Origin- des*na*on Flow Measurement in High- Speed Networks

Size: px
Start display at page:

Download "Origin- des*na*on Flow Measurement in High- Speed Networks"

Transcription

1 IEEE INFOCOM, 2012 Origin- des*na*on Flow Measurement in High- Speed Networks Tao Li Shigang Chen Yan Qiao

2 Introduc*on (Defini*ons) Origin- des+na+on flow between two routers is the set of packets that pass both routers. Egress Ingress ISP network Ingress Egress Ø Traverse from one router to the other (direc<onal) Ø Traverse between two routers (undirec<onal) OD flow size: cardinality of the packet set (more details soon ) 2 /20

3 Applica*ons of OD- flow Measurement Network Access PaKern Capacity Planning Intrusion Detec<on System Accoun<ng and Billing Worm Detec<on Scanner Detec<on Discover BoKleneck Anomaly Detec<on Service Provision 3 /20

4 Problem Measure the OD- flow size between any two routers. An Example r1 r2 The OD flow size between r1 and r2 is 1 4 /20

5 Technical Challenges Fast Speed Small Memory Huge Volume 5 /20

6 One Naïve Solu*on Each router: record all packets that pass it Ø Compare two routers set Ø Unrealis<c : many packets (e.g. 100M) Ø Signature, each 160 bits, total: 2GB Ø We only want a few bits for each packet!! p1 p2 p3 6 /20

7 Another Solu*on Each router: maintains a counter for each of other routers Each packet: records all routers it has passed (r1) (r1, r2) r1 r2 r3 id value id Value value id value r2 r3 0 0 r1 r r1 r /20

8 Mo*va*on & Overview Mo<va<on Ø Storing exact info too expensive => sta<s<cal methods. Basic Idea: Each router maintains a bitmap. For each packet p Ø Pseudo- randomly mark one bit in the bitmap Ø Same packet always maps to same bit in different routers r1 r2 p9 p1 p3 p5 p4 p Under- 1 1 es<ma<on Can be stalslcally solved Bitwise when the AND bitmap of two is long bitmaps Over- enough es<ma<on 1 1 p1 p3 p7 p8 p6 p9 8 /20

9 Marking the Packet Informa*on Online: marking packet informa<on Offline: measuring OD flow size Each router maintains a bitmap B with fixed length m, ini<ally all bits are set to zero When it comes a packet p Ø H(p): H(..) is a hash func<on whose output range is [1,..,m] Ø Mark the bit to 1: B[H(p)] := 1 p1 p2 p B m 9 /20

10 Measuring the Size of Each OD Flow All routers report their bitmaps to centralized server Nota<ons: Ø Let S1 and S2 be the set of packets that pass two routers r1 and r2 Ø n1 and n2 be the cardinali<es of S1 and S2, n1 = S1, n2 = S2 Ø nc is the number of common packets that r1 and r2 share What we want to know 10 /20

11 Measuring the Size of Each OD Flow (Cont.) Measuring n1 and n2 Ø [K. Whang, etc. A linear- Lme probabilislc counlng algorithm for database applicalons, ACM Trans. On Database Systems, 1990] p1 p2 p3 p4 p5 p6 p7 p8 p9 B m = 10, V = 4/10 = * ln(0.4) = /20

12 Measuring the Size of Each OD Flow (Cont.) Measuring nc Ø Take a bitwise AND opera<on of B1 and B2, denoted as Bc B p1 p3 p2 Bc 1 1 B p1 p4 p6 p5 Ø A bit in Bc, it is 0 iff the following two condi<ons are BOTH sa<sfied 1. It is not chosen by any packet in 2. It is either not chosen by any packet in or not chosen by any packet in 12 /20

13 Measuring the Size of Each OD Flow (Cont.) The probability for a bit in Bc to remain 0 is Observe in Bc Maximum Likelihood Es<ma<on Ø Select values that produce a distribu<on that gives the observed data the greatest probability 13 /20

14 Measuring the Size of Each OD Flow (Cont.) We can compute nc in the following formula Measurement accuracy 14 /20

15 Simula*ons Simula<on setup Ø n1 and n2 are randomly selected from [100,000, 10,000,000] Ø nc is randomly selected from [100, 50,000] Ø Choose 1,000 different n1, n2, and nc Ø Run 1,000 <mes and show averaged results Most related work: Quasi- likelihood approach (QMLE): Ø Use bucket array for packet storage Ø Derive quasi- probability distribu<on of packet informa<on 15 /20

16 Simula*ons (Cont.) Per- packet Processing Overhead Measurement Accuracy 16 /20

17 Experimental Results Abilene Network Ø 12 routers at different ci<es in US Ø 24 weeks of traffic trace Ø 5 minutes for each dataset (a measurement period) Ø 0.3M to 13M packets for each router in one measurement period 17 /20

18 Experimental Results (Cont.) Per- packet Processing Overhead Measurement Accuracy 18 /20

19 Conclusions Measure OD flow size A bitmap data structure MLE method Experiments 19 /20

20 Thank you

21 Condi*on 1 : It is not chosen by any packet in Each packet: randomly select one bit in B B 1 m A specific bit b in B, each packet has probability Ø 1/m to choose it Ø (1-1/m) not to choose it p The probability for Condi<on 1 is 21 /20

22 Condi*on 2: It is either not chosen by any packet in chosen by any packet in or not A specific bit b in B, each packet has probability Ø 1/m to choose it Ø (1-1/m) not to choose it Ø The probability for b not to be chosen by any packet in Ø is Ø is 22 /20

