Capacity Planning for Vehicular Fog Computing

Abstract

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. Fog computing shortens the network latency by moving computation close to the location where the data is generated. Vehicular fog computing (VFC) proposes to complement stationary fog nodes co-located with cellular base stations (i.e., CFNs) with mobile ones carried by vehicles (i.e., VFNs) in a cost-efficient way. Previous works on VFC have mainly focused on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, the uncertainty in the computational demand, and the trade-off between the quality of service (QoS) and cost expenditure.

This dissertation focuses on capacity planning for VFC. The objective of capacity planning is to maximize the techno-economic performance of VFC in terms of profit and QoS. To address the spatial-temporal dynamics of vehicular traffic, this dissertation presents a capacity planning solution for VFC that jointly decide the location and number of CFNs together with the route and schedule of VFNs carried by buses. Such a long-term planning solution is supposed to be updated seasonally according to the traffic pattern and bus timetables. To address the uncertainty in the computational resource demand, this dissertation presents two capacity planning solutions for VFC that dynamically schedule the routes of VFNs carried by taxis in an on-demand manner. Such a short-term planning solution is supposed to be updated within minutes or even seconds. To evaluate the techno-economic performance of our capacity planning solutions, an open-source simulator was developed that takes real-world data as inputs and simulates the VFC scenarios in urban environments. The results of this dissertation can contribute to the development of edge and fog computing, the Internet of Vehicles (IoV), and intelligent transportation systems (ITS).