Performance Analysis of a Cooperative Flow Game Algorithm in Ad Hoc Networks and a Comparison to Dijkstra’S Algorithm
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A Survey of Network Performance Monitoring Tools
A Survey of Network Performance Monitoring Tools Travis Keshav -- [email protected] Abstract In today's world of networks, it is not enough simply to have a network; assuring its optimal performance is key. This paper analyzes several facets of Network Performance Monitoring, evaluating several motivations as well as examining many commercial and public domain products. Keywords: network performance monitoring, application monitoring, flow monitoring, packet capture, sniffing, wireless networks, path analysis, bandwidth analysis, network monitoring platforms, Ethereal, Netflow, tcpdump, Wireshark, Ciscoworks Table Of Contents 1. Introduction 2. Application & Host-Based Monitoring 2.1 Basis of Application & Host-Based Monitoring 2.2 Public Domain Application & Host-Based Monitoring Tools 2.3 Commercial Application & Host-Based Monitoring Tools 3. Flow Monitoring 3.1 Basis of Flow Monitoring 3.2 Public Domain Flow Monitoring Tools 3.3 Commercial Flow Monitoring Protocols 4. Packet Capture/Sniffing 4.1 Basis of Packet Capture/Sniffing 4.2 Public Domain Packet Capture/Sniffing Tools 4.3 Commercial Packet Capture/Sniffing Tools 5. Path/Bandwidth Analysis 5.1 Basis of Path/Bandwidth Analysis 5.2 Public Domain Path/Bandwidth Analysis Tools 6. Wireless Network Monitoring 6.1 Basis of Wireless Network Monitoring 6.2 Public Domain Wireless Network Monitoring Tools 6.3 Commercial Wireless Network Monitoring Tools 7. Network Monitoring Platforms 7.1 Basis of Network Monitoring Platforms 7.2 Commercial Network Monitoring Platforms 8. Conclusion 9. References and Acronyms 1.0 Introduction http://www.cse.wustl.edu/~jain/cse567-06/ftp/net_perf_monitors/index.html 1 of 20 In today's world of networks, it is not enough simply to have a network; assuring its optimal performance is key. -
An Introduction
This chapter is from Social Media Mining: An Introduction. By Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. Cambridge University Press, 2014. Draft version: April 20, 2014. Complete Draft and Slides Available at: http://dmml.asu.edu/smm Chapter 10 Behavior Analytics What motivates individuals to join an online group? When individuals abandon social media sites, where do they migrate to? Can we predict box office revenues for movies from tweets posted by individuals? These questions are a few of many whose answers require us to analyze or predict behaviors on social media. Individuals exhibit different behaviors in social media: as individuals or as part of a broader collective behavior. When discussing individual be- havior, our focus is on one individual. Collective behavior emerges when a population of individuals behave in a similar way with or without coordi- nation or planning. In this chapter we provide examples of individual and collective be- haviors and elaborate techniques used to analyze, model, and predict these behaviors. 10.1 Individual Behavior We read online news; comment on posts, blogs, and videos; write reviews for products; post; like; share; tweet; rate; recommend; listen to music; and watch videos, among many other daily behaviors that we exhibit on social media. What are the types of individual behavior that leave a trace on social media? We can generally categorize individual online behavior into three cate- gories (shown in Figure 10.1): 319 Figure 10.1: Individual Behavior. 1. User-User Behavior. This is the behavior individuals exhibit with re- spect to other individuals. For instance, when befriending someone, sending a message to another individual, playing games, following, inviting, blocking, subscribing, or chatting, we are demonstrating a user-user behavior. -
Print Prt489138397779225351.Tif (16 Pages)
U.S. Department ofHo1Deland.se¢urUy U.S. Citizenship and Immigration Services Administrative Appeals Office (AAO) 20 Massachusetts Ave., N.W., MS 2090 Washington, DC 20529-2090 (b)(6) U.S. Citizenship and Immigration Services DATE : APR 1 7 2015 OFFICE: CALIFORNIA SERVICE CENTER FILE: INR E: Petitioner: Benef icia ry: PETITION: Petition for a Nonim migrant Worker Pursuant to Section 101(a)(15)(H)(i)(b) of the Immigration and Nationality Act, 8 U.S.C. § 1101(a)(15)(H)(i)(b) ON BEHALF OF PETITIONER : INSTRUCTIONS: Enclosed please find the decision of the Administrative Appeals Office (AAO) in y our case. This is a n on-pre cedent decision. The AAO does not announce new constructions of law nor establish agency policy through non-precedent decisions. I f you believe the AAO incorrectly applied current law or policy to your case or if you seek to present new facts for consideration, you may file a motion to reconsider or a motion to reopen, respectively. Any motion must be filed on a Notice of Appeal or Motion (Form I-290B) within 33 days of the date of this decision. Please review the Form I-290B instructions at http://www.uscis.gov/forms for the latest information on fee, filing location, and other requirements. See also 8 C.F.R. § 103.5. Do not file a motion directly with the AAO. Ron Rose rg Chief, Administrative Appeals Office www.uscis.gov (b)(6) NON-PRECEDENTDECISION Page2 DISCUSSION: The service center director (hereinafter "director") denied the nonimmigrant visa petition, and the matter is now before the Administrative Appeals Office on appeal. -
