Vehicular Cloud Computing Systems Research Articles

A SMDP Approach to Evaluate the Performance of a Vehicular Cloud Computing System with Prioritize Requests

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.

FRAC: a flexible resource allocation for vehicular cloud system

Vehicular cloud computing (VCC) is a promising technology for the intelligent transport system. It provides a better quality of transport and vehicular services that will increase the safety and comfort of drivers and passengers. A group of vehicles creates VCC and offers vehicular services for its users in the absence of infrastructure to support the system. The vehicles having computing, storage, communication, and sensing devices can share these resources with other vehicles. VCC creates a resource pool by aggregating all the shared resources of nearby vehicles. The major challenge for VCC is to manage the resources from different vehicles in a resource pool and to allocate necessary resources to its user on-demand. The authors propose a semi-Markov decision process based resource allocation method for the VCC system called flexible resource allocation for vehicular cloud system to manage and allocate the resources. The proposed method finds optimal resource allocation strategies for different states of the VCC system and maximises the long-term expected reward under different parameter settings.

Cooperative Task Scheduling for Computation Offloading in Vehicular Cloud

Technological evolutions in the automobile industry, especially the development of connected and autonomous vehicles, have granted vehicles more computing, storage, and sensing resources. The necessity of efficient utilization of these resources leads to the vision of vehicular cloud computing (VCC), which can offload the computing tasks from the edge or remote cloud to enhance the overall efficiency. In this paper, we study the problem of computation offloading through the vehicular cloud (VC), where computing missions from edge cloud can be offloaded and executed cooperatively by vehicles in VC. Specifically, computing missions are further divided into computing tasks with interdependency and executed in different vehicles in the VC to minimize the overall response time. To characterize the instability of computing resources resulting from the high vehicular mobility, a mobility model focusing on vehicular dwell time is utilized. Considering the heterogeneity of vehicular computing capabilities and the interdependency of computing tasks, we formulate an optimization problem for task scheduling, which is NP-hard. For low complexity, a modified genetic algorithm based scheduling scheme is designed where integer coding is used rather than binary coding, and relatives are defined and employed to avoid infeasible solutions. In addition, a task load based stability analysis of the VCC system is presented for the cases where some vehicles within the VC are offline. Numerical results demonstrate that the proposed scheme can significantly improve the utilization of computing resources while guaranteeing low latency and system stability.

Resource Allocation in Vehicular Cloud Computing Systems With Heterogeneous Vehicles and Roadside Units

Vehicular cloud computing (VCC) system coordinates the vehicular cloud (consisting of vehicles' computing resources) and the remote cloud properly to provide in-time services to users. Although pervious works had established the models for resource allocation in the VCC system based on semi-Markov decision processes (SMDPs), few of them discussed heterogeneity of vehicles and influences of roadside units (RSUs). Heterogeneous vehicles made by different manufacturers may be equipped with different amount of computing resources; and furthermore, RSU can enhance the computing capability of VCC. Therefore, this paper creates an SMDP model for VCC resource allocation that additionally considers heterogeneous vehicles and RSUs, and proposes an approach for finding the optimal strategy of VCC resource allocation. The two additional features significantly elaborate the SMDP model, and demonstrate different results from the original model. Simulation shows that the resource allocation in the VCC system can be captured by the proposed model, which performs well in terms of long-term expected values (consisting of consumption costs of power and time), under various parameter settings.

Task Replication for Deadline-Constrained Vehicular Cloud Computing: Optimal Policy, Performance Analysis, and Implications on Road Traffic

In vehicular cloud computing (VCC) systems, the computational resources of moving vehicles are exploited and managed by infrastructures, e.g., roadside units, to provide computational services. The offloading of computational tasks and collection of results rely on successful transmissions between vehicles and infrastructures during encounters. In this paper, we investigate how to provide timely computational services in VCC systems. In particular, we seek to minimize the deadline violation probability given a set of tasks to be executed in vehicular clouds. Due to the uncertainty of vehicle movements, the task replication methodology is leveraged which allows one task to be executed by several vehicles, and thus trading computational resources for delay reduction. The optimal task replication policy is of key interest. We first formulate the problem as a finite-horizon sampled-time Markov decision problem and obtain the optimal policy by value iterations. To conquer the complexity issue, we propose the balanced-task-assignment (BETA) policy which is proved optimal and has a clear structure: it always assigns the task with the minimum number of replicas. Moreover, a tight closed-form performance upper bound for the BETA policy is derived, which indicates that the deadline violation probability follows the Rayleigh distribution approximately. Applying the vehicle speed-density relationship in the traffic flow theory, we find that vehicle mobility benefits VCC systems more compared with road traffic systems, by showing that the optimum vehicle speed to minimize the deadline violation probability is larger than the critical vehicle speed in traffic theory which maximizes traffic flow efficiency.

An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems

Vehicular ad hoc networks are expected to significantly improve traffic safety and transportation efficiency while providing a comfortable driving experience. However, available communication, storage, and computation resources of the connected vehicles are not well utilized to meet the service requirements of intelligent transportation systems. Vehicular cloud computing (VCC) is a promising approach that makes use of the advantages of cloud computing and applies them to vehicular networks. In this paper, we propose an optimal computation resource allocation scheme to maximize the total long-term expected reward of the VCC system. The system reward is derived by taking into account both the income and cost of the VCC system as well as the variability feature of available resources. Then, the optimization problem is formulated as an infinite horizon semi-Markov decision process (SMDP) with the defined state space, action space, reward model, and transition probability distribution of the VCC system. We utilize the iteration algorithm to develop the optimal scheme that describes which action has to be taken under a certain state. Numerical results demonstrate that the significant performance gain can be obtained by the SMDP-based scheme within the acceptable complexity.