Data-Driven 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. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities (a.k.a fog nodes). In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus 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, varying computing resource demand generated by vehicular applications, and the mobility of fog nodes. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using realworld traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile fog nodes and create more savings in operational costs in the long term.

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks

“…However, due to the highly decentralized and mobile nature of fog nodes and context-aware computational demands of vehicular applications, the facility location in VFC is a complex task [ 6 ]. Here, the FLP can be partitioned into three sub-problems.…”

“…Considering the mobility of VANET infrastructures, the facility location and capacity planning in VFC recently received considerable attention. For example, in some papers, the VFC is studied from the viewpoint of delay optimization [ 9 ], energy optimization [ 32 ], utility maximization [ 33 ], cost minimization [ 6 ], etc.…”

“…The model is further solved using a hybrid simulated annealing algorithm, evaluated on a 5G desktop application leveraged with network function virtualization (NFV). A cost efficient data-driven VFC framework was proposed in a very recent work [ 6 ] where authors used both heuristic and integer linear programming (ILP) to solve the data-driven capacity planning problem. The objective is to minimize installation and operational expenses considering spatio-temporal variations in vehicular traffic and QoS constraints.…”

“…Moreover, the presence of unstable connections in multi-user CAV environments degrades the overall quality of experience. For instance, many ITS applications need interactivity requiring a low response time and service delay for providing instant feedback [ 6 ]. In such scenarios, the control nodes must provide accurate signals for the steering system in milliseconds.…”

Verifiable and privacy-preserving fine-grained data management in vehicular fog computing: A game theory-based approach

“…Consequently, some IoV applications may stop and decrease the performance of vehicle systems. As a solution to this problem, vehicular fog computing(VFC) that uses vehicles as mobile fog nodes to meet dynamic user needs can be an alternative to support fog computing for vehicular network-based applications [5,6].…”