Optimized Distributed Processing in a Vehicular Cloud Architecture

Abstract

The introduction of cloud data centres has opened new possibilities for the storage and processing of data, augmenting the limited capabilities of peripheral devices. Large data centres tend to be located away from the end users, which increases latency and power consumption in the interconnecting networks. These limitations led to the introduction of edge processing where small-distributed data centres or fog units are located at the edge of the network close to the end user. Vehicles can have substantial processing capabilities, often unused , in their onboard-units (OBUs). These can be used to augment the network edge processing capabilities. In this paper, we extend our previous work and develop a mixed integer linear programming (MILP) formulation that optimizes the allocation of networking and processing resources to minimize power consumption. Our edge processing architecture includes vehicular processing nodes, edge processing and cloud infrastructure. Furthermore, in this paper our optimization formulation includes delay. Compared to power minimization, our new formulation reduces delay significantly, while resulting in a very limited increase in power consumption.

A Software-Defined Networking Roadside Unit Cloud Resource Management Framework for Vehicle Ad Hoc Networks

“…On the one hand, a cloud of on-board units (OBUs) are playing vital roles in the VANETs, where some RSUs still act as the gateways to the traditional communication networks [9]. Behbehani et al [10] introduce a vehicular cloud architecture supported by fixed edge computing nodes and the central cloud; mixed-integer linear programming (MLP) model is developed to optimize the allocation of the computing demands in the distributed architecture while minimizing power consumption. Nodes that are consisted of "SmartCloud" vehicles in the minimum connected dominating set form a virtual backbone in the VANETs [11], where a "SmartCloud" vehicle sends periodic beacon messages to the nearest RSU.…”

“…Constraints (8) and (9) ensure that the routes carrying the network loads are only between the RSU servers and the users. To step further, (10) and (11) ensure that there are enough loads for carrying the rule of the control plane which is the natural cost of the system itself.…”

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

“…In Ref. [13], a mixed integer linear programming (MLP) model is proposed to best manage the distribution of computational demands in the distributed architecture while decreasing energy consumption.…”