Origin-Destination Flow Measurement in High-Speed Networks

Origin-Destination Flow Measurement in High-Speed Networks Origin-Destination Flow Measurement in High-Speed Networks Tao Li Shigang Chen Yan Qiao Department of Computer & Information Science & Engineering University of Florida, Gainesville, FL, USA Abstract An

More information

Estimating Persistent Spread in High-speed Networks Qingjun Xiao, Yan Qiao, Zhen Mo, Shigang Chen

Estimating Persistent Spread in High-speed Networks Qingjun Xiao, Yan Qiao, Zhen Mo, Shigang Chen Estimating Persistent Spread in High-speed Networks Qingjun Xiao, Yan Qiao, Zhen Mo, Shigang Chen Southeast University of China University of Florida Motivation for Persistent Stealthy Spreaders Imagine

More information

Highly Compact Virtual Maximum Likelihood Sketches for Counting Big Network Data

Highly Compact Virtual Maximum Likelihood Sketches for Counting Big Network Data Highly Compact Virtual Maximum Likelihood Sketches for Counting Big Network Data Zhen Mo Yan Qiao Shigang Chen Department of Computer & Information Science & Engineering University of Florida Gainesville,

More information

Name-Based Content Routing in Information Centric Networks Using Distance Information

Name-Based Content Routing in Information Centric Networks Using Distance Information Name-Based Content Routing in Information Centric Networks Using Distance Information J.J. Garcia-Luna-Aceves Palo Alto Research Center UC Santa Cruz jj@soe.ucsc.edu Origins of Routing for Packet Switching

More information

hashfs Applying Hashing to Op2mize File Systems for Small File Reads

hashfs Applying Hashing to Op2mize File Systems for Small File Reads hashfs Applying Hashing to Op2mize File Systems for Small File Reads Paul Lensing, Dirk Meister, André Brinkmann Paderborn Center for Parallel Compu2ng University of Paderborn Mo2va2on and Problem Design

More information

RouteBricks: Exploi2ng Parallelism to Scale So9ware Routers

RouteBricks: Exploi2ng Parallelism to Scale So9ware Routers RouteBricks: Exploi2ng Parallelism to Scale So9ware Routers Mihai Dobrescu and etc. SOSP 2009 Presented by Shuyi Chen Mo2va2on Router design Performance Extensibility They are compe2ng goals Hardware approach

More information

Network Measurement. COS 461 Recita8on. h:p://

Network Measurement. COS 461 Recita8on. h:p:// Network Measurement COS 461 Recita8on h:p://www.cs.princeton.edu/courses/archive/spr14/cos461/ 2! Why Measure the Network? Scien8fic discovery Characterizing traffic, topology, performance Understanding

More information

Nfsight Ne*low- based Network Awareness Tool

Nfsight Ne*low- based Network Awareness Tool LISA 10 Nov 7-12, 2010 San Jose, CA Nfsight Ne*low- based Network Awareness Tool Robin Berthier (University of Illinois at Urbana- Champaign) Michel Cukier (University of Maryland) Ma3 Hiltunen (AT&T Research)

More information

Packet Doppler: Network Monitoring using Packet Shift Detection

Packet Doppler: Network Monitoring using Packet Shift Detection Packet Doppler: Network Monitoring using Packet Shift Detection Tongqing Qiu, Nan Hua, Jun (Jim) Xu Georgia Tech Jian Ni, Hao Wang, Richard (Yang) Yang Yale University Presenter: Richard Ma December 10th,

More information

Data Flow Analysis. Suman Jana. Adopted From U Penn CIS 570: Modern Programming Language Implementa=on (Autumn 2006)

Data Flow Analysis. Suman Jana. Adopted From U Penn CIS 570: Modern Programming Language Implementa=on (Autumn 2006) Data Flow Analysis Suman Jana Adopted From U Penn CIS 570: Modern Programming Language Implementa=on (Autumn 2006) Data flow analysis Derives informa=on about the dynamic behavior of a program by only

More information

Jason Polakis, Marco Lancini, Georgios Kontaxis, Federico Maggi, So5ris Ioannidis, Angelos Keromy5s, Stefano Zanero.

Jason Polakis, Marco Lancini, Georgios Kontaxis, Federico Maggi, So5ris Ioannidis, Angelos Keromy5s, Stefano Zanero. Jason Polakis, Marco Lancini, Georgios Kontaxis, Federico Maggi, So5ris Ioannidis, Angelos Keromy5s, Stefano Zanero polakis@ics.forth.gr Annual Computer Security Applica5ons Conference (ACSAC) 2012 Introduc5on

More information

CS144 An Introduc8on to Computer Networks

CS144 An Introduc8on to Computer Networks CS144 An Introduc8on to Computer Networks Packet Switching Philip Levis Oct 11, 2017 Packet Switching A Source R1 R2 R3 B Des8na8on R4 - Packets are routed individually, by looking up address in router

More information

Applica'on Aware Deadlock Free Oblivious Rou'ng

Applica'on Aware Deadlock Free Oblivious Rou'ng Applica'on Aware Deadlock Free Oblivious Rou'ng Michel Kinsy, Myong Hyo Cho, Tina Wen, Edward Suh (Cornell University), Marten van Dijk and Srinivas Devadas Massachuse(s Ins,tute of Technology Outline

More information

Network Layer Overview. Star8ng the Network Layer! Builds on the link layer. Routers send packets over mul8ple networks

Network Layer Overview. Star8ng the Network Layer! Builds on the link layer. Routers send packets over mul8ple networks Introduc8on to Computer Networks Network Layer Overview Computer Science & Engineering Where we are in the Course Star8ng the Network Layer! Builds on the link layer. Routers send packets over mul8ple

