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QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing.

Chenhong Cao1,2, Meijia Su1,2, Shengyu Duan1,2

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

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Summary

This study introduces a novel vehicular edge computing (VEC) framework that optimizes task offloading for reduced latency and energy use. The proposed method enhances Quality of Service (QoS) by coordinating multiple roadside units (RSUs) and considering vehicle speed.

Keywords:
computation offloadingmulti-objective optimizationresource allocationvehicular edge computing

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Area of Science:

  • Vehicular edge computing (VEC)
  • Internet of Vehicles (IoV)
  • Mobile computing

Background:

  • Vehicular edge computing (VEC) offloads tasks to roadside units (RSUs) to reduce vehicle processing delay and resource consumption.
  • Existing VEC solutions often overlook multi-RSU coordination and application-specific Quality of Service (QoS) needs, leading to suboptimal performance.
  • Achieving both low latency and low energy consumption is crucial for ideal VEC computation offloading policies.

Purpose of the Study:

  • To present FEVEC, a Fast and Energy-efficient VEC framework designed for optimal offloading strategies.
  • To minimize both delay and energy consumption in VEC systems.
  • To coordinate multiple RSUs and incorporate application-specific QoS requirements for enhanced performance.

Main Methods:

  • Formalized the computation offloading problem as a multi-objective optimization problem (a mixed-integer nonlinear programming problem, NP-hard).
  • Proposed MOV, a Multi-Objective computing offloading method for VEC.
  • Implemented vehicle prejudgment based on vehicle speed and maximum tolerance delay.
  • Utilized an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for Pareto-optimal solutions.
  • Selected the optimal offloading strategy for QoS maximization.

Main Results:

  • The proposed MOV method effectively coordinates multiple RSUs and considers application-specific QoS requirements.
  • FEVEC framework achieves minimization of both delay and energy consumption through optimized offloading decisions and resource allocation.
  • Extensive evaluations using real and simulated vehicle trajectories demonstrated significant improvements.
  • The average QoS value was improved by 20% compared to state-of-the-art VEC mechanisms.

Conclusions:

  • The FEVEC framework and MOV method offer a superior approach to VEC computation offloading.
  • The coordinated multi-RSU strategy effectively addresses the limitations of previous VEC systems.
  • The proposed solution significantly enhances overall system performance and user experience in the Internet of Vehicles (IoV).