Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks

Abstract

Due to limited computation resources of a vehicle terminal, it is impossible to meet the demands of some applications and services, especially for computation-intensive types, which not only results in computation burden and delay, but also consumes more energy. Mobile edge computing (MEC) is an emerging architecture in which computation and storage services are extended to the edge of a network, which is an advanced technology to support multiple applications and services that requires ultra-low latency. In this paper, a task offloading approach for an MEC-assisted vehicle platooning is proposed, where the Lyapunov optimization algorithm is employed to solve the optimization problem under the condition of stability of task queues. The proposed approach dynamically adjusts the offloading decisions for all tasks according to data parameters of current task, and judge whether it is executed locally, in other platooning member or at an MEC server. The simulation results show that the proposed algorithm can effectively reduce energy consumption of task execution and greatly improve the offloading efficiency compared with the shortest queue waiting time algorithm and the full offloading to an MEC algorithm.

Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

“…The authors proposed to optimize task offloading and wireless frequency allocation simultaneously. In a study to reduce task execution time and energy consumption, the authors proposed an approximation of offloading decisions based on the alternating direction method of multipliers (ADMM) [22] and Lyapunov optimization [23].…”

A context-oriented framework for computation offloading in vehicular edge computing using WAVE and 5G networks

“…For 5G/V2I communications, the authors in [41,42,43] show that the data transmission rate between a node i and a node j in a given time t is given by:…”

“…where B V 2I represents the bandwidth between i and j, d i,j is the distance between i and j and the factor is the exponent of propagation loss of the system. Due to the fast transmission rates on the wired link and the coexisting deployment between the base station and the edge server, the transmission delay of the wired link is neglected in this work [43].…”

Energy Efficient Virtual Machines Placement Over Cloud-Fog Network Architecture

“…This resulted in up to 65% savings in energy in the fog layer. In [5], the authors investigate a Lyapunov optimization technique, that derives a greedy algorithm and a joint algorithm, which maps requests, in a network queue, to the amount of energy required to fulfill the requests and investigates whether to dispatch requests one by one or all at once. In [6], the authors investigated the energy efficiency of three different packet routing based objective functions, Objective Function Zero (OF0), Advanced Objective Function Zero (AF0), and Minimum Rank with Hysteresis Objective Function (MRHOF) for Routing Protocol for Low Power and Lossy Networks (RPL) in the fog layer.…”