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Sequential Task Assignment and Resource Allocation in V2X-Enabled Mobile Edge Computing

Yufei Ye, Shijian Gao, Xinhu Zheng, Liuqing Yang

TL;DR

The paper tackles energy-efficient execution of sequential-subtask applications in MEC-enabled V2X networks by introducing a two-tier offloading framework: first, NV-HV matching within the Vehicle Tier to maximize local collaboration, and second, RSU-tier coordination for unmatched NVs. It develops convex optimization-based solutions (via KKT conditions and bisection) for the Vehicle Tier and a convex-then-adjacent-integer strategy for the RSU Tier, complemented by a two-step subchannel allocation method to approach continuous optimality. The key contributions are the NV-HV matching algorithm, the P1 and P2 formulations with tractable solution approaches, and extensive simulations showing energy-delay improvements over baselines across varying traffic densities, bandwidths, and task granularities. The framework offers a practical path to near-optimal sequential-task offloading in dynamic vehicular MEC environments, with significant implications for energy efficiency and latency of perception and other compute-heavy onboard applications.

Abstract

Nowadays, the convergence of Mobile Edge Computing (MEC) and vehicular networks has emerged as a vital facilitator for the ever-increasing intelligent onboard applications. This paper introduces a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores two tiers of collaboration. In the vehicle tier, we design a needing vehicle (NV)-helping vehicle (HV) matching scheme and inter-vehicle collaborative computation is studied, with joint optimization of task offloading decision, communication, and computation resource allocation to minimize energy consumption and meet latency requirements. In the roadside unit (RSU) tier, collaboration among RSUs is investigated to address multi-access issues of bandwidth and computation resources for multiple vehicles. A two-step method is proposed to solve the subchannel allocation problem. Detailed experiments are conducted to demonstrate the effectiveness of the proposed method and assess the impact of different parameters on system energy consumption.

Sequential Task Assignment and Resource Allocation in V2X-Enabled Mobile Edge Computing

TL;DR

The paper tackles energy-efficient execution of sequential-subtask applications in MEC-enabled V2X networks by introducing a two-tier offloading framework: first, NV-HV matching within the Vehicle Tier to maximize local collaboration, and second, RSU-tier coordination for unmatched NVs. It develops convex optimization-based solutions (via KKT conditions and bisection) for the Vehicle Tier and a convex-then-adjacent-integer strategy for the RSU Tier, complemented by a two-step subchannel allocation method to approach continuous optimality. The key contributions are the NV-HV matching algorithm, the P1 and P2 formulations with tractable solution approaches, and extensive simulations showing energy-delay improvements over baselines across varying traffic densities, bandwidths, and task granularities. The framework offers a practical path to near-optimal sequential-task offloading in dynamic vehicular MEC environments, with significant implications for energy efficiency and latency of perception and other compute-heavy onboard applications.

Abstract

Nowadays, the convergence of Mobile Edge Computing (MEC) and vehicular networks has emerged as a vital facilitator for the ever-increasing intelligent onboard applications. This paper introduces a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores two tiers of collaboration. In the vehicle tier, we design a needing vehicle (NV)-helping vehicle (HV) matching scheme and inter-vehicle collaborative computation is studied, with joint optimization of task offloading decision, communication, and computation resource allocation to minimize energy consumption and meet latency requirements. In the roadside unit (RSU) tier, collaboration among RSUs is investigated to address multi-access issues of bandwidth and computation resources for multiple vehicles. A two-step method is proposed to solve the subchannel allocation problem. Detailed experiments are conducted to demonstrate the effectiveness of the proposed method and assess the impact of different parameters on system energy consumption.

Paper Structure

This paper contains 19 sections, 27 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. System Overview
  4. Application Task Model
  5. Communication Model
  6. Computation Model
  7. Tier-1: Collaborative Computation Between NV and HV in the Vehicle Tier
  8. NV-HV Matching
  9. Problem Formulation of Vehicle Tier
  10. Problem Solving
  11. Complexity Analysis
  12. Tier-2: Collaborative Computation in the RSU Tier
  13. Problem Formulation of RSU Tier
  14. Solutions to Allocation Policies of Communication and Computation Resources
  15. Solutions to Subchannel Allocation and Offloading Decisions
  16. ...and 4 more sections

Figures (9)

  • Figure 1: The system model of the two-tier MEC-enabled vehicular network.
  • Figure 2: Application task model.
  • Figure 3: Average energy consumption for different methods under varying task delay requirement.
  • Figure 4: Average energy consumption with varying subtask structure.
  • Figure 5: Impact of V2V bandwidth and distance on average energy consumption.
  • ...and 4 more figures