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Decentralized Network Topology Design for Task Offloading in Mobile Edge Computing

Ke Ma, Junfei Xie

TL;DR

A novel decentralized network topology design strategy for task offloading (DNTD-TO) that jointly considers topology design and task allocation and efficiently constructs three-layered network structures for task offloading and generates optimal task allocations.

Abstract

The rise of delay-sensitive yet computing-intensive Internet of Things (IoT) applications poses challenges due to the limited processing power of IoT devices. Mobile Edge Computing (MEC) offers a promising solution to address these challenges by placing computing servers close to end users. Despite extensive research on MEC, optimizing network topology to improve computational efficiency remains underexplored. Recognizing the critical role of network topology, we introduce a novel decentralized network topology design strategy for task offloading (DNTD-TO) that jointly considers topology design and task allocation. Inspired by communication and sensor networks, DNTD-TO efficiently constructs three-layered network structures for task offloading and generates optimal task allocations for these structures. Comparisons with existing topology design methods demonstrate the promising performance of our approach.

Decentralized Network Topology Design for Task Offloading in Mobile Edge Computing

TL;DR

A novel decentralized network topology design strategy for task offloading (DNTD-TO) that jointly considers topology design and task allocation and efficiently constructs three-layered network structures for task offloading and generates optimal task allocations.

Abstract

The rise of delay-sensitive yet computing-intensive Internet of Things (IoT) applications poses challenges due to the limited processing power of IoT devices. Mobile Edge Computing (MEC) offers a promising solution to address these challenges by placing computing servers close to end users. Despite extensive research on MEC, optimizing network topology to improve computational efficiency remains underexplored. Recognizing the critical role of network topology, we introduce a novel decentralized network topology design strategy for task offloading (DNTD-TO) that jointly considers topology design and task allocation. Inspired by communication and sensor networks, DNTD-TO efficiently constructs three-layered network structures for task offloading and generates optimal task allocations for these structures. Comparisons with existing topology design methods demonstrate the promising performance of our approach.

Paper Structure

This paper contains 11 sections, 1 theorem, 11 equations, 2 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Modeling and Problem Formulation
  3. System Modeling
  4. Problem Formulation
  5. Decentralized Network Topology Design for Task Offloading (DNTD-TO)
  6. Local Cluster Formation (LCF)
  7. Cluster Selection
  8. Simulation studies
  9. Experiment Setup
  10. Experiment Results
  11. Conclusion and Future Works

Key Result

Lemma 1

Consider problem $\mathcal{P}_1$. The optimal task allocation, denoted as $y^*_l$, $\forall l\in C_i \cup \{i\}$, satisfy $\alpha_iy^*_i = \alpha_ly^*_l$, $\forall l\neq i, l\in C_i$, when $C_i \neq \emptyset$. Consequently, the minimum task processing time is given by $J^* = \alpha_iy^*_i = \alpha_

Figures (2)

  • Figure 1: Performance comparison across different topologies when (a) $N=20$ and (b) $N=100$.
  • Figure 2: (a) Illustration of the network with $\xi=10$m. "$\boldsymbol{\times}$" marks the master and the dashed circles indicates the communication range; (b) Performance comparison for different values of $\xi$.

Theorems & Definitions (3)

  • Lemma 1
  • Remark 1
  • Remark 2