More information

Introduc3on to Computer Networks

Introduc3on to Computer Networks Introduc3on to Computer Networks Network Layer Overview Computer Science & Engineering Where we are in the Course Star3ng the Network Layer! Builds on the link layer. Routers send packets over mul3ple

More information

Developing an IDE4L Grid

Developing an IDE4L Grid Developing an IDE4L Grid Presenter: Antonino Riccobono, Ph.D. RWTH Aachen University Project Coordinator: Prof. Sami Repo Tampere University of Technology 5 th November 2014 SLIDE 2 Contents Overview of

More information

Introduc)on to Probabilis)c Latent Seman)c Analysis. NYP Predic)ve Analy)cs Meetup June 10, 2010

Introduc)on to Probabilis)c Latent Seman)c Analysis. NYP Predic)ve Analy)cs Meetup June 10, 2010 Introduc)on to Probabilis)c Latent Seman)c Analysis NYP Predic)ve Analy)cs Meetup June 10, 2010 PLSA A type of latent variable model with observed count data and nominal latent variable(s). Despite the

More information

Lab 8: Firewalls & Intrusion Detec6on Systems

Lab 8: Firewalls & Intrusion Detec6on Systems Lab 8: Firewalls & Intrusion Detec6on Systems Fengwei Zhang Wayne State University CSC Course: Cyber Security Prac6ce 1 Firewall & IDS Firewall A device or applica6on that analyzes packet headers and enforces

More information

On Measuring the Client- Side DNS Infrastructure Kyle Schomp, Tom Callahan, Michael Rabinovich, Mark Allman

On Measuring the Client- Side DNS Infrastructure Kyle Schomp, Tom Callahan, Michael Rabinovich, Mark Allman On Measuring the Client- Side DNS Infrastructure Kyle Schomp, Tom Callahan, Michael Rabinovich, Mark Allman Case Western Reserve University Interna@onal Computer Science Ins@tute 10/23/2013 ACM IMC 2013

More information

Mul$media Networking. #10 QoS Semester Ganjil 2012 PTIIK Universitas Brawijaya

Mul$media Networking. #10 QoS Semester Ganjil 2012 PTIIK Universitas Brawijaya Mul$media Networking #10 QoS Semester Ganjil 2012 PTIIK Universitas Brawijaya Schedule of Class Mee$ng 1. Introduc$on 2. Applica$ons of MN 3. Requirements of MN 4. Coding and Compression 5. RTP 6. IP Mul$cast

More information

Link State Rou.ng Reading: Sec.ons 4.2 and 4.3.4

Link State Rou.ng Reading: Sec.ons 4.2 and 4.3.4 Link State Rou.ng Reading: Sec.ons. and.. COS 6: Computer Networks Spring 009 (MW :0 :50 in COS 05) Michael Freedman Teaching Assistants: WyaN Lloyd and Jeff Terrace hnp://www.cs.princeton.edu/courses/archive/spring09/cos6/

More information

Address and Switching in the Link Layer

Address and Switching in the Link Layer Address and Switching in the Link Layer Brad Karp (slides contributed by Kyle Jamieson, Scott Shenker, and adapted from Kurose and Ross) UCL Computer Science CS 05/GZ01 18 th November 014 1 The link layer:

More information

Introduction to Netflow

Introduction to Netflow Introduction to Netflow Campus Network Design & Operations Workshop These materials are licensed under the Creative Commons Attribution-NonCommercial 4.0 International license (http://creativecommons.org/licenses/by-nc/4.0/)

More information

Minimum Redundancy and Maximum Relevance Feature Selec4on. Hang Xiao

Minimum Redundancy and Maximum Relevance Feature Selec4on. Hang Xiao Minimum Redundancy and Maximum Relevance Feature Selec4on Hang Xiao Background Feature a feature is an individual measurable heuris4c property of a phenomenon being observed In character recogni4on: horizontal

More information

Quan'fying QoS Requirements of Network Services: A Cheat- Proof Framework

Quan'fying QoS Requirements of Network Services: A Cheat- Proof Framework Quan'fying QoS Requirements of Network Services: A Cheat- Proof Framework Kuan- Ta Chen Academia Sinica Chen- Chi Wu Na3onal Taiwan University Yu- Chun Chang Na3onal Taiwan University Chin- Laung Lei Na3onal

More information

Energy- Aware Time Change Detec4on Using Synthe4c Aperture Radar On High- Performance Heterogeneous Architectures: A DDDAS Approach

Energy- Aware Time Change Detec4on Using Synthe4c Aperture Radar On High- Performance Heterogeneous Architectures: A DDDAS Approach Energy- Aware Time Change Detec4on Using Synthe4c Aperture Radar On High- Performance Heterogeneous Architectures: A DDDAS Approach Sanjay Ranka (PI) Sartaj Sahni (Co- PI) Mark Schmalz (Co- PI) University

More information

Effect of Router Buffers on Stability of Internet Conges8on Control Algorithms

Effect of Router Buffers on Stability of Internet Conges8on Control Algorithms Effect of Router Buffers on Stability of Internet Conges8on Control Algorithms Somayeh Sojoudi Steven Low John Doyle Oct 27, 2011 1 Resource alloca+on problem Objec8ve Fair assignment of rates to the users

More information

Conges'on Control Reading: Sec'ons

Conges'on Control Reading: Sec'ons Conges'on Control Reading: Sec'ons 6.1 6.4 COS 461: Computer Networks Spring 2009 (MW 1:30 2:50 in CS 105) Mike Freedman Teaching Assistants: WyaM Lloyd and Jeff Terrace hmp://www.cs.princeton.edu/courses/archive/spring09/cos461/

More information

Feature Rich Flow Monitoring with P4

Feature Rich Flow Monitoring with P4 Feature Rich Flow Monitoring with P4 John Sonchack University of Pennsylvania 1 Outline Introduction: Flow Records Design and Implementation: P4 Accelerated Flow Record Generation Benchmarks and Optimizations

More information

Introduc8on to Computer Networks. Where we are in the Course. Network Layer Overview. Star8ng the Network Layer!

