A vehicular backbone network (VBN) has the potential to augment the Internet with high-throughput data flows for delay-tolerant traffic. High-throughput flows require a joint utilization of transportation capacity for carrying data packets through physical mobility and wireless capacity for switching data packets from one route to another. This paper establishes a model that incorporates both transportation mobility and wireless switching. Then, it characterizes the network capacity based on flow conservation, wireless communication capacity constraints and data storage limits, and solves a convex optimization that results in joint routing and congestion control. A variant with cost minimization reduces delay while maximizing throughput. Next, this paper develops a distributed algorithm that achieves the global objective with limited infrastructure support. Lastly, a packet-level simulation platform using real-world road map and traffic statistics is used to evaluate the distributed algorithm, and demonstrate the significant performance enhancement achieved.

In this paper, we address the problem of content placement in peer-to-peer (P2P) systems, with the objective of maximizing the utilization of peers' uplink bandwidth resources. We consider system performance under a many-user asymptotic. We distinguish two scenarios, namely “Distributed Server Networks” (DSNs) for which requests are exogenous to the system, and “Pure P2P Networks” (PP2PNs) for which requests emanate from the peers themselves. For both scenarios, we consider a loss network model of performance and determine asymptotically optimal content placement strategies in the case of a limited content catalog. We then turn to an alternative “large catalog” scaling where the catalog size scales with the peer population. Under this scaling, we establish that storage space per peer must necessarily grow unboundedly if bandwidth utilization is to be maximized. Relating the system performance to properties of a specific random graph model, we then identify a content placement strategy and a request acceptance policy that jointly maximize bandwidth utilization, provided storage space per peer grows unboundedly, although arbitrarily slowly, with system size.

Recently, it has been shown that carrier-sense multiple access (CSMA)-type random access algorithms can achieve the maximum possible throughput in ad hoc wireless networks. However, these algorithms assume an idealized continuous-time CSMA protocol where collisions can never occur. In addition, simulation results indicate that the delay performance of these algorithms can be quite bad. On the other hand, although some simple heuristics (such as greedy maximal scheduling) can yield much better delay performance for a large set of arrival rates, in general they may only achieve a fraction of the capacity region. In this paper, we propose a discrete-time version of the CSMA algorithm. Central to our results is a discrete-time distributed randomized algorithm that is based on a generalization of the so-called Glauber dynamics from statistical physics, where multiple links are allowed to update their states in a single timeslot. The algorithm generates collision-free transmission schedules while explicitly taking collisions into account during the control phase of the protocol, thus relaxing the perfect CSMA assumption. More importantly, the algorithm allows us to incorporate heuristics that lead to very good delay performance while retaining the throughput-optimality property.

In this paper, we address the problem of content placement in peer-to-peer systems, with the objective of maximizing the utilization of peers' uplink bandwidth resources. We consider system performance under a many-user asymptotic. We distinguish two scenarios, namely “Distributed Server Networks” (DSN) for which requests are exogenous to the system, and “Pure P2P Networks” (PP2PN) for which requests emanate from the peers themselves. For both scenarios, we consider a loss network model of performance, and determine asymptotically optimal content placement strategies in the case of a limited content catalogue. We then turn to an alternative “large catalogue” scaling where the catalogue size scales with the peer population. Under this scaling, we establish that storage space per peer must necessarily grow unboundedly if bandwidth utilization is to be maximized. Relating the system performance to properties of a specific random graph model, we then identify a content placement strategy and a request acceptance policy which jointly maximize bandwidth utilization, provided storage space per peer grows unboundedly, although arbitrarily slowly, with system size.

Recently, it has been shown that CSMA-type random access algorithms can achieve the maximum possible throughput in ad hoc wireless networks. However, these algorithms assume an idealized continuous-time CSMA protocol where collisions can never occur. In addition, simulation results indicate that the delay performance of these algorithms can be quite bad. On the other hand, although some simple heuristics (such as distributed approximations of greedy maximal scheduling) can yield much better delay performance for a large set of arrival rates, they may only achieve a fraction of the capacity region in general. In this paper, we propose a discrete-time version of the CSMA algorithm. Central to our results is a discrete-time distributed randomized algorithm which is based on a generalization of the so-called Glauber dynamics from statistical physics, where multiple links are allowed to update their states in a single time slot. The algorithm generates collision-free transmission schedules while explicitly taking collisions into account during the control phase of the protocol, thus relaxing the perfect CSMA assumption. More importantly, the algorithm allows us to incorporate delay-reduction mechanisms which lead to very good delay performance while retaining the throughput-optimality property.

In this paper, we address the problem of content placement in peer-to-peer systems, with the objective of maximizing the utilization of peers' uplink bandwidth resources. We consider system performance under a many-user asymptotic. We identify optimal content placement strategies in a particular scenario of limited content catalogue, casting the problem into the framework of loss networks. We then turn to an alternative "large catalogue" scaling where the catalogue size grows with the peer population. Relating the system performance to properties of a specific random graph model, we establish a content placement strategy which again maximizes system performance, provided storage space per peer grows unboundedly, although arbitrarily slowly, with system size.