Three-Dimensional Integrated Circuit Design: EDA, Design And
Integrated Circuits and Systems Series Editor Anantha Chandrakasan, Massachusetts Institute of Technology Cambridge, Massachusetts For other titles published in this series, go to http://www.springer.com/series/7236 Yuan Xie · Jason Cong · Sachin Sapatnekar Editors Three-Dimensional Integrated Circuit Design EDA, Design and Microarchitectures 123 Editors Yuan Xie Jason Cong Department of Computer Science and Department of Computer Science Engineering University of California, Los Angeles Pennsylvania State University [email protected] [email protected] Sachin Sapatnekar Department of Electrical and Computer Engineering University of Minnesota [email protected] ISBN 978-1-4419-0783-7 e-ISBN 978-1-4419-0784-4 DOI 10.1007/978-1-4419-0784-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009939282 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Foreword We live in a time of great change. -
Learning to Predict End-To-End Network Performance
CORE Metadata, citation and similar papers at core.ac.uk Provided by BICTEL/e - ULg UNIVERSITÉ DE LIÈGE Faculté des Sciences Appliquées Institut d’Électricité Montefiore RUN - Research Unit in Networking Learning to Predict End-to-End Network Performance Yongjun Liao Thèse présentée en vue de l’obtention du titre de Docteur en Sciences de l’Ingénieur Année Académique 2012-2013 ii iii Abstract The knowledge of end-to-end network performance is essential to many Internet applications and systems including traffic engineering, content distribution networks, overlay routing, application- level multicast, and peer-to-peer applications. On the one hand, such knowledge allows service providers to adjust their services according to the dynamic network conditions. On the other hand, as many systems are flexible in choosing their communication paths and targets, knowing network performance enables to optimize services by e.g. intelligent path selection. In the networking field, end-to-end network performance refers to some property of a network path measured by various metrics such as round-trip time (RTT), available bandwidth (ABW) and packet loss rate (PLR). While much progress has been made in network measurement, a main challenge in the acquisition of network performance on large-scale networks is the quadratical growth of the measurement overheads with respect to the number of network nodes, which ren- ders the active probing of all paths infeasible. Thus, a natural idea is to measure a small set of paths and then predict the others where there are no direct measurements. This understanding has motivated numerous research on approaches to network performance prediction. -
Designing Vertical Bandwidth Reconfigurable 3D Nocs for Many
Designing Vertical Bandwidth Reconfigurable 3D NoCs for Many Core Systems Qiaosha Zou∗, Jia Zhany, Fen Gez, Matt Poremba∗, Yuan Xiey ∗ Computer Science and Engineering, The Pennsylvania State University, USA y Electrical and Computer Engineering, University of California, Santa Barbara, USA z Nanjing University of Aeronautics and Astronautics, China Email: ∗fqszou, [email protected], yfjzhan, [email protected], z [email protected] Abstract—As the number of processing elements increases in a single Routers chip, the interconnect backbone becomes more and more stressed when Core 1 Cache/ Cache/Memory serving frequent memory and cache accesses. Network-on-Chip (NoC) Memory has emerged as a potential solution to provide a flexible and scalable Core 0 interconnect in a planar platform. In the mean time, three-dimensional TSVs (3D) integration technology pushes circuit design beyond Moore’s law and provides short vertical connections between different layers. As a result, the innovative solution that combines 3D integrations and NoC designs can further enhance the system performance. However, due Core 0 Core 1 to the unpredictable workload characteristics, NoC may suffer from intermittent congestions and channel overflows, especially when the (a) (b) network bandwidth is limited by the area and energy budget. In this work, we explore the performance bottlenecks in 3D NoC, and then leverage Fig. 1. Overview of core-to-cache/memory 3D stacking. (a). Cores are redundant TSVs, which are conventionally used for fault tolerance only, allocated in all layers with part of cache/memory; (b). All cores are located as vertical links to provide additional channel bandwidth for instant in the same layers while cache/memory in others. -
A Social Network Matrix for Implicit and Explicit Social Network Plates