Introduc8on to Computer Networks. Where we are in the Course. Network Layer Overview. Star8ng the Network Layer! Introduc8on to Computer Networks Network Layer Overview Computer Science & Engineering Where we are in the Course Star8ng the Network Layer! Builds on the link layer. Routers send packets over mul8ple

More information

Trustworthy Keyword Search for Regulatory Compliant Records Reten;on

Trustworthy Keyword Search for Regulatory Compliant Records Reten;on Trustworthy Keyword Search for Regulatory Compliant Records Reten;on S. Mitra, W. Hsu, M. WinsleA Presented by Thao Pham Introduc;on Important documents: emails, mee;ng memos, must be maintained in a trustworthy

More information

TerraSwarm. A Machine Learning and Op0miza0on Toolkit for the Swarm. Ilge Akkaya, Shuhei Emoto, Edward A. Lee. University of California, Berkeley

TerraSwarm. A Machine Learning and Op0miza0on Toolkit for the Swarm. Ilge Akkaya, Shuhei Emoto, Edward A. Lee. University of California, Berkeley TerraSwarm A Machine Learning and Op0miza0on Toolkit for the Swarm Ilge Akkaya, Shuhei Emoto, Edward A. Lee University of California, Berkeley TerraSwarm Tools Telecon 17 November 2014 Sponsored by the

More information

State- space search algorithm

State- space search algorithm Graph Search These are slides I usually use for teaching AI, but they offer another perspec;ve on BFS, DFS, and Dijkstra s algorithm. Ignore men;ons of a problem and simply consider that the search starts

More information

Handling heterogeneous storage devices in clusters

Handling heterogeneous storage devices in clusters Handling heterogeneous storage devices in clusters André Brinkmann University of Paderborn Toni Cortes Barcelona Supercompu8ng Center Randomized Data Placement Schemes n Randomized Data Placement Schemes

More information

Outline. In Situ Data Triage and Visualiza8on

Outline. In Situ Data Triage and Visualiza8on In Situ Data Triage and Visualiza8on Kwan- Liu Ma University of California at Davis Outline In situ data triage and visualiza8on: Issues and strategies Case study: An earthquake simula8on Case study: A

More information

EITF25 Internet- - Techniques and Applica8ons Stefan Höst. L6 Networking and IP

EITF25 Internet- - Techniques and Applica8ons Stefan Höst. L6 Networking and IP EITF25 Internet- - Techniques and Applica8ons Stefan Höst L6 Networking and IP Data communica8on In reality, the source and des8na8on hosts are very seldom on the same network, for example web surf. Internet

More information

Lecture 1: Monte Carlo Method

Lecture 1: Monte Carlo Method Lecture 1: Monte Carlo Method Youjin Deng ( 邓友金 ) h>p://staff.ustc.edu.cn/~yjdeng/ University of Science and Technology of China Non- equilibrium Sta1s1cal Physics & Ac1ve Ma;er Systems: School and Workshop

More information

PPI Network Alignment Advanced Topics in Computa8onal Genomics

PPI Network Alignment Advanced Topics in Computa8onal Genomics PPI Network Alignment 02-715 Advanced Topics in Computa8onal Genomics PPI Network Alignment Compara8ve analysis of PPI networks across different species by aligning the PPI networks Find func8onal orthologs

More information

There is a tempta7on to say it is really used, it must be good

There is a tempta7on to say it is really used, it must be good Notes from reviews Dynamo Evalua7on doesn t cover all design goals (e.g. incremental scalability, heterogeneity) Is it research? Complexity? How general? Dynamo Mo7va7on Normal database not the right fit

More information

Highly Compact Virtual Counters for Per-Flow Traffic Measurement through Register Sharing

Highly Compact Virtual Counters for Per-Flow Traffic Measurement through Register Sharing Highly Compact Virtual Counters for Per-Flow Traffic Measurement through Register Sharing You Zhou Yian Zhou Min Chen Qingjun Xiao Shigang Chen Department of Computer & Information Science & Engineering,

More information

Ensemble- Based Characteriza4on of Uncertain Features Dennis McLaughlin, Rafal Wojcik

Ensemble- Based Characteriza4on of Uncertain Features Dennis McLaughlin, Rafal Wojcik Ensemble- Based Characteriza4on of Uncertain Features Dennis McLaughlin, Rafal Wojcik Hydrology TRMM TMI/PR satellite rainfall Neuroscience - - MRI Medicine - - CAT Geophysics Seismic Material tes4ng Laser

More information

Ges$one Avanzata dell Informazione Part A Full- Text Informa$on Management. Full- Text Indexing

Ges$one Avanzata dell Informazione Part A Full- Text Informa$on Management. Full- Text Indexing Ges$one Avanzata dell Informazione Part A Full- Text Informa$on Management Full- Text Indexing Contents } Introduction } Inverted Indices } Construction } Searching 2 GAvI - Full- Text Informa$on Management:

More information

Network Management and Monitoring

Network Management and Monitoring Network Management and Monitoring Introduction to Netflow These materials are licensed under the Creative Commons Attribution-Noncommercial 3.0 Unported license (http://creativecommons.org/licenses/by-nc/3.0/)

More information

CSCI 360 Introduc/on to Ar/ficial Intelligence Week 2: Problem Solving and Op/miza/on. Instructor: Wei-Min Shen

CSCI 360 Introduc/on to Ar/ficial Intelligence Week 2: Problem Solving and Op/miza/on. Instructor: Wei-Min Shen CSCI 360 Introduc/on to Ar/ficial Intelligence Week 2: Problem Solving and Op/miza/on Instructor: Wei-Min Shen Status Check and Review Status check Have you registered in Piazza? Have you run the Project-1?

