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PoPeC: PAoI-Centric Task Offloading with Priority over Unreliable Channels

Nan Qiao, Sheng Yue, Yongmin Zhang, Ju Ren

TL;DR

A joint resource allocation and task offloading algorithm to solve the freshness-aware task offloading problem as a multi-priority optimization problem, considering the unreliability of wireless channels, prioritized users, and the heterogeneity of edge servers is introduced.

Abstract

Freshness-aware computation offloading has garnered great attention recently in the edge computing arena, with the aim of promptly obtaining up-to-date information and minimizing the transmission of outdated data. However, most of the existing work assumes that wireless channels are reliable and neglect the dynamics and stochasticity thereof. In addition, varying priorities of offloading tasks along with heterogeneous computing units also pose significant challenges in effective task scheduling and resource allocation. To address these challenges, we cast the freshness-aware task offloading problem as a multi-priority optimization problem, considering the unreliability of wireless channels, the heterogeneity of edge servers, and prioritized users. Based on the nonlinear fractional programming and ADMM-Consensus method, we propose a joint resource allocation and task offloading algorithm to solve the original problem iteratively. To improve communication efficiency, we further devise a distributed asynchronous variant for the proposed algorithm. We rigorously analyze the performance and convergence of the proposed algorithms and conduct extensive simulations to corroborate their efficacy and superiority over the existing baselines.

PoPeC: PAoI-Centric Task Offloading with Priority over Unreliable Channels

TL;DR

A joint resource allocation and task offloading algorithm to solve the freshness-aware task offloading problem as a multi-priority optimization problem, considering the unreliability of wireless channels, prioritized users, and the heterogeneity of edge servers is introduced.

Abstract

Freshness-aware computation offloading has garnered great attention recently in the edge computing arena, with the aim of promptly obtaining up-to-date information and minimizing the transmission of outdated data. However, most of the existing work assumes that wireless channels are reliable and neglect the dynamics and stochasticity thereof. In addition, varying priorities of offloading tasks along with heterogeneous computing units also pose significant challenges in effective task scheduling and resource allocation. To address these challenges, we cast the freshness-aware task offloading problem as a multi-priority optimization problem, considering the unreliability of wireless channels, the heterogeneity of edge servers, and prioritized users. Based on the nonlinear fractional programming and ADMM-Consensus method, we propose a joint resource allocation and task offloading algorithm to solve the original problem iteratively. To improve communication efficiency, we further devise a distributed asynchronous variant for the proposed algorithm. We rigorously analyze the performance and convergence of the proposed algorithms and conduct extensive simulations to corroborate their efficacy and superiority over the existing baselines.
Paper Structure (68 sections, 14 theorems, 139 equations, 8 figures, 2 tables, 7 algorithms)

This paper contains 68 sections, 14 theorems, 139 equations, 8 figures, 2 tables, 7 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. MEC Architecture
  5. Task Offloading
  6. Transmission Model
  7. Limited Capacity Model with Confidence Evaluation
  8. Channel Capacity
  9. Computation Capacity
  10. Freshness Model and Problem Formulation
  11. PoPeC: PAoI-Centric Task Offloading with Priority over Unreliable Channels
  12. Priority-Free Task Scheduling
  13. Problem Transformation
  14. Problem Decomposition and Solving
  15. Multi-Priority Task Scheduling
  16. ...and 53 more sections

Key Result

Lemma 1

In Problem P1, Constraints eq:c7a and eq:c8a are equivalent to following Constraints eq:c7b and eq:c8b respectively:

Figures (8)

  • Figure 1: MEC Architecture with Unreliable Channles and Priority Users.
  • Figure 2: Impact of methods on various metrics.
  • Figure 3: Performance comparison between different methods.
  • Figure 4: The performance in various settings.
  • Figure 5: Performance of Priority Users.
  • ...and 3 more figures

Theorems & Definitions (28)

  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Theorem 2
  • proof
  • ...and 18 more