18 research outputs found
Polynomial Linear Programming with Gaussian Belief Propagation
Interior-point methods are state-of-the-art algorithms for solving linear
programming (LP) problems with polynomial complexity. Specifically, the
Karmarkar algorithm typically solves LP problems in time O(n^{3.5}), where
is the number of unknown variables. Karmarkar's celebrated algorithm is known
to be an instance of the log-barrier method using the Newton iteration. The
main computational overhead of this method is in inverting the Hessian matrix
of the Newton iteration. In this contribution, we propose the application of
the Gaussian belief propagation (GaBP) algorithm as part of an efficient and
distributed LP solver that exploits the sparse and symmetric structure of the
Hessian matrix and avoids the need for direct matrix inversion. This approach
shifts the computation from realm of linear algebra to that of probabilistic
inference on graphical models, thus applying GaBP as an efficient inference
engine. Our construction is general and can be used for any interior-point
algorithm which uses the Newton method, including non-linear program solvers.Comment: 7 pages, 1 figure, appeared in the 46th Annual Allerton Conference on
Communication, Control and Computing, Allerton House, Illinois, Sept. 200
A Hybrid Multicast-Unicast Infrastructure for Efficient Publish-Subscribe in Enterprise Networks
One of the main challenges in building a large scale publish-subscribe
infrastructure in an enterprise network, is to provide the subscribers with the
required information, while minimizing the consumed host and network resources.
Typically, previous approaches utilize either IP multicast or point-to-point
unicast for efficient dissemination of the information.
In this work, we propose a novel hybrid framework, which is a combination of
both multicast and unicast data dissemination. Our hybrid framework allows us
to take the advantages of both multicast and unicast, while avoiding their
drawbacks. We investigate several algorithms for computing the best mapping of
publishers' transmissions into multicast and unicast transport.
Using extensive simulations, we show that our hybrid framework reduces
consumed host and network resources, outperforming traditional solutions. To
insure the subscribers interests closely resemble those of real-world settings,
our simulations are based on stock market data and on recorded IBM WebShpere
subscriptions
Distributed Large Scale Network Utility Maximization
Recent work by Zymnis et al. proposes an efficient primal-dual interior-point
method, using a truncated Newton method, for solving the network utility
maximization (NUM) problem. This method has shown superior performance relative
to the traditional dual-decomposition approach. Other recent work by Bickson et
al. shows how to compute efficiently and distributively the Newton step, which
is the main computational bottleneck of the Newton method, utilizing the
Gaussian belief propagation algorithm.
In the current work, we combine both approaches to create an efficient
distributed algorithm for solving the NUM problem. Unlike the work of Zymnis,
which uses a centralized approach, our new algorithm is easily distributed.
Using an empirical evaluation we show that our new method outperforms previous
approaches, including the truncated Newton method and dual-decomposition
methods. As an additional contribution, this is the first work that evaluates
the performance of the Gaussian belief propagation algorithm vs. the
preconditioned conjugate gradient method, for a large scale problem.Comment: In the International Symposium on Information Theory (ISIT) 200
Dr. Multicast: Rx for Data Center Communication Scalability
Data centers avoid IP Multicast because of a series of
problems with the technology. We propose Dr. Multicast
(MCMD), a system that maps IPMC operations to
a combination of point-to-point unicast and traditional
IPMC transmissions. MCMD optimizes the use of IPMC
addresses within a data center, while simultaneously respecting
an administrator-specified acceptable-use policy.
We argue that with the resulting range of options,
IPMC no longer represents a threat and can therefore be
used much more widely.AFRL, AFOSR, NSF,
Cisco and Intel Corporation
Constructing Scalable Overlays for Pub-Sub with Many Topics Problems, Algorithms, and Evaluation
We investigate the problem of designing a scalable overlay network to support decentralized topic-based pub/sub communication. We introduce a new optimization problem, called Minimum Topic-Connected Overlay (Min-TCO), that captures the tradeoff between the scalability of the overlay (in terms of the nodes ’ fanout) and the message forwarding overhead incurred by the communicating parties. Roughly, the Min-TCO problem is as follows: Given a collection of nodes and their subscriptions, connect the nodes using the minimum possible number of edges so that for each topic t, a message published on t could reach all the nodes interested in t by being forwarded by only the nodes interested in t. We show that the decision version of Min-TCO is NPcomplete, and present a polynomial algorithm that approximate
Gravity: An interest-aware publish/subscribe system based on structured overlays
Publish/subscribe (pub/sub) is a popular communication middleware that allows users to subscribe to topics of interest, and then be notified of messages being posted on any of the topics in their subscriptions. Traditionally, most uses of pub/sub have been limite