Optimized Processing Placement Over a Vehicular Cloud

Abstract

Modern vehicles equipped with on-board units (OBU) are playing an essential role in the emerging smart city revolution. The vehicular processing resources, however, are not used to their full potential. The concept of vehicular clouds is proposed to exploit the underutilized vehicular resources to supplement cloud computing services to relieve the burden on centralized cloud data centers and improve quality of service. In this paper we introduce a vehicular cloud architecture supported by fixed edge computing nodes and a central cloud data center. A mixed integer linear programming (MILP) model is developed to optimize the allocation of the processing demands in the distributed architecture while minimizing the overall power consumption. The results show power savings as high as 84% compared to processing in the conventional cloud.Variations in the test cases to include processing demand and traffic demand splitting showed power saving of 71% and 16% respectively, even for large demand volumes. A heuristic algorithm with performance approaching that of the MILP model is developed to validate the MILP model and allocate processing demands in real time.

Keywords

• Vehicular clouds
• edge computing
• fog
• power optimization
• distributed processing
• MILP