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

Yun Xia, Hai Xue, Di Zhang, Shahid Mumtaz, Xiaolong Xu, Joel J. P. C. Rodrigues

TL;DR

This work addresses resource allocation in mobile edge computing under limited edge-server resources by introducing a dynamic pricing mechanism that links ES resources to EU offloading. It develops a rigorous EU utility model that exhibits a local maximum with respect to $(F_{server},B)$ and proposes DISC-PSO to locate near-optimal allocations within a prescribed precision. The authors prove that near the EU local maximum, the ES utility is also near-optimal, and validate the approach through extensive simulations showing substantially faster convergence and improved stability compared to baseline algorithms. The resulting framework offers a practical, incentive-aligned solution for elastic edge offloading with potential real-world impact in remote MEC deployments.

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\%.

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

TL;DR

This work addresses resource allocation in mobile edge computing under limited edge-server resources by introducing a dynamic pricing mechanism that links ES resources to EU offloading. It develops a rigorous EU utility model that exhibits a local maximum with respect to and proposes DISC-PSO to locate near-optimal allocations within a prescribed precision. The authors prove that near the EU local maximum, the ES utility is also near-optimal, and validate the approach through extensive simulations showing substantially faster convergence and improved stability compared to baseline algorithms. The resulting framework offers a practical, incentive-aligned solution for elastic edge offloading with potential real-world impact in remote MEC deployments.

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\%.
Paper Structure (21 sections, 2 theorems, 48 equations, 9 figures, 5 tables, 1 algorithm)

This paper contains 21 sections, 2 theorems, 48 equations, 9 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Optimization algorithm-based resource allocation
  4. Stackelberg game-based resource allocation
  5. Auction-based resource allocation
  6. system model and problem formulation
  7. System model
  8. Time consumption model
  9. Energy consumption model
  10. Validation function of EU
  11. Validation function of ES
  12. Server optimal policy
  13. performance evaluation
  14. Simulation Setup
  15. The effect of $F_{server}$ on $P$, $U_{user}$, and $U_{server}$.
  16. ...and 6 more sections

Key Result

Lemma 1

$\mathcal{H}$ is a negative definite matrix.

Figures (9)

  • Figure 1: System model.
  • Figure 2: Effect of $F_{server}$ on $P$, $U_{user}$, and $U_{server}$.
  • Figure 3: Effect of $B$ on $P$, $U_{user}$, and $U_{server}$.
  • Figure 4: Effect of $q$ on $P$, $U_{user}$, and $U_{server}$.
  • Figure 5: Effect of $F_{local}$ on $P$, $U_{user}$, and $U_{server}$.
  • ...and 4 more figures

Theorems & Definitions (4)

  • Lemma 1
  • Proof 1
  • Lemma 2
  • Proof 2