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Title: Systems and methods for fast detection of elephant flows in network traffic

Abstract

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 is employed to calculate a posterior distribution that captures uncertainty about the sizes of each flow, yielding a top flow detection likelihood. The posterior distribution is used to find the most likely subset of elephant flows. The technique rapidly converges to the optimal sampling rate at a speed O(1/n), where n is the number of packet samples received, and the only hyperparameter required is the targeted detection likelihood.

Inventors:
;
Issue Date:
Research Org.:
Reservoir Labs, Inc., New York, NY (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1805526
Patent Number(s):
10924418
Application Number:
16/270,089
Assignee:
Reservoir Labs, Inc. (New York, NY)
Patent Classifications (CPCs):
H - ELECTRICITY H04 - ELECTRIC COMMUNICATION TECHNIQUE H04L - TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
DOE Contract Number:  
SC0011358
Resource Type:
Patent
Resource Relation:
Patent File Date: 02/07/2019
Country of Publication:
United States
Language:
English

Citation Formats

Gudibanda, Aditya, and Ros-Giralt, Jordi. Systems and methods for fast detection of elephant flows in network traffic. United States: N. p., 2021. Web.
Gudibanda, Aditya, & Ros-Giralt, Jordi. Systems and methods for fast detection of elephant flows in network traffic. United States.
Gudibanda, Aditya, and Ros-Giralt, Jordi. Tue . "Systems and methods for fast detection of elephant flows in network traffic". United States. https://www.osti.gov/servlets/purl/1805526.
@article{osti_1805526,
title = {Systems and methods for fast detection of elephant flows in network traffic},
author = {Gudibanda, Aditya and Ros-Giralt, Jordi},
abstractNote = {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 is employed to calculate a posterior distribution that captures uncertainty about the sizes of each flow, yielding a top flow detection likelihood. The posterior distribution is used to find the most likely subset of elephant flows. The technique rapidly converges to the optimal sampling rate at a speed O(1/n), where n is the number of packet samples received, and the only hyperparameter required is the targeted detection likelihood.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {2}
}

Works referenced in this record:

Sampling based on large flow detection for network visibility monitoring
patent, October 2018


Systems and Methods for Pacing Data Flows
patent-application, September 2016


Large flow detection for network visibility monitoring
patent, May 2018


Network visibility monitoring
patent, June 2018


Historically large flows in network visibility monitoring
patent, July 2018


Traffic-aware sampling rate adjustment within a network device
patent, January 2016


Determining Sampling Rate from Randomly Sampled Events
patent-application, March 2015


System and Method for Sampling Network Traffic
patent-application, June 2010


Systems and Methods for Identifying Candidate Flows in Data Packet Networks
patent-application, August 2019