More information

Differen'al Privacy. CS 297 Pragya Rana

Differen'al Privacy. CS 297 Pragya Rana Differen'al Privacy CS 297 Pragya Rana Outline Introduc'on Privacy Data Analysis: The SeAng Impossibility of Absolute Disclosure Preven'on Achieving Differen'al Privacy Introduc'on Sta's'c: quan'ty computed

More information

Design Principles & Prac4ces

Design Principles & Prac4ces Design Principles & Prac4ces Robert France Robert B. France 1 Understanding complexity Accidental versus Essen4al complexity Essen%al complexity: Complexity that is inherent in the problem or the solu4on

More information

Architecture of So-ware Systems Massively Distributed Architectures Reliability, Failover and failures. Mar>n Rehák

Architecture of So-ware Systems Massively Distributed Architectures Reliability, Failover and failures. Mar>n Rehák Architecture of So-ware Systems Massively Distributed Architectures Reliability, Failover and failures Mar>n Rehák Mo>va>on Internet- based business models imposed new requirements on computa>onal architectures

More information

Machine Learning Crash Course: Part I

Machine Learning Crash Course: Part I Machine Learning Crash Course: Part I Ariel Kleiner August 21, 2012 Machine learning exists at the intersec

More information

Link State Rou.ng Reading: Sec.ons 4.2 and 4.3.4

Link State Rou.ng Reading: Sec.ons 4.2 and 4.3.4 Link State Rou.ng Reading: Sec.ons. and.. COS 6: Computer Networks Spring 0 Mike Freedman hep://www.cs.princeton.edu/courses/archive/spring/cos6/ Inside a router Goals of Today s Lecture Control plane:

More information

Hypergraph Sparsifica/on and Its Applica/on to Par//oning

Hypergraph Sparsifica/on and Its Applica/on to Par//oning Hypergraph Sparsifica/on and Its Applica/on to Par//oning Mehmet Deveci 1,3, Kamer Kaya 1, Ümit V. Çatalyürek 1,2 1 Dept. of Biomedical Informa/cs, The Ohio State University 2 Dept. of Electrical & Computer

More information

Tools zur Op+mierung eingebe2eter Mul+core- Systeme. Bernhard Bauer

Tools zur Op+mierung eingebe2eter Mul+core- Systeme. Bernhard Bauer Tools zur Op+mierung eingebe2eter Mul+core- Systeme Bernhard Bauer Agenda Mo+va+on So.ware Engineering & Mul5core Think Parallel Models Added Value Tooling Quo Vadis? The Mul5core Era Moore s Law: The

More information

Implemen'ng IPv6 Segment Rou'ng in the Linux Kernel

Implemen'ng IPv6 Segment Rou'ng in the Linux Kernel Implemen'ng IPv6 Segment Rou'ng in the Linux Kernel David Lebrun, Olivier Bonaventure ICTEAM, UCLouvain Work supported by ARC grant 12/18-054 (ARC-SDN) and a Cisco grant Agenda IPv6 Segment Rou'ng Implementa'on

More information

Fit a Compact Spread Estimator in Small High-Speed Memory MyungKeun Yoon, Tao Li, Shigang Chen, and Jih-Kwon Peir

Fit a Compact Spread Estimator in Small High-Speed Memory MyungKeun Yoon, Tao Li, Shigang Chen, and Jih-Kwon Peir IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 19, NO. 5, OCTOBER 2011 1253 Fit a Compact Spread Estimator in Small High-Speed Memory MyungKeun Yoon, Tao Li, Shigang Chen, and Jih-Kwon Peir Abstract The spread

More information

Logis&c Regression. Aar$ Singh & Barnabas Poczos. Machine Learning / Jan 28, 2014

Logis&c Regression. Aar$ Singh & Barnabas Poczos. Machine Learning / Jan 28, 2014 Logis&c Regression Aar$ Singh & Barnabas Poczos Machine Learning 10-701/15-781 Jan 28, 2014 Linear Regression & Linear Classifica&on Weight Height Linear fit Linear decision boundary 2 Naïve Bayes Recap

More information

Architectures, and Protocol Design Issues for Mobile Social Networks: A Survey

Architectures, and Protocol Design Issues for Mobile Social Networks: A Survey Applica@ons, Architectures, and Protocol Design Issues for Mobile Social Networks: A Survey N. Kayastha,D. Niyato, P. Wang and E. Hossain, Proceedings of the IEEEVol. 99, No. 12, Dec. 2011. Sabita Maharjan

More information

Tangible Visualiza.on. Andy Wu Synaesthe.c Media Lab GVU Center Georgia Ins.tute of Technology

Tangible Visualiza.on. Andy Wu Synaesthe.c Media Lab GVU Center Georgia Ins.tute of Technology Tangible Visualiza.on Andy Wu Synaesthe.c Media Lab GVU Center Georgia Ins.tute of Technology Introduc.on Informa.on Visualiza.on (Infovis) is the study of the visual representa.on of complex informa.on,

More information

Objec0ves. Gain understanding of what IDA Pro is and what it can do. Expose students to the tool GUI

