Framework of Cloudcomputing Resource Scheduling Vehicle Fault Diagnosis

Authors- Mr Keerthivasan L P, Assistant Professor Dr Kavitha P

Abstract--Internet of Vehicles (IoVs) provides communication and computing resources, which makes the on-board diagnosis of vehicle faults possible. However, those resources need to be expanded to support the accurate analysis of the on-board diagnosis. Vehicular Cloud Computing (VCC) can solve the pressure of local vehicle processing but will cause an unavoidable delay. Thus, the accuracy and timeliness of on-board diagnosis cannot be guaranteed. To address the issue, we propose a Mobile Edge Caching based Resource Scheduling (MECRS) mechanism for the on-board diagnosis of vehicle faults. According to the urgency of vehicle fault diagnosis, we first design a cloud scheduling algorithm to meet the computation requirements of both the essential business of IoVs and the fault diagnosis. Subsequently, the priority allocation strategy is made for all four types of requests The urgent requests can be processed timely then. Specifically, Theproposed method is a multi-objective optimization method for allocating communication and computing resources for the above requests. We also present a large scale file mobile edge caching alr algorithm where the large scale file is cached at the mobile edge. Offloading the cloud with high popularity takes advantage of it to relieve the pressure of the cloud. Finally, we carry out comprehensive simulations. Results show that the developed mechanism has a high service rate for on board The performances of the other three essential services are not compromised.

DOI: /10.61463/ijset.vol.13.issue2.432