3,793 research outputs found
Optimal relay location and power allocation for low SNR broadcast relay channels
We consider the broadcast relay channel (BRC), where a single source
transmits to multiple destinations with the help of a relay, in the limit of a
large bandwidth. We address the problem of optimal relay positioning and power
allocations at source and relay, to maximize the multicast rate from source to
all destinations. To solve such a network planning problem, we develop a
three-faceted approach based on an underlying information theoretic model,
computational geometric aspects, and network optimization tools. Firstly,
assuming superposition coding and frequency division between the source and the
relay, the information theoretic framework yields a hypergraph model of the
wideband BRC, which captures the dependency of achievable rate-tuples on the
network topology. As the relay position varies, so does the set of hyperarcs
constituting the hypergraph, rendering the combinatorial nature of optimization
problem. We show that the convex hull C of all nodes in the 2-D plane can be
divided into disjoint regions corresponding to distinct hyperarcs sets. These
sets are obtained by superimposing all k-th order Voronoi tessellation of C. We
propose an easy and efficient algorithm to compute all hyperarc sets, and prove
they are polynomially bounded. Using the switched hypergraph approach, we model
the original problem as a continuous yet non-convex network optimization
program. Ultimately, availing on the techniques of geometric programming and
-norm surrogate approximation, we derive a good convex approximation. We
provide a detailed characterization of the problem for collinearly located
destinations, and then give a generalization for arbitrarily located
destinations. Finally, we show strong gains for the optimal relay positioning
compared to seemingly interesting positions.Comment: In Proceedings of INFOCOM 201
Optimal Networks from Error Correcting Codes
To address growth challenges facing large Data Centers and supercomputing
clusters a new construction is presented for scalable, high throughput, low
latency networks. The resulting networks require 1.5-5 times fewer switches,
2-6 times fewer cables, have 1.2-2 times lower latency and correspondingly
lower congestion and packet losses than the best present or proposed networks
providing the same number of ports at the same total bisection. These advantage
ratios increase with network size. The key new ingredient is the exact
equivalence discovered between the problem of maximizing network bisection for
large classes of practically interesting Cayley graphs and the problem of
maximizing codeword distance for linear error correcting codes. Resulting
translation recipe converts existent optimal error correcting codes into
optimal throughput networks.Comment: 14 pages, accepted at ANCS 2013 conferenc
The edge-disjoint path problem on random graphs by message-passing
We present a message-passing algorithm to solve the edge disjoint path
problem (EDP) on graphs incorporating under a unique framework both traffic
optimization and path length minimization. The min-sum equations for this
problem present an exponential computational cost in the number of paths. To
overcome this obstacle we propose an efficient implementation by mapping the
equations onto a weighted combinatorial matching problem over an auxiliary
graph. We perform extensive numerical simulations on random graphs of various
types to test the performance both in terms of path length minimization and
maximization of the number of accommodated paths. In addition, we test the
performance on benchmark instances on various graphs by comparison with
state-of-the-art algorithms and results found in the literature. Our
message-passing algorithm always outperforms the others in terms of the number
of accommodated paths when considering non trivial instances (otherwise it
gives the same trivial results). Remarkably, the largest improvement in
performance with respect to the other methods employed is found in the case of
benchmarks with meshes, where the validity hypothesis behind message-passing is
expected to worsen. In these cases, even though the exact message-passing
equations do not converge, by introducing a reinforcement parameter to force
convergence towards a sub optimal solution, we were able to always outperform
the other algorithms with a peak of 27% performance improvement in terms of
accommodated paths. On random graphs, we numerically observe two separated
regimes: one in which all paths can be accommodated and one in which this is
not possible. We also investigate the behaviour of both the number of paths to
be accommodated and their minimum total length.Comment: 14 pages, 8 figure
Scalable Traffic Engineering for Higher Throughput in Heavily-loaded Software Defined Networks
Existing traffic engineering (TE) solutions performs well for software
defined network (SDN) in average cases. However, during peak hours, bursty
traffic spikes are challenging to handle, because it is difficult to react in
time and guarantee high performance even after failures with limited flow
entries.
Instead of leaving some capacity empty to guarantee no congestion happens due
to traffic rerouting after failures or path updating after demand or topology
changes, we decide to make full use of the network capacity to satisfy the
demands for heavily-loaded peak hours. The TE system also needs to react to
failures quickly and utilize the priority queue to guarantee the transmission
of loss and delay sensitive traffic. We propose TED, a scalable TE system that
can guarantee high throughput in peak hours. TED can quickly compute a group of
maximum number of edge-disjoint paths for each ingress-egress switch pair. We
design two methods to select paths under the flow entry limit. We then input
the selected paths to our TE to minimize the maximum link utilization. In case
of large traffic matrix making the maximum link utilization larger than 1, we
input the utilization and the traffic matrix to the optimization of maximizing
overall throughput under a new constrain. Thus we obtain a realistic traffic
matrix, which has the maximum overall throughput and guarantees no traffic
starvation for each switch pair. Experiments show that TED has much better
performance for heavily-loaded SDN and has 10% higher probability to satisfy
all (> 99.99%) the traffic after a single link failure for G-Scale topology
than Smore under the same flow entry limit.Comment: The 8 pages double column version of the paper is submitted to NOMS
2020 technical sessio
Timely Data Delivery in a Realistic Bus Network
Abstract—WiFi-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
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