PECCO: A Profit and Cost-oriented Computation Offloading Scheme in Edge-Cloud Environment with Improved Moth-flame Optimisation

TL;DR

This paper introduces PECCO, a novel profit and cost-oriented offloading model for edge-cloud computing, optimized using an improved Moth-flame algorithm, demonstrating superior performance through extensive experiments.

Contribution

It presents a new joint optimization model considering profit, cost, heterogeneity, and load balancing, and proposes an enhanced Moth-flame optimizer tailored for this complex problem.

Findings

The proposed PECCO model effectively balances profit and cost in edge-cloud offloading.

The PECCO-MFI algorithm outperforms existing methods in optimization quality.

Experimental results show improved QoS and resource utilization.

Abstract

With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource-rich cloud centres have been utilised to tackle these challenges. To relieve the burden on cloud centres, edge-cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and Quality of Service (QoS). Several optimisation models of edge-cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and cost-oriented computation offloading optimisation model PECCO proposed in this paper. Considering that the model is hard in nature and the optimisation objective is not differentiable, we propose an improved Moth-flame optimiser PECCO-MFI which addresses some deficiencies of the original Moth-flame Optimiser and integrate it under the edge-cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimising the proposed task offloading model under the edge-cloud environment.