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Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover

Ling Hou, Shi Li, Zhishu Shen, Jing Fu, Jingjin Wu, Jiong Jin

TL;DR

This paper model the offloading problem using the restless multi-armed bandit (RMAB) framework and develop two scalable online policies that prioritize resources based on their marginal costs, demonstrating that the policies significantly outperform baseline methods in power conservation and show robust performance under non-exponentially distributed task lifespans.

Abstract

The rapid growth of mobile devices and the increasing complexity of tasks have made energy efficiency a critical challenge in Multi-Access Edge Computing (MEC) systems. This paper explores energy-efficient offloading strategies in large-scale MEC systems with heterogeneous mobile users, diverse network components, and frequent task handovers to capture user mobility. The problem is inherently complex due to the system's scale, task and resource diversity, and the need to maintain real-time performance. Traditional optimization approaches are often computationally infeasible for such scenarios. To tackle these challenges, we model the offloading problem using the restless multi-armed bandit (RMAB) framework and develop two scalable online policies that prioritize resources based on their marginal costs. The proposed policies dynamically adapt to the system's heterogeneity and mobility while ensuring near-optimal energy efficiency. Through extensive numerical simulations, we demonstrate that the policies significantly outperform baseline methods in power conservation and show robust performance under non-exponentially distributed task lifespans. These results highlight the practical applicability and scalability of our approach in dynamic MEC environments.

Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover

TL;DR

This paper model the offloading problem using the restless multi-armed bandit (RMAB) framework and develop two scalable online policies that prioritize resources based on their marginal costs, demonstrating that the policies significantly outperform baseline methods in power conservation and show robust performance under non-exponentially distributed task lifespans.

Abstract

The rapid growth of mobile devices and the increasing complexity of tasks have made energy efficiency a critical challenge in Multi-Access Edge Computing (MEC) systems. This paper explores energy-efficient offloading strategies in large-scale MEC systems with heterogeneous mobile users, diverse network components, and frequent task handovers to capture user mobility. The problem is inherently complex due to the system's scale, task and resource diversity, and the need to maintain real-time performance. Traditional optimization approaches are often computationally infeasible for such scenarios. To tackle these challenges, we model the offloading problem using the restless multi-armed bandit (RMAB) framework and develop two scalable online policies that prioritize resources based on their marginal costs. The proposed policies dynamically adapt to the system's heterogeneity and mobility while ensuring near-optimal energy efficiency. Through extensive numerical simulations, we demonstrate that the policies significantly outperform baseline methods in power conservation and show robust performance under non-exponentially distributed task lifespans. These results highlight the practical applicability and scalability of our approach in dynamic MEC environments.
Paper Structure (27 sections, 34 equations, 9 figures, 1 table, 2 algorithms)

This paper contains 27 sections, 34 equations, 9 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. MEC Offloading
  4. Task Handover in MEC
  5. Discussion
  6. System Model
  7. Network Model
  8. Edge Offloading with Task Handover
  9. Cloud Offloading
  10. Energy Efficiency
  11. Stochastic Process and Optimization
  12. Scheduling Policy
  13. Randomization and Relaxation
  14. Highest Energy Efficiency with Adjusted Capacity Coefficients (HEE-ACC)
  15. HEE-ACC-zero
  16. ...and 12 more sections

Figures (9)

  • Figure 1: A simple handover example for the network system. CN1 and CN2, SC1, and a sub-channel in each of the channels are highlighted in red, as they are occupied/reserved by a task generated by the moving MTs.
  • Figure 2: Performance evaluation of HEE-ACC-zero and HEE-ALRN against the timeline, where $\rho = 7.5$.
  • Figure 3: Performance evaluation of HEE-ACC-zero and HEE-ALRN against the timeline, where $\rho = 10$.
  • Figure 4: Performance evaluation of HEE-ACC-zero and HEE-ALRN against the timeline, where different task groups have different offered traffic intensities.
  • Figure 5: Performance evaluation of HEE-ACC-zero and HEE-ALRN against the scaling parameter, where $\rho = 7.5$.
  • ...and 4 more figures

Theorems & Definitions (2)

  • proof
  • proof