'Heavy tailed' presentation slideshows

1.49k views • 133 slides

Network Simulation and Testing

Network Simulation and Testing. Polly Huang EE NTU http://cc.ee.ntu.edu.tw/~phuang phuang@cc.ee.ntu.edu.tw. Traffic Papers. V. Paxson, and S. Floyd, Wide-Area Traffic: The Failure of Poisson Modeling. IEEE/ACM Transactions on Networking, Vol. 3 No. 3, pp. 226-244, June 1995

754 views • 66 slides

Network Simulation and Testing

Network Simulation and Testing. Polly Huang EE NTU http://cc.ee.ntu.edu.tw/~phuang phuang@cc.ee.ntu.edu.tw. Traffic Papers. V. Paxson, and S. Floyd, Wide-Area Traffic: The Failure of Poisson Modeling. IEEE/ACM Transactions on Networking, Vol. 3 No. 3, pp. 226-244, June 1995

727 views • 65 slides

Social Network Analysis

Social Network Analysis. Social Network Introduction Statistics and Probability Theory Models of Social Network Generation Networks in Biological System Mining on Social Network Summary. Society. Nodes : individuals Links : social relationship (family/work/friendship/etc.).

764 views • 54 slides

Social Network Analysis

Social Network Analysis. Social Network Introduction Statistics and Probability Theory Models of Social Network Generation Networks in Biological System Mining on Social Network Summary. Society. Nodes : individuals Links : social relationship (family/work/friendship/etc.).

1.62k views • 53 slides

Identify Phase Transitions In Problem Hardness Leverage Randomization In Computation

Combinatorial Problems in Cooperative Control: Complexity and Scalability Carla P. Gomes and Bart Selman Cornell University Muri Meeting June 2002. Overview. Overall Approach. Goal. Identify Phase Transitions In Problem Hardness Leverage Randomization In Computation.

622 views • 48 slides

Energy procurement in the presence of intermittent sources

494 views • 38 slides

Social Network Analysis and Mining

Social Network Analysis and Mining. CENG 514. Social Network Analysis. Social Network Introduction Statistics and Probability Theory Models of Social Network Generation Mining on Social Network. Society. Nodes : individuals Links : social relationship (family/work/friendship/etc.).

1.37k views • 35 slides

Balaji Prabhakar

Coping with (exploiting) heavy tails. Balaji Prabhakar Departments of EE and CS Stanford University balaji@stanford.edu. Balaji Prabhakar. Overview. SIFT: Asimple algorithm for identifying large flows reducing average flow delays with smaller router buffers

534 views • 32 slides

Network Traffic Characteristics

Network Traffic Characteristics. Outline Motivation Self-similarity Ethernet traffic WAN traffic Web traffic. Motivation for Network Traffic Study. Understanding network traffic behavior is essential for all aspects of network design and operation Component design Protocol design

558 views • 31 slides

Network Traffic Characteristics

Network Traffic Characteristics. Outline Motivation Self-similarity Ethernet traffic WAN traffic Web traffic. Motivation for Network Traffic Study. Understanding network traffic behavior is essential for all aspects of network design and operation Component design Protocol design

329 views • 31 slides

Huai Huang Dept. of Electronic Engineering Queen Mary, University of London

Analysis of self-similar Traffic Using Multiplexer & Demultiplexer Loaded with Heterogeneous ON/OFF Sources. Huai Huang Dept. of Electronic Engineering Queen Mary, University of London. Overview. Background Knowledge Motivation & Model description Results & Analysis

473 views • 30 slides

Origins of Long Range Dependence Myths and Legends

Origins of Long Range Dependence Myths and Legends. Aleksandar Kuzmanovic 01/08/2001. Outline. Definitions Why is LRD important? Heavy tails Producing self-similar traffic Physical interpretation in LAN and WAN networks Different hypothesis from around 10 papers.

385 views • 28 slides

Task Assignment with Unknown Duration

208 views • 20 slides

Affiliation Networks

Affiliation Networks. Silvio Lattanzi , D. Sivakumar. Under the guidance of: Augustin Chaintreau. Presented By: Aditi Bhatnagar. Objective. To provide a simple, realistic and Mathematically tractable model which are algorithmically useful and:-

451 views • 19 slides

Chapters 9

Chapters 9. Self-Similar Traffic. Introduction- Motivation. Validity of the queuing models we have studied depends on the Poisson nature of data traffic Recent studies show that Internet traffic patterns are often bursty and self-similar , not Poisson (exponential)

361 views • 19 slides

Graphs over time: densification laws, shrinking diameters and possible explanations

374 views • 16 slides

Self Similarity in World Wide Web: Traffic Evidence and Possible Causes

Self Similarity in World Wide Web: Traffic Evidence and Possible Causes. Mark E. Crovella and Azer Bestavros Computer Science Dept, Boston University. Presented by Kalyan Boggavarapu CSC 497 Lehigh University. Self-Similarity.

297 views • 16 slides

Topics in Random Processes

303 views • 15 slides

Opinionated

247 views • 11 slides