6,402 research outputs found
Space-Efficient Routing Tables for Almost All Networks and the Incompressibility Method
We use the incompressibility method based on Kolmogorov complexity to
determine the total number of bits of routing information for almost all
network topologies. In most models for routing, for almost all labeled graphs
bits are necessary and sufficient for shortest path routing. By
`almost all graphs' we mean the Kolmogorov random graphs which constitute a
fraction of of all graphs on nodes, where is an arbitrary
fixed constant. There is a model for which the average case lower bound rises
to and another model where the average case upper bound
drops to . This clearly exposes the sensitivity of such bounds
to the model under consideration. If paths have to be short, but need not be
shortest (if the stretch factor may be larger than 1), then much less space is
needed on average, even in the more demanding models. Full-information routing
requires bits on average. For worst-case static networks we
prove a lower bound for shortest path routing and all
stretch factors in some networks where free relabeling is not allowed.Comment: 19 pages, Latex, 1 table, 1 figure; SIAM J. Comput., To appea
On a family of strong geometric spanners that admit local routing strategies
We introduce a family of directed geometric graphs, denoted \paz, that
depend on two parameters and . For and , the \paz graph is a strong
-spanner, with . The out-degree of a node
in the \paz graph is at most . Moreover, we show that routing can be
achieved locally on \paz. Next, we show that all strong -spanners are also
-spanners of the unit disk graph. Simulations for various values of the
parameters and indicate that for random point sets, the
spanning ratio of \paz is better than the proven theoretical bounds
Selfish Routing on Dynamic Flows
Selfish routing on dynamic flows over time is used to model scenarios that
vary with time in which individual agents act in their best interest. In this
paper we provide a survey of a particular dynamic model, the deterministic
queuing model, and discuss how the model can be adjusted and applied to
different real-life scenarios. We then examine how these adjustments affect the
computability, optimality, and existence of selfish routings.Comment: Oberlin College Computer Science Honors Thesis. Supervisor: Alexa
Sharp, Oberlin Colleg
Timely Data Delivery in a Realistic Bus Network
AbstractâWiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I
Adaptive Probabilistic Flooding for Multipath Routing
In this work, we develop a distributed source routing algorithm for topology
discovery suitable for ISP transport networks, that is however inspired by
opportunistic algorithms used in ad hoc wireless networks. We propose a
plug-and-play control plane, able to find multiple paths toward the same
destination, and introduce a novel algorithm, called adaptive probabilistic
flooding, to achieve this goal. By keeping a small amount of state in routers
taking part in the discovery process, our technique significantly limits the
amount of control messages exchanged with flooding -- and, at the same time, it
only minimally affects the quality of the discovered multiple path with respect
to the optimal solution. Simple analytical bounds, confirmed by results
gathered with extensive simulation on four realistic topologies, show our
approach to be of high practical interest.Comment: 6 pages, 6 figure
A Stackelberg Strategy for Routing Flow over Time
Routing games are used to to understand the impact of individual users'
decisions on network efficiency. Most prior work on routing games uses a
simplified model of network flow where all flow exists simultaneously, and
users care about either their maximum delay or their total delay. Both of these
measures are surrogates for measuring how long it takes to get all of a user's
traffic through the network. We attempt a more direct study of how competition
affects network efficiency by examining routing games in a flow over time
model. We give an efficiently computable Stackelberg strategy for this model
and show that the competitive equilibrium under this strategy is no worse than
a small constant times the optimal, for two natural measures of optimality
Lying Your Way to Better Traffic Engineering
To optimize the flow of traffic in IP networks, operators do traffic
engineering (TE), i.e., tune routing-protocol parameters in response to traffic
demands. TE in IP networks typically involves configuring static link weights
and splitting traffic between the resulting shortest-paths via the
Equal-Cost-MultiPath (ECMP) mechanism. Unfortunately, ECMP is a notoriously
cumbersome and indirect means for optimizing traffic flow, often leading to
poor network performance. Also, obtaining accurate knowledge of traffic demands
as the input to TE is elusive, and traffic conditions can be highly variable,
further complicating TE. We leverage recently proposed schemes for increasing
ECMP's expressiveness via carefully disseminated bogus information ("lies") to
design COYOTE, a readily deployable TE scheme for robust and efficient network
utilization. COYOTE leverages new algorithmic ideas to configure (static)
traffic splitting ratios that are optimized with respect to all (even
adversarially chosen) traffic scenarios within the operator's "uncertainty
bounds". Our experimental analyses show that COYOTE significantly outperforms
today's prevalent TE schemes in a manner that is robust to traffic uncertainty
and variation. We discuss experiments with a prototype implementation of
COYOTE
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