A Computation Offloading Model over Collaborative Cloud-Edge Networks with Optimal Transport Theory

TL;DR

This paper introduces a collaborative computation offloading model over cloud-edge networks using optimal transport theory, enhancing resource utilization, reducing execution time, and saving energy for diverse network services.

Contribution

It presents a novel flexible offloading mechanism based on optimal transport theory for collaborative cloud-edge computing, tailored to diverse network requirements.

Findings

Reduced execution time of computing tasks

Improved server resource utilization

Decreased terminal energy consumption

Abstract

As novel applications spring up in future network scenarios, the requirements on network service capabilities for differentiated services or burst services are diverse. Aiming at the research of collaborative computing and resource allocation in edge scenarios, migrating computing tasks to the edge and cloud for computing requires a comprehensive consideration of energy consumption, bandwidth, and delay. Our paper proposes a collaboration mechanism based on computation offloading, which is flexible and customizable to meet the diversified requirements of differentiated networks. This mechanism handles the terminal's differentiated computing tasks by establishing a collaborative computation offloading model between the cloud server and edge server. Experiments show that our method has more significant improvements over regular optimization algorithms, including reducing the execution time of computing tasks, improving the utilization of server resources, and decreasing the terminal's energy consumption.