29 research outputs found
Message-driven dynamics
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 251-260).by Richard Anton Lethin.Ph.D
A Quantitative Theory of Bottleneck Structures for Data Networks
The conventional view of the congestion control problem in data networks is
based on the principle that a flow's performance is uniquely determined by the
state of its bottleneck link, regardless of the topological properties of the
network. However, recent work has shown that the behavior of
congestion-controlled networks is better explained by models that account for
the interactions between bottleneck links. These interactions are captured by a
latent \textit{bottleneck structure}, a model describing the complex ripple
effects that changes in one part of the network exert on the other parts. In
this paper, we present a \textit{quantitative} theory of bottleneck structures
(QTBS), a mathematical and engineering framework comprising a family of
polynomial-time algorithms that can be used to reason about a wide variety of
network optimization problems, including routing, capacity planning and flow
control. QTBS can contribute to traffic engineering by making clear predictions
about the relative performance of alternative flow routes, and by providing
numerical recommendations for the optimal rate settings of traffic shapers. A
particularly novel result in the domain of capacity planning indicates that
previously established rules for the design of folded-Clos networks are
suboptimal when flows are congestion controlled. We show that QTBS can be used
to derive the optimal rules for this important class of topologies, and
empirically demonstrate the correctness and efficacy of these results using the
BBR and Cubic congestion-control algorithms
Automatic Parallelization and Locality Optimization of Beamforming Algorithms
International audienceThis paper demonstrates the benefits of a global optimization strategy using a new automatic parallelization and locality optimization methodology for high performance embedded computing algorithms that occur in adaptive radar systems, for modern multi-core computing chips. As a baseline, the resulting performance was compared against the performance that could be obtained using highly optimized math libraries