A Social Network Matrix for Implicit and Explicit Social Network Plates Wei Zhou1;4, Wenjing Duan2, Selwyn Piramuthu3;4 1Information & Operations Management, ESCP Europe, Paris, France 2Information Systems and Technology Management, George Washington University, U.S.A. 3Information Systems and Operations Management, University of Florida, U.S.A. Gainesville, Florida 32611-7169, USA 4RFID European Lab, Paris, France. [email protected], [email protected], selwyn@ufl.edu Abstract A majority of social network research deals with explicitly formed social networks. Although only rarely acknowledged for its existence, we believe that implicit social networks play a significant role in the overall dynamics of social networks. We propose a framework to evalu- ate the dynamics and characteristics of a set of explicit and associated implicit social networks. Specifically, we propose a social network ma- trix to measure the implicit relationships among the entities in various social networks. We also derive several indicators to characterize the dynamics in online social networks. We proceed by incorporating im- plicit social networks in a traditional network flow context to evaluate key network performance indicators such as the lowest communication cost, maximum information flow, and the budgetary constraints. Keywords: Implicit Social Network, Online Social Network 1 1 Introduction In recent years, several online social network platforms have witnessed huge public attention from social and financial perspectives. However, there are different facets to this interest. Facebook, for example, has gained a large set of users but failed to excel on profitability. Online social networks can be broadly classified as explicit and implicit social networks. Explicit social networks (e.g., Facebook, LinkedIn, Twitter, and MySpace) are where the users define the network by explicitly connect- ing with other users, possibly, but not necessarily, based on shared interests. -
ORMS 1020: Operations Research with GNU Octave
ORMS 1020 Operations Research with GNU Octave Tommi Sottinen [email protected] www.uwasa.fi/ tsottine/or_with_octave/ ∼ October 19, 2011 Contents I Introduction and Preliminaries6 1 Selection of Optimization Problems7 1.1 Product Selection Problem.......................7 1.2 Knapsack Problem........................... 10 1.3 Portfolio Selection Problem*...................... 12 1.4 Exercises and Projects......................... 13 2 Short Introduction to Octave 14 2.1 Installing Octave............................ 14 2.2 Octave as Calculator.......................... 15 2.3 Linear Algebra with Octave...................... 18 2.4 Function and Script Files....................... 28 2.5 Octave Programming: glpk Wrapper................. 32 2.6 Exercises and Projects......................... 37 II Linear Programming 39 3 Linear Programs and Their Optima 40 3.1 Form of Linear Program........................ 40 3.2 Location of Linear Programs’ Optima................ 43 3.3 Solution Possibilities of Linear Programs............... 48 3.4 Karush–Kuhn–Tucker Conditions*.................. 53 3.5 Proofs*................................. 54 3.6 Exercises and Projects......................... 56 0.0 CONTENTS 2 4 Simplex Algorithm 58 4.1 Simplex tableaux and General Idea.................. 59 4.2 Top-Level Algorithm.......................... 62 4.3 Initialization Algorithm........................ 66 4.4 Optimality-Checking Algorithm.................... 68 4.5 Tableau Improvement Algorithm................... 71 4.6 Exercises and Projects........................ -
Operations Research for Resource Planning and -Use in Radiotherapy: a Literature Review Bruno Vieira1,2,4*, Erwin W
Vieira et al. BMC Medical Informatics and Decision Making (2016) 16:149 DOI 10.1186/s12911-016-0390-4 RESEARCH ARTICLE Open Access Operations research for resource planning and -use in radiotherapy: a literature review Bruno Vieira1,2,4*, Erwin W. Hans2,3, Corine van Vliet-Vroegindeweij1, Jeroen van de Kamer1 and Wim van Harten1,4,5 Abstract Background: The delivery of radiotherapy (RT) involves the use of rather expensive resources and multi-disciplinary staff. As the number of cancer patients receiving RT increases, timely delivery becomes increasingly difficult due to the complexities related to, among others, variable patient inflow, complex patient routing, and the joint planning of multiple resources. Operations research (OR) methods have been successfully applied to solve many logistics problems through the development of advanced analytical models for improved decision making. This paper presents the state of the art in the application of OR methods for logistics optimization in RT, at various managerial levels. Methods: A literature search was performed in six databases covering several disciplines, from the medical to the technical field. Papers included in the review were published in peer-reviewed journals from 2000 to 2015. Data extraction includes the subject of research, the OR methods used in the study, the extent of implementation according to a six-stage model and the (potential) impact of the results in practice. Results: From the 33 papers included in the review, 18 addressed problems related to patient scheduling (of which 12 focus on scheduling patients on linear accelerators), 8 focus on strategic decision making, 5 on resource capacity planning, and 2 on patient prioritization. -
Social Network Analysis and Information Propagation: a Case Study Using Flickr and Youtube Networks
