In a system for efficiently detecting large/elephant flows in a network, the rate at which the received packets are sampled is adjusted according to a top flow detection likelihood computed for a cache of flows identified in the arriving network traffic. After observing packets sampled from the network, Dirichlet-Categorical inference
The Theory of Bottleneck Structures is a recently developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network
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)
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
A multiresolution parser (MRP) can selectively extract one or more information units from a dataset based on the available processing capacity and/or the arrival rate of the dataset. Should any of these parameters change, the MRP can adaptively change the information units to be extracted such that the benefit or
Positive linear programming (PLP), also known as packing and covering linear programs, is an important class of problems frequently found in fields such as network science, operations research, or economics. In this work we demonstrate that all PLP problems can be represented using a network structure, revealing new key insights
Full packet capture (FPC) consists in capturing all packets and storing them into permanent storage to enable offline forensic analysis. FPC however suffers from a scalability issue: at today’s normal traffic speed rates of 10Gbps or above, it either becomes intractable or requires highly expensive hardware both in processing and
The problem of elephant flow detection is a longstanding research area with the goal of quickly identifying flows in a network that are large enough to affect the quality of service of smaller flows. Past work in this field has largely been either domain-specific, based on thresholds for a specific
Multiresolution priority queues are data structures recently discovered by Reservoir Labs that reduce the entropy of some critical graph algorithms—such as Dijkstra’s or Prim’s algorithms—and deliver new lower computational complexity bounds. These new data structures are capable of exploiting the multiresolution properties of discrete algorithms, a characteristic that has been
632 Broadway, Suite 803
New York, New York 10012
