Latency-aware placement of vehicular metaverses using virtual network functions

Document Type

Article

Publication Title

Simulation Modelling Practice and Theory

Abstract

Recent unprecedented trend towards novel vehicular network applications (e.g., lane change assistance, collision avoidance, accident reporting, and infotainment) led to research activities towards the novel design of vehicular networks. Such a novel design can leverage metaverse architecture among various possible schemes. However, enabling the metaverse for a vehicular network requires careful placement of meta spaces (i.e., virtual network functions based on virtual machines running a virtual model of the actual network) at the network edge. Furthermore, virtual network functions will use additional entities (e.g., memory storage) to better implement meta spaces. Therefore, in this work, we consider the efficient placement of meta spaces at the network edge. We formulate a cost function that accounts for latency (i.e., meta space computing latency and transmission latency for meta spaces signaling). To minimize this cost, an optimization problem is formulated that uses three variables: (a) meta spaces (i.e., based on virtual machines) operating frequency, (b) meta space placement, and (c) wireless resource allocation. A decomposition-based solution is used to solve the formulated problem due to its difficult nature. Finally, numerical results are provided and the paper is concluded.

DOI

10.1016/j.simpat.2024.102899

Publication Date

5-1-2024

Keywords

Machine learning, Matching theory, Metaverse, Vehicular networks

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