International Journal of Future Computer and Communication, Vol. 2, No. 3, June 2013 Social Network Analysis and Information Propagation: A Case Study Using Flickr and YouTube Networks Samir Akrouf, Laifa Meriem, Belayadi Yahia, and Mouhoub Nasser Eddine, Member, IACSIT 1 makes decisions based on what other people do because their Abstract—Social media and Social Network Analysis (SNA) decisions may reflect information that they have and he or acquired a huge popularity and represent one of the most she does not. This concept is called ″herding″ or ″information important social and computer science phenomena of recent cascades″. Therefore, analyzing the flow of information on years. One of the most studied problems in this research area is influence and information propagation. The aim of this paper is social media and predicting users’ influence in a network to analyze the information diffusion process and predict the became so important to make various kinds of advantages influence (represented by the rate of infected nodes at the end of and decisions. In [2]-[3], the marketing strategies were the diffusion process) of an initial set of nodes in two networks: enhanced with a word-of-mouth approach using probabilistic Flickr user’s contacts and YouTube videos users commenting models of interactions to choose the best viral marketing plan. these videos. These networks are dissimilar in their structure Some other researchers focused on information diffusion in (size, type, diameter, density, components), and the type of the relationships (explicit relationship represented by the contacts certain special cases. Given an example, the study of Sadikov links, and implicit relationship created by commenting on et.al [6], where they addressed the problem of missing data in videos), they are extracted using NodeXL tool. -
Network Performance Analysis
Network Performance Analysis Guido Sanguinetti (based on notes by Neil Lawrence) Autumn Term 2007-2008 2 Schedule Lectures will take place in the following locations: Mondays 15:10 - 17:00 pm (St Georges LT1). Tuesdays 16:10 pm - 17:00 pm (St Georges LT1). Lab Classes In the Lewin Lab, Regent Court G12 (Monday Weeks 3 and 8). Week 1 Monday Lectures 1 & 2 Networks and Probability Review Tuesday Lecture 3 Poisson Processes Week 2 Monday Lecture 4 & 5 Birth Death Processes, M=M=1 queues Tuesday Lecture 6 Little's Formula Week 3 Monday Lab Class 1 Discrete Simulation of the M=M=1 Queue Tuesday Tutorial 1 Probability and Poisson Processes Week 4 Monday Lectures 7 & 8 Other Markov Queues and Erlang Delay Tuesday Lab Review 1 Review of Lab Class 1 Week 5 Monday Lectures 9 & 10 Erlang Loss and Markov Queues Review Tuesday Tutorial 2 M=M=1 and Little's Formula Week 6 Monday Lectures 11 & 12 M=G=1 and M=G=1 with Vacations Tuesday Lecture 13 M=G=1 contd Week 7 Monday Lectures 14 & 15 Networks of Queues, Jackson's Theorem Tuesday Tutorial 3 Erlang Delay and Loss, M=G=1 Week 8 Monday Lab Class 2 Simulation of Queues in NS2 Tuesday Lecture 16 ALOHA Week 9 Tuesday Lab Review 2 Review of NS2 Lab Class Week 10 Exam Practice Practice 2004-2005 Exam Week 12 Monday Exam Review Review of 2004-2005 Exam Evaluation This course is 100% exam. 3 4 Reading List The course is based primarily on Bertsekas and Gallager [1992]. -
A Framework for a Bandwidth Based Network Performance Model for CS Students
A Framework for a Bandwidth Based Network Performance Model for CS Students D. Veal, G. Kohli, S. P. Maj J. Cooper Edith Cowan University Curtin University of Technology Western Australia Western Australia [email protected] Abstract There are currently various methods by which network and internetwork performance can be addressed. Examples include simulation modeling and analytical modeling which often results in models that are highly complex and often mathematically based (e.g. queuing theory). The authors have developed a new model which is based upon simple formulae derived from an investigation of computer networks. This model provides a conceptual framework for performance analysis based upon bandwidth considerations from a constructivist perspective. The bandwidth centric model uses high level abstraction decoupled from the implementation details of the underlying technology. This paper represents an initial attempt to develop this theory further in the field of networking education and presents a description of some of our work to date. This paper also includes details of experiments undertaken to measure bandwidth and formulae derived and applied to the investigation of converging data streams. Introduction There are many ways of approaching computer networking education. Davies notes that: “Network courses are often based on one or more of the following areas: The OSI model; Performance analysis; and Network simulation” 1. The OSI model is a popular approach that is used extensively in the Cisco Networking Academy Program (CNAP) 2 and in other Cisco learning materials. With respect to simulation Davis describes the Optimized Network Engineering Tools (OPNET) system that that can model networks and sub-networks, individual nodes and stations and state transition models that defines a node 1.