Objec0ves. Gain understanding of what IDA Pro is and what it can do. Expose students to the tool GUI Intro to IDA Pro 31/15 Objec0ves Gain understanding of what IDA Pro is and what it can do Expose students to the tool GUI Discuss some of the important func

More information

One Memory Access Bloom Filters and Their Generalization

One Memory Access Bloom Filters and Their Generalization This paper was presented as part of the main technical program at IEEE INFOCOM 211 One Memory Access Bloom Filters and Their Generalization Yan Qiao Tao Li Shigang Chen Department of Computer & Information

More information

Extending Heuris.c Search

Extending Heuris.c Search Extending Heuris.c Search Talk at Hebrew University, Cri.cal MAS group Roni Stern Department of Informa.on System Engineering, Ben Gurion University, Israel 1 Heuris.c search 2 Outline Combining lookahead

More information

Organized Segmenta.on

Organized Segmenta.on Organized Segmenta.on Alex Trevor, Georgia Ins.tute of Technology PCL TUTORIAL @ICRA 13 Overview Mo.va.on Connected Component Algorithm Planar Segmenta.on & Refinement Euclidean Clustering Timing Results

More information

CIS 551 / TCOM 401 Computer and Network Security. Spring 2007 Lecture 12

CIS 551 / TCOM 401 Computer and Network Security. Spring 2007 Lecture 12 CIS 551 / TCOM 401 Computer and Network Security Spring 2007 Lecture 12 Announcements Project 2 is on the web. Due: March 15th Send groups to Jeff Vaughan (vaughan2@seas) by Thurs. Feb. 22nd. Plan for

More information

Combinatorial Mathema/cs and Algorithms at Exascale: Challenges and Promising Direc/ons

Combinatorial Mathema/cs and Algorithms at Exascale: Challenges and Promising Direc/ons Combinatorial Mathema/cs and Algorithms at Exascale: Challenges and Promising Direc/ons Assefaw Gebremedhin Purdue University (Star/ng August 2014, Washington State University School of Electrical Engineering

More information

Conges'on. Last Week: Discovery and Rou'ng. Today: Conges'on Control. Distributed Resource Sharing. Conges'on Collapse. Conges'on

Conges'on. Last Week: Discovery and Rou'ng. Today: Conges'on Control. Distributed Resource Sharing. Conges'on Collapse. Conges'on Last Week: Discovery and Rou'ng Provides end-to-end connectivity, but not necessarily good performance Conges'on logical link name Michael Freedman COS 461: Computer Networks Lectures: MW 10-10:50am in

More information

The Drone War Fall 2012 COMS George Brink Shuo Qiu Xiaotong Chen Xiang Yao

The Drone War Fall 2012 COMS George Brink Shuo Qiu Xiaotong Chen Xiang Yao Fall 2012 COMS 4115 George Brink Shuo Qiu Xiaotong Chen Xiang Yao A stack- based Impera@ve language Applied to designed game Simple Interes@ng Powerful Mo@va@on Simple enough to be understood by users

More information

Alignment and Image Comparison. Erik Learned- Miller University of Massachuse>s, Amherst

Alignment and Image Comparison. Erik Learned- Miller University of Massachuse>s, Amherst Alignment and Image Comparison Erik Learned- Miller University of Massachuse>s, Amherst Alignment and Image Comparison Erik Learned- Miller University of Massachuse>s, Amherst Alignment and Image Comparison

More information

MapReduce, Apache Hadoop

MapReduce, Apache Hadoop Czech Technical University in Prague, Faculty of Informaon Technology MIE-PDB: Advanced Database Systems hp://www.ksi.mff.cuni.cz/~svoboda/courses/2016-2-mie-pdb/ Lecture 12 MapReduce, Apache Hadoop Marn

More information

Compiler: Control Flow Optimization

Compiler: Control Flow Optimization Compiler: Control Flow Optimization Virendra Singh Computer Architecture and Dependable Systems Lab Department of Electrical Engineering Indian Institute of Technology Bombay http://www.ee.iitb.ac.in/~viren/

More information

Model- Based Security Tes3ng with Test Pa9erns

Model- Based Security Tes3ng with Test Pa9erns Model- Based Security Tes3ng with Test Pa9erns Julien BOTELLA (Smartes5ng) Jürgen GROSSMANN (FOKUS) Bruno LEGEARD (Smartes3ng) Fabien PEUREUX (Smartes5ng) Mar5n SCHNEIDER (FOKUS) Fredrik SEEHUSEN (SINTEF)

More information

MapReduce, Apache Hadoop

MapReduce, Apache Hadoop NDBI040: Big Data Management and NoSQL Databases hp://www.ksi.mff.cuni.cz/ svoboda/courses/2016-1-ndbi040/ Lecture 2 MapReduce, Apache Hadoop Marn Svoboda svoboda@ksi.mff.cuni.cz 11. 10. 2016 Charles University

More information

Lecture 14: Tracking mo3on features op3cal flow

Lecture 14: Tracking mo3on features op3cal flow Lecture 14: Tracking mo3on features op3cal flow Dr. Juan Carlos Niebles Stanford AI Lab Professor Fei- Fei Li Stanford Vision Lab Lecture 14-1 What we will learn today? Introduc3on Op3cal flow Feature

More information

Amol Deshpande, University of Maryland Lisa Hellerstein, Polytechnic University, Brooklyn

Amol Deshpande, University of Maryland Lisa Hellerstein, Polytechnic University, Brooklyn Amol Deshpande, University of Maryland Lisa Hellerstein, Polytechnic University, Brooklyn Mo>va>on: Parallel Query Processing Increasing parallelism in compu>ng Shared nothing clusters, mul> core technology,

