On the Bottleneck Structure of Congestion-Controlled Networks



Publication Source: ACM SIGMETRICS 2020, Boston MA; Proc. ACM Meas. Anal. Comput. Syst., Vol. 3, No. 3, Article 59, December 2019

In this paper, we introduce the Theory of Bottleneck Ordering, a mathematical framework that reveals the bottleneck structure of data networks. This theoretical framework provides insights into the inherent topological properties of a network at least in three areas: (1) It identifies the regions of influence of each bottleneck; (2) it reveals the order in which bottlenecks (and flows traversing them) converge to their steady state transmission rates in distributed congestion control algorithms; and (3) it provides key insights to the design of optimized traffic engineering policies. We demonstrate the efficacy of the proposed theory in TCP congestion-controlled networks for two broad classes of algorithms: congestion-based algorithms (BBR) and loss-based additive-increase/multiplicative-decrease algorithms (Cubic and Reno). Among other results, our network experiments show that: (1) Qualitatively, both classes of congestion control algorithms behave as predicted by the bottleneck structure of the network; (2) flows compete for bandwidth only with other flows operating at the same bottleneck level; (3) BBR flows achieve higher performance and fairness than Cubic and Reno flows due to their ability to operate at the right bottleneck level; (4) the bottleneck structure of a network is ever-changing and its levels can be folded due to variations in the flows’ round trip times; and (5) against conventional wisdom, low-hitter flows can have a large impact to the overall performance of a network.
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Uniform Random Sampling in Polyhedra



Publication Source: 10th International Workshop on Polyhedral Compilation Techniques; IMPACT 2020, Bologna, Italy

We propose a method for generating uniform samples among a domain of integer points defined by a polyhedron in a multidimensional space. The method extends to domains defined by parametric polyhedra, in which a subset of the variables are symbolic. We motivate this work by a list of applications for the method in computer science. The proposed method relies on polyhedral ranking functions, as well as a recent inversion method for them, named trahrhe expressions.

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Systems and Methods for Minimizing Communications



Publication Source: Patent US20160196086A1

A system for allocation of one or more data structures used in a program across a number of processing units takes into account a memory access pattern of the data structure, and the amount of total memory available for duplication across the several processing units. Using these parameters duplication factors are determined for the one or more data structures such that the cost of remote communication is minimized when the data structures are duplicated according to the respective duplication factors while allowing parallel execution of the program.
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G2: A Network Optimization Framework for High-Precision Analysis of Bottleneck and Flow Performance



Publication Source: 2019 IEEE/ACM SuperComputing Conference, Innovating the Network for Data-Intensive Science (INDIS) Workshop, Denver, CO

Congestion control algorithms for data networks have been the subject of intense research for the last three decades. While most of the work has focused around the characterization of a flow’s bottleneck link, understanding the interactions amongst links and the ripple effects that perturbations in a link can cause on the rest of the network has remained much less understood. The Theory of Bottleneck Ordering is a recently developed mathematical framework that reveals the bottleneck structure of a network and provides a model to understand such effects. In this paper we present G2, the first operational network optimization framework that utilizes this new theoretical framework to characterize with high-precision the performance of bottlenecks and flows. G2 generates an interactive graph structure that describes how perturbations in links and flows propagate, providing operators new optimization insights and traffic engineering recommendations to help improve network performance. We provide a description of the G2 implementation and a set of experiments using real TCP/IP code to demonstrate its operational efficacy.

For more information on G2 technology, visit https://www.reservoir.com/research/tech/networking/.  


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Systems and Methods for Efficient Targeting



Publication Source: Patent US10466349B2

A system for determining the physical path of an object can map several candidate paths to a suitable path space that can be explored using a convex optimization technique. The optimization technique may take advantage of the typical sparsity of the path space and can identify a likely physical path using a function of sensor observation as constraints. A track of an object can also be determined using a track model and a convex optimization technique.
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