The rank of the feasible MEC server is based on the presently available CPU frequency of the MEC server relative to the required computing resources for the task. To calculate the final height score of the MEC server, this work used Min-Max normalization and a high score for the MEC server from these servers that were considered optimal for offloading tasks. This paper aims to maintain the total latency as little as much as possible.