More information

Traffic Matrix Estimation

Traffic Matrix Estimation Traffic Matrix Estimation Matthew Roughan http://www.research.att.com/~roughan Shannon Lab Problem C Have link traffic measurements Want to know demands from source to destination

More information

Redundancy and Dependability Evalua2on. EECE 513: Design of Fault- tolerant Systems

Redundancy and Dependability Evalua2on. EECE 513: Design of Fault- tolerant Systems Redundancy and Dependability Evalua2on EECE 513: Design of Fault- tolerant Systems Learning Objec2ves At the end of this lecture, you will be able to Define combinatorial models of reliability Evaluate

More information

Network Access Transla0on - NAT

Network Access Transla0on - NAT Network Access Transla0on - NAT Foreword Those slides have been done by gathering a lot of informa0on on the net Ø Cisco tutorial Ø Lectures from other ins0tu0ons University of Princeton University of

More information

Bioinforma)cs Resources - NoSQL -

Bioinforma)cs Resources - NoSQL - Bioinforma)cs Resources - NoSQL - Lecture & Exercises Prof. B. Rost, Dr. L. Richter, J. Reeb Ins)tut für Informa)k I12 Short SQL Recap schema typed data tables defined layout space consump)on is computable

More information

Sliding HyperLogLog: Estimating cardinality in a data stream

Sliding HyperLogLog: Estimating cardinality in a data stream Sliding HyperLogLog: Estimating cardinality in a data stream Yousra Chabchoub, Georges Hébrail To cite this version: Yousra Chabchoub, Georges Hébrail. Sliding HyperLogLog: Estimating cardinality in a

More information

Adaptive Parallel Compressed Event Matching

Adaptive Parallel Compressed Event Matching Adaptive Parallel Compressed Event Matching Mohammad Sadoghi 1,2 Hans-Arno Jacobsen 2 1 IBM T.J. Watson Research Center 2 Middleware Systems Research Group, University of Toronto April 2014 Mohammad Sadoghi

More information

Policy-preserving Middlebox Placement in SDN-Enabled Data Centers

Policy-preserving Middlebox Placement in SDN-Enabled Data Centers Policy-preserving Middlebox Placement in SDN-Enabled Data Centers Bin Tang Computer Science Department California State University Dominguez Hills Some slides are from www.cs.berkeley.edu/~randy/courses/cs268.f08/lectures/22-

More information

Lecture 13: Tracking mo3on features op3cal flow

Lecture 13: Tracking mo3on features op3cal flow Lecture 13: Tracking mo3on features op3cal flow Professor Fei- Fei Li Stanford Vision Lab Lecture 13-1! What we will learn today? Introduc3on Op3cal flow Feature tracking Applica3ons (Problem Set 3 (Q1))

More information

OTN Technology, Standards and Applica7ons

OTN Technology, Standards and Applica7ons OTN Technology, Standards and Applica7ons Sco8 T. Wilkinson, PhD Sr. Director, Technical Marke3ng swilkinson@mrv.com 1 Agenda MRV Company introduc3on OTN Technology and Standards MRV OTN, packet and op3cal

More information

Using the Cray Gemini Performance Counters

Using the Cray Gemini Performance Counters Photos placed in horizontal position with even amount of white space between photos and header Using the Cray Gemini Performance Counters 0 1 2 3 4 5 6 7 Backplane Backplane 8 9 10 11 12 13 14 15 Backplane

More information

Alignment and Image Comparison

Alignment and Image Comparison Alignment and Image Comparison Erik Learned- Miller University of Massachuse>s, Amherst Alignment and Image Comparison Erik Learned- Miller University of Massachuse>s, Amherst Alignment and Image Comparison

More information

Indexing and Searching

Indexing and Searching Indexing and Searching Introduction How to retrieval information? A simple alternative is to search the whole text sequentially Another option is to build data structures over the text (called indices)

More information

Donato Ba*aglino Lorenzo Bracciale

Donato Ba*aglino Lorenzo Bracciale IP Donato Ba*aglino Lorenzo Bracciale Outline why IP (mo:va:on) IP architecture (router, LAN) IP addressing Sta:c IP (CIDR, host + net) DHCP Rou:ng IP ARP Why IP? There are many different LAN technologies

More information

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY Gayatri Chavan,, 2013; Volume 1(8): 832-841 T INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK RECTIFIED PROBABILISTIC PACKET MARKING

More information

Monitoring & Analy.cs Working Group Ini.a.ve PoC Setup & Guidelines

Monitoring & Analy.cs Working Group Ini.a.ve PoC Setup & Guidelines Monitoring & Analy.cs Working Group Ini.a.ve PoC Setup & Guidelines Copyright 2017 Open Networking User Group. All Rights Reserved Confiden@al Not For Distribu@on Outline ONUG PoC Right Stuff Innova@on

More information

Fast and Accurate Traffic Matrix Measurement Using Adaptive Cardinality Counting

Fast and Accurate Traffic Matrix Measurement Using Adaptive Cardinality Counting Fast and Accurate Traffic Matrix Measurement Using Adaptive Cardinality Counting Min Cai Jianping Pan Yu-Kwong Kwok Kai Hwang University of Southern California NTT MCL {mincai,kaihwang}@usc.edu panjianping@acm.org

More information

A scalability comparison study of data management approaches for smart metering systems

A scalability comparison study of data management approaches for smart metering systems A scalability comparison study of data management approaches for smart metering systems Houssem Chihoub, Chris.ne Collet Grenoble INP houssem.chihoub@imag.fr Journées Plateformes Clermont Ferrand 6-7 octobre

