Abstract
In intelligent transportation systems, Vehicular Cloud Computing (VCC) is a new technology that can help ensure road security and transport efficiency. The study and evaluation of performances of a VCC is a topic of crucial interest in these environments. This paper presents a model of the computation resource allocation problem in VCC by considering heterogeneity and priority of service requests. We consider service requests from two classes, Primary service requests and Secondary service requests. We involve a Semi-Markov Decision Process (SMDP) to achieve the optimal policy that maximizes the performances of the VCC system taking into account the variability of resources, the income and the system cost. We utilize an iterative approach to achieve the optimal scheme that characterizes the action to be taken under each state. We validate our study by numerical results that show the effectiveness of the proposed SMDP-based scheme.
Highlights
In the last years, the Intelligent transportation system (ITS) involves the Vehicular Ad-Hoc Network (VANET) to facilitate data exchange among vehicles
Hussain et al [3] presented a potential architecture structure for different cloud scenarios in VANETs that divided into three frameworks named Vehicular Clouds VC, Hybrid Vehicular Clouds (HVC) and Vehicles using Clouds (VuC)
Two types of service requests are considered with different priority in the Vehicular Cloud Computing (VCC) system based on the Semi-Markov infinite horizon decision process Semi-Markov Decision Process (SMDP)
Summary
The Intelligent transportation system (ITS) involves the Vehicular Ad-Hoc Network (VANET) to facilitate data exchange among vehicles. We propose a resource allocation scheme for the VCC system with priority requests. This work assumes that priority of service requests provides a different amount of computing resources with different distributions of probability. The main assumptions for the sake of analysis are presented as follows: The service request arrivals and departures per vehicle are distributed according to the Poisson distribution as well as for the service requests; The number of RUs in VC alters over time; The future decision is affected by the current action.
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