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Framework Design and Job Scheduling for Vehicular Cloud Computing

Vehicles nowadays are not just tools transporting people from one place to another. Drivers or passengers usually demand more features to be provided on the road, like driving assistance, multimedia services and more. The inherent problem is how to meet this ever-increasing computational demand in vehicles. Usually, more features mean more hardware to be installed, along with higher cost. However, the emergence of cloud computing and edge computing technologies change this. By using a pool of resource to provide flexible services through network, users are freed from upgrading and maintaining hardware. How to apply such technologies in vehicular environment becomes a problem. At first, we proposed a framework to allow vehicles pool their computation resources and help each other in processing. Different from the existing solutions, this framework does not rely on deployed infrastructures, which are not available everywhere at the moment, but utilizes the new DSRC standard and a decentralized algorithm to distribute computation tasks. It can be the fundamental of a more generic and scalable vehicular cloud computing framework. In the framework, the software system is divided into 3 main components: application, backend and manager. And then we propose a mechanism to cache multiple jobs for optimal assignment and an algorithm to solve the assignment problem. The framework also takes the predictable movement pattern in vehicular environment into account. A new Ant Colony Optimization (ACO) based algorithm, with a polynomial time complexity, is designed. Preliminary evaluation shows the superiority of the algorithm comparing to brute force searching and random assignment. Evaluation environment is built with Omnet++ network simulator, Simulation of Urban MObility (SUMO) traffic simulator and Veins simulation framework. Then realistic traffic scenario, Luxembourg SUMO Traffic scenario(LuST), with 24 hours traffic from real-world traffic demand data, in the Luxembourg City, Luxembourg is used. The evaluation is done on both the urban part and highway part of this scenario, each with a 15 minutes time span. Results from repeated experiment indicate the proposed framework outperforms competing schemes in both environments, and achieving more than 90% optimal assigning solutions. And then, we consider the scenario with already deployed infrastructures, like roadside units and cellular base stations. Hence the new framework, Hybrid Vehicular Cloud (HVC) is designed. HVC aims at the utilization of edge resources, like roadside units and idle neighbor vehicles, as augmentation of the remote cloud to provide computational services. With DSRC connections to roadside units and other vehicles, HVC also reduces communication costs in offloading with cellular network. To better utilizes the dynamic resources on road, we use the “available time” concept to model the connection status change with movements of vehicles. A handshaking procedure is introduced to eliminate offloading failure due to volatile connections in vehicular network. An online assignment algorithm is proposed to assign jobs in a heuristic way that as many as possible jobs are guaranteed to be finished in time, while still leaving time slots for jobs arriving in future. Simulation is done with the aforementioned LuST scenario. Two roadside units are set in the traffic map and an ideal assumption on cellular network is made. It is found that the new proposal achieves 100% job finishing ratio in almost all settings, while reducing more than 50% cellular traffics in most cases. In addition to the two proposed framework, we then discuss the extensions to them and alternative application scenarios. We also make some anticipations on the future development in this field.