Benjamin Baron

Virtualizing vehicular node resources

With emerging geo-distributed services, there is a need to coordinate the use of resources offered by field-area networks. In the case of vehicular networks, such resources include the processing, sensing, and storage capabilities offered to service providers for urban sensing or intelligent transportation. In this paper, we propose to virtualize the resources embedded on the vehicular nodes to allow multiple tenants to coexist and deploy their services on the same underlying mobile substrate. Virtualization is the task of an infrastructure provider that controls the mobile substrate and allocates sliced resources to the tenants. A service results from a collection of virtual machines hosted on the mobile nodes allocated by the infrastructure provider. Efficient utilization of the node resources may trigger virtual machine migrations. We study the problem of virtual machine migrations through V2V communications between mobile nodes. To evaluate the impact of such migrations on the resource allocation process, we use the real traces of a bus transit system to simulate a vehicular network where virtual machines migrate via V2V communications. Our results show that virtual machines of several hundreds of Megabytes can migrate between moving buses. We then discuss design principles and research issues toward the full virtualization of opportunistic networks.

Software-defined vehicular backhaul

The network of roads and highways is a promising candidate to help network operators offload their infrastructure and cope with the ever-growing amount of data exchanged on the Internet. By piggybacking data onto vehicles, roads can be turned into a large-capacity transmission system when considering the increasing number of journeys involving vehicles. The data to be transferred is opportunistically loaded on or off the vehicles at specific locations referred to as offloading spots. Two of the main challenges of such a system are how to assign the road paths matching the data transfer requirements and how much data to allocate to each flow of vehicles. We propose a centralized SDN-like architecture consisting of a central controller acting as a service broker and the offloading spots as SDN agents. The controller computes the road paths that accommodate the data transfer requirements and installs the corresponding forwarding states at each offloading spot along those paths. We describe our SDN-controlled offloading system and evaluate its performance using road traffic counts from France. Our numerical results show that the controller can achieve efficient and fair allocation of multiple data transfers between major cities of France. Each transfer successfully delivers over 10 PB of data within a week when considering that 10% of vehicles on the road are equipped with 1TB of storage.

Offloading Massive Data Onto Passenger Vehicles: Topology Simplification and Traffic Assignment

Offloading is a promising technique for alleviating the ever-growing traffic load from infrastructure-based networks such as the Internet. Offloading consists of using alternative methods of transmission as a cost-effective solution for network operators to extend their transport capacity. In this paper, we advocate the use of conventional vehicles equipped with storage devices as data carriers whilst being driven for daily routine journeys. The road network can be turned into a large-capacity transmission system to offload bulk transfers of delay-tolerant data from the Internet. One of the challenges we address is assigning data to flows of vehicles while coping with the complexity of the road network. We propose an embedding algorithm that computes an offloading overlay where each logical link spans over multiple stretches of road from the underlying road infrastructure. We then formulate the data transfer assignment problem as a novel linear programming model we solve to determine the optimal logical paths matching the performance requirements of a data transfer. We evaluate our road traffic allocation scheme using actual road traffic counts in France. The numerical results show that 20% of vehicles in circulation in France equipped with only one Terabyte of storage can offload Petabyte transfers in a week.

Vehicles as Big Data Carriers: Road Map Space Reduction and Efficient Data Assignment

We advocate the use of a data shuttle service model to offload bulk transfers of delay-tolerant data from the Internet onto standard vehicles equipped with data storage capabilities. We first propose an embedding algorithm that computes an offloading overlay on top of the road infrastructure. The goal is to simplify the representation of the road infrastructure as raw maps are too complex to handle. In this overlay, each logical link maps multiple stretches of road from the underlying road infrastructure. We formulate then the data transfer assignment problem as a novel linear programming model that determines the most appropriate logical paths in the offloading overlay for a data transfer request. We evaluate our proposal using actual road traffic counts in France. Numerical results show that we can satisfy weekly aggregate requests in the petabyte range while achieving cumulative bandwidth above 10 Gbps with a market share of 20% and only one terabyte of storage per vehicle.

Centrally Controlled Mass Data Offloading Using Vehicular Traffic

With over 300 billion vehicle trips made in the United States and 64 billion in France per year, network operators have the opportunity to utilize the existing road and highway network as an alternative data network to offload large amounts of delay-tolerant traffic. To enable the road network as a large-capacity transmission system, we exploit the existing mobility of vehicles equipped with wireless and storage capacities together with a collection of offloading spots. An offloading spot is a data storage equipment located where vehicles usually park. Data is transloaded from a conventional data network to the closest offloading spot and then shipped by vehicles along their line of travel. The subsequent offloading spots act as data relay boxes where vehicles can drop off data for later pick-up by other vehicles, depending on their direction of travel. The main challenges of this offloading system are how to compute the road path matching the performance requirements of a data transfer and how to configure the sequence of offloading spots involved in the transfer. We propose a scalable and adaptive centralized architecture built on software-defined networking that maximizes the utilization of the flow of vehicles connecting consecutive offloading spots. We simulate the performance of our system using real roads traffic counts for France. Results show that the centralized controlled offloading architecture can achieve an efficient and fair allocation of concurrent data transfers between major cities in France.

Cloud storage for mobile users using pre-positioned storage facilities

We propose a cloud-like file storage and sharing system designed for mobile users. Our system relies on a collection of strategically pre-positioned repositories within which files are replicated without relying on a conventional infrastructure-based network. Once stored in the first encountered repository, copies of the files are carried by the initial uploader or subsequent users and distributed among the other repositories. Having multiple copies available at different repositories thus increases the likelihood of finding the requested files in a timely fashion. Files can later be retrieved by other users at different locations. We are interested in processing user storage and retrieval requests before their deadlines expire. We design an algorithm to place the repositories such that they serve a maximum number of requests before their deadlines expire. We evaluate our system using mobility traces of San Francisco city buses. We show the impact of the number and placement of repositories on request success rate. We also show the benefits of mobility-leveraged file distribution.