Energy Minimization Task Offloading Mechanism with Edge-Cloud Collaboration in IoT Networks

Abstract

With the development of Industrial Internet of Things (IIoT), the computation intensive tasks with restrict delay constraints generated at network edge emerge as the main challenge to the terminals with limited power and processing capability. The combination of edge and cloud computing has been demonstrated as one promising solution to such problem. In the edge-cloud collaboration (ECC) framework, the task scheduling among edges and cloud is of great importance on impacting the performance of the system. In this paper, we investigate the task offloading strategy to minimize the energy consumption of the networks. To achieve that, a time delay penalty mechanism, which searches the optimal power for edge to cloud task offloading under given delay constraint, is first proposed. On that basis, a low complexity edge-cloud matching algorithm leveraging the bipartite matching method is developed, to further minimize the execution energy consumption of all devices. Finally, to evaluate its efficiency, the proposed algorithm is deployed and tested on an novel edge-cloud computing collaboration platform. Both simulation and experiment results revel that our proposed scheme can achieve the less energy consumption compared with other alternatives. In addition, it also indicates that our proposed scheme can effectively matching resources from the edge to the cloud, especially for the issues that edge devices fail to meet demands due to limit processing ability.