Energy Efficient Processing Allocation in Opportunistic Cloud-Fog-Vehicular Edge Cloud Architectures

TL;DR

This paper proposes an energy-efficient task allocation model in Vehicular Edge Cloud architectures, demonstrating up to 70% power savings by leveraging distributed processing and task splitting among vehicles.

Contribution

It develops a MILP model for optimizing processing task allocation across cloud, fog, and vehicular nodes considering multiple processing dimensions.

Findings

Up to 70% power savings by vehicle-based processing.

Power savings depend on vehicle density, processing capacity, and workload.

Task splitting enhances energy efficiency.

Abstract

This paper investigates distributed processing in Vehicular Edge Cloud (VECs), where a group of vehicles in a car park, at a charging station or at a road traffic intersection, cluster and form a temporary vehicular cloud by combining their computational resources in the cluster. We investigated the problem of energy efficient processing task allocation in VEC by developing a Mixed Integer Linear Programming (MILP) model to minimize power consumption by optimizing the allocation of different processing tasks to the available network resources, cloud resources, fog resources and vehicular processing nodes resources. Three dimensions of processing allocation were investigated. The first dimension compared centralized processing (in the central cloud) to distributed processing (in the multi-layer fog nodes). The second dimension introduced opportunistic processing in the vehicular nodes with low and high vehicular node density. The third dimension considered non-splittable tasks (single allocation) versus splittable tasks (distributed allocation), representing real-time versus non real-time applications respectively. The results revealed that a power savings up to 70% can be achieved by allocating processing to the vehicles. However, many factors have an impact on the power saving such the vehicle processing capacities, vehicles density, workload size, and the number of generated tasks. It was observed that the power saving is improved by exploiting the flexibility offered by task splitting among the available vehicles.