More information

ECE 1749H: Interconnec1on Networks for Parallel Computer Architectures: Rou1ng. Prof. Natalie Enright Jerger

ECE 1749H: Interconnec1on Networks for Parallel Computer Architectures: Rou1ng. Prof. Natalie Enright Jerger ECE 1749H: Interconnec1on Networks for Parallel Computer Architectures: Rou1ng Prof. Natalie Enright Jerger Rou1ng Overview Discussion of topologies assumed ideal rou1ng In prac1ce Rou1ng algorithms are

More information

Minimizing Collateral Damage by Proactive Surge Protection

Minimizing Collateral Damage by Proactive Surge Protection Minimizing Collateral Damage by Proactive Surge Protection Jerry Chou, Bill Lin University of California, San Diego Subhabrata Sen, Oliver Spatscheck AT&T Labs-Research ACM SIGCOMM LSAD Workshop, Kyoto,

More information

Verifiable Cloud Outsourcing for Network Func9ons (+ Verifiable Resource Accoun9ng for Cloud Services)

Verifiable Cloud Outsourcing for Network Func9ons (+ Verifiable Resource Accoun9ng for Cloud Services) 1 Verifiable Cloud Outsourcing for Network Func9ons (+ Verifiable Resource Accoun9ng for Cloud Services) Vyas Sekar vnfo joint with Seyed Fayazbakhsh, Mike Reiter VRA joint with Chen Chen, Petros Mania9s,

More information

Link Layer. w/ much credit to Cisco CCNA and Rick Graziani (Cabrillo)

Link Layer. w/ much credit to Cisco CCNA and Rick Graziani (Cabrillo) Link Layer w/ much credit to Cisco CCNA and Rick Graziani (Cabrillo) Administra>via How are the labs going? Telnet- ing into Linux as root In /etc/pam.d/remote comment out line auth required pam_securely.so

More information

Design and Implementa/on of a Consolidated Middlebox Architecture. Vyas Sekar Sylvia Ratnasamy Michael Reiter Norbert Egi Guangyu Shi

Design and Implementa/on of a Consolidated Middlebox Architecture. Vyas Sekar Sylvia Ratnasamy Michael Reiter Norbert Egi Guangyu Shi Design and Implementa/on of a Consolidated Middlebox Architecture Vyas Sekar Sylvia Ratnasamy Michael Reiter Norbert Egi Guangyu Shi 1 Need for Network Evolu/on New applica/ons Evolving threats Performance,

More information

TerraSwarm. A Machine Learning and Op0miza0on Toolkit for the Swarm. Ilge Akkaya, Shuhei Emoto, Edward A. Lee. University of California, Berkeley

TerraSwarm. A Machine Learning and Op0miza0on Toolkit for the Swarm. Ilge Akkaya, Shuhei Emoto, Edward A. Lee. University of California, Berkeley TerraSwarm A Machine Learning and Op0miza0on Toolkit for the Swarm Ilge Akkaya, Shuhei Emoto, Edward A. Lee University of California, Berkeley TerraSwarm Tools Telecon 17 November 2014 Sponsored by the

More information

Natural Scene Sta,s,cs of Color and Range. Che- Chun Su, Lawrence K. Cormack, and Alan C. Bovik

Natural Scene Sta,s,cs of Color and Range. Che- Chun Su, Lawrence K. Cormack, and Alan C. Bovik Natural Scene Sta,s,cs of Color and Range Che- Chun Su, Lawrence K. Cormack, and Alan C. Bovik Mo,va,on Color and range/depth play important roles in natural scenes and human vision systems. Percep,on

More information

Master Course Computer Networks IN2097

Master Course Computer Networks IN2097 Chair for Network Architectures and Services Prof. Carle Department for Computer Science TU München Master Course Computer Networks IN2097 Prof. Dr.-Ing. Georg Carle Christian Grothoff, Ph.D. Dr. Nils

More information

Introduc)on to Computer Networks

Introduc)on to Computer Networks Introduc)on to Computer Networks COSC 4377 Lecture 7 Spring 2012 February 8, 2012 Announcements HW3 due today Start working on HW4 HW5 posted In- class student presenta)ons No TA office hours this week

More information

Mul7cast protocols. IP Mul7cast and IGMP SRM (Scalable Reliable Mul7cast) PGM (Pragma7c General Mul7cast)

Mul7cast protocols. IP Mul7cast and IGMP SRM (Scalable Reliable Mul7cast) PGM (Pragma7c General Mul7cast) IP ANYCAST and MULTICAST; OVERLAYS and UNDERLAYS 1 IP Anycast Outline today Mul7cast protocols IP Mul7cast and IGMP SRM (Scalable Reliable Mul7cast) PGM (Pragma7c General Mul7cast) Overlay networks Tunnels

More information

Sec$on 4: Parallel Algorithms. Michelle Ku8el

Sec$on 4: Parallel Algorithms. Michelle Ku8el Sec$on 4: Parallel Algorithms Michelle Ku8el mku8el@cs.uct.ac.za The DAG, or cost graph A program execu$on using fork and join can be seen as a DAG (directed acyclic graph) Nodes: Pieces of work Edges:

More information

Effec%ve Replica Maintenance for Distributed Storage Systems

Effec%ve Replica Maintenance for Distributed Storage Systems Effec%ve Replica Maintenance for Distributed Storage Systems USENIX NSDI2006 Byung Gon Chun, Frank Dabek, Andreas Haeberlen, Emil Sit, Hakim Weatherspoon, M. Frans Kaashoek, John Kubiatowicz, and Robert

More information