UniDRM: Unified Data and Resource Management for Federated Vehicular Cloud Computing

Danquah W. M., Altılar D. T.

IEEE ACCESS, vol.9, pp.157052-157067, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2021
  • Doi Number: 10.1109/access.2021.3127521
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.157052-157067
  • Keywords: Cloud computing, Task analysis, Scheduling, Processor scheduling, Resource management, Roads, Peer-to-peer computing, Vehicular cloud computing, federated vehicular cloud, resource management, resource-based clustering, resource ranking, divisible load partitioning, divisible load scheduling, CHALLENGES, MAPREDUCE, FRAMEWORK
  • Istanbul Technical University Affiliated: Yes


The demand for computational resources in vehicular environments has increased due to the deployment of numerous intelligent transportation systems in the last decade. The federated vehicular cloud, a variant of vehicular cloud computing where resources embedded in individual vehicles are organized as a single unit to provide cloud services, is considered as an emerging alternative to the conventional cloud platforms for the execution of computationally intensive and delay-sensitive applications. However, the federated vehicular cloud is beset with a capacity-constrained communication channel and limited resource capacity in individual vehicles, leading to challenges in data and resource management. To address these challenges, we propose UniDRM, a unified data and resource management framework for the federated vehicular cloud. The UniDRM organizes vehicles on the road into clusters based on their mobility and resource characteristics, such as resource cost, resource credibility level, resource type, and available resource capacity. The data of computationally intensive tasks are then partitioned using our proposed analytical model and assigned to individual vehicles in the cluster for parallel execution. Three data partitioning and scheduling schemes: time-aware, cost-aware, and reliability-aware, are proposed in this study to execute time-critical tasks, low-cost tasks, and high-security tasks, respectively. Through realistic simulations, a comparative analysis of the proposed partitioning and scheduling schemes is presented.