Dependency-Aware Task Offloading and Service Caching in Vehicular Edge Computing

With the emergence of computation-intensive vehicular applications, vehicular edge computing (VEC) offers a new paradigm to augment the capabilities of vehicles. In this article, we study the problem of dependency-aware task offloading and service caching in VEC, where each application can be divide...

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Bibliographic Details
Published in:	IEEE transactions on vehicular technology 2022-12, Vol.71 (12), p.13182-13197
Main Authors:	Shen, Qiaoqiao, Hu, Bin-Jie, Xia, Enjun
Format:	Article
Language:	eng
Subjects:	Algorithms Caching Computation offloading Computational modeling Delays Dynamic programming Edge computing Efficiency Energy consumption Linear programming Mixed integer Nonlinear programming Offloading efficiency Optimization Roadsides Scheduling Servers service caching software-defined network (SDN) Software-defined networking Task analysis task dependency task offloading vehicular edge computing (VEC)
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Summary:	With the emergence of computation-intensive vehicular applications, vehicular edge computing (VEC) offers a new paradigm to augment the capabilities of vehicles. In this article, we study the problem of dependency-aware task offloading and service caching in VEC, where each application can be divided into multiple tasks with task dependency, and vehicles can access the software-defined network (SDN) via roadside units (RSUs) to request edge servers to assist in processing tasks. Edge servers can selectively cache executed services for reuse by subsequent tasks to improve the offloading efficiency of the system. The offloading efficiency is defined as a weighted sum of the computation time of the task and the energy drained from the respective vehicle. To maximize the offloading efficiency, we formulate the tasks offloading and service caching problem as a mixed-integer non-linear programming (MINLP) problem and develop a semi-distributed algorithm based on dynamic programming to solve the optimization problem. Simulation results demonstrate that the proposed algorithm has higher offloading efficiency and a higher completion rate under application deadlines compared with similar offloading algorithms.
ISSN:	0018-9545 1939-9359