Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks

By Yueyue Dai, Du Xu, Sabita Maharjan, Yan Zhang. IEEE Internet of Things Journal, 2019

403 citations recorded by the source corpus.

The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes, where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multiuser multiserver VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer nonlinear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading algorithm compared to the benchmark solutions.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/bc11a01d61b5c217b90a2d575f8d07a42984c099

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Citations in the corpus

Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution (2020, background): “[27] investigated the task offloading problem in VECNs with multi-users and multi-servers and formulated it as a mixed inter non-linear programming problem.”
Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks (2019, background): “Some novel MEC-based vehicular networks are proposed in [25], [26], in which the computation ofﬂoading policies are carefully designed according to different scenarios.”
Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks (2019, methodology): “Moreover, similar to [33], the wireless technology in the vehicular environment is widely used in the contention based on the IEEE 802.”
Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond (2019, background): “The data size of each content is randomly taken from [3, 6, 9] GB.”
Blockchain for Internet of Things: A Survey (2019, methodology): “of [66] presents an offloading method with consideration of load balancing among multiple MEC servers.”
Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles (2020, background): “Integrating MEC in vehicular networks, the authors in [26] studied the resource allocation problem by combining load balance with offloading for a multi-”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “Load balancing among edge servers when designing the offloading decision was investigated in [96].”
Vehicular Edge Computing and Networking: A Survey (2019, background): “The authors of [59] investigate resource allocation by jointly considering load balancing and offloading in a multi-user multi-server vehicular environment.”
Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges (2020, background): “The lower the equipment performance, the stronger the requirement for effective load balancing, such as in the MEC scenario [119]– [121].”