Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

TL;DR

This paper introduces a dynamic pricing mechanism and a novel DISC-PSO algorithm for near-optimal resource allocation in mobile edge computing, effectively balancing user utility and resource efficiency.

Contribution

It presents a new pricing model and an efficient optimization algorithm tailored for elastic edge offloading in MEC systems.

Findings

DISC-PSO reduces iteration count by 92.11%

Increases user utility by 0.24%

Decreases result variance by 95.45%

Abstract

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11\%, increasing the final user utility function value by 0.24\%, and decreasing the variance of results by 95.45\%.