A Meta-Heuristic Computation Offloading Strategy for IoT Applications in an Edge-Cloud Framework

Edge-cloud computing provides performance guarantees for IoT applications which are real-time or security sensitive. The new placement of edge-cloud services leverages resources both in Cloud Data Centers (CDC) and at the edge of the network. A computation task can be divided into subtasks and offloaded to different edge/cloud servers, which are donated as offloading destinations. Offloading destination heterogeneity and different architecture of Edge Data Center (EDC) and CDC bring challenges to computation offloading. One critical issue in edge-cloud computing is energy consumption in computation offloading. The existing computation offloading strategies either ignored energy consumption or ignored delay and/or security constraints. Meta-heuristic strategies have been used widely to design heuristic resource allocation algorithms in CDC. This paper aims to explore meta-heuristic energy-efficient computation offloading (EE-CO) approaches with the objective to meet the delay and security constraints, while minimizing energy consumption. To achieve the goal, we investigated the performance of the Ant-Colony-Optimization (ACO) strategies combining with mixed integer programming (MIP). We propose an ACO-based computation offloading strategy, which including two algorithms, called EA-OMIP and EA-RMIP, respectively. The only difference of them is the construction method of integer programming models. Simulations are carried out to value the performance of proposed two algorithms. We also give an analysis of the experimental results in terms of the subtask acceptance ratio, revenue of the cloud service provider (CSP), and the resource utilization.