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Study Documents » California Colleges & Universities » Caltech » Computer Science (CS) » CS 147
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69 sample documents related to CS 147

  • fairnesssurvey
    Caltech CS 147
    Fairness and Classications Adam Wierman Computer Science Department Carnegie Mellon University Pittsburgh, PA 15217 acw@cs.cmu.edu ABSTRACT The growing trend in computer systems towards using scheduling policies that prioritize jobs with small service req
     
  • fb-survey
    Caltech CS 147
    The Foreground-Background queue: a survey Misja Nuyens Adam Wierman September 12, 2007 Abstract Computer systems researchers have begun to apply the Foreground-Background (FB) scheduling discipline to a variety of applications, and as a result, there has
     
  • hw1
    Caltech CS 147
    CS/EE 147 Assigned: 03/30/10 HW 1: Probability Refresher Guru: Raga Due: 04/09/10, Ragas mailbox1 , 1pm We encourage you to discuss these problems with others, but you need to write up the actual solutions alone. At the top of your homework sheet, list al
     
  • hw2
    Caltech CS 147
    HW 2: Practice with DTMCs CS/EE 147 Assigned: 04/06/10 Guru: Lina Due: 04/16/10, Ragas mailbox, 1pm We encourage you to discuss these problems with others, but you need to write up the actual solutions alone. At the top of your homework sheet, list all th
     
  • hw3
    Caltech CS 147
    CS/EE 147 Assigned: 4/13/10 HW 3: Practice with CTMCs Guru: Raga Due: 4/28/10, Ragas mailbox, 1pm We encourage you to discuss these problems with others, but you need to write up the actual solutions alone. At the top of your homework sheet, list all the
     
  • hw4
    Caltech CS 147
    CS/EE 147 Assigned: 04/27/10 HW 4: Queueing games Guru: Lina Due: 05/07/10, Ragas mailbox, 1pm We encourage you to discuss these problems with others, but you need to write up the actual solutions alone. At the top of your homework sheet, list all the peo
     
  • hw5
    Caltech CS 147
    CS/EE 147 Assigned: 05/06/10 HW 5: Queueing networks and PH distributions Guru: Raga Due: 05/14/10, Ragas mailbox, 1pm We encourage you to discuss these problems with others, but you need to write up the actual solutions alone. At the top of your homework
     
  • hw6
    Caltech CS 147
    CS/EE 147 Assigned: 05/13/10 HW 6: Transform world Guru: Lina Due: 05/26/10, Ragas mailbox, 1pm We encourage you to discuss these problems with others, but you need to write up the actual solutions alone. At the top of your homework sheet, list all the pe
     
  • hw7
    Caltech CS 147
    CS/EE 147 Assigned: 05/25/10 HW 7: Scheduling Guru: Raga Due: 06/04/10, Ragas mailbox, 1pm We encourage you to discuss these problems with others, but you need to write up the actual solutions alone. At the top of your homework sheet, list all the people
     
  • queueing copy
    Caltech CS 147
    Queueing Theory Ivo Adan and Jacques Resing Department of Mathematics and Computing Science Eindhoven University of Technology P.O. Box 513, 5600 MB Eindhoven, The Netherlands February 28, 2002 Contents 1 Introduction 1.1 Examples . . . . . . . . . . . .
     
  • review
    Caltech CS 147
    Review 147 Page 1 147 Page 2 147 Page 3 147 Page 4 147 Page 5
     
  • search
    Caltech CS 147
    Eciency and Revenue in Certain Nash Equilibria of Keyword Auctions Sbastien Lahaie e lahaies@yahoo-inc.com Yahoo Research New York, NY 10018 SISHOO 2007 p.1 Sponsored Search SISHOO 2007 p.2 Outline Model for keyword auctions. Eciency in pure-strategy Nas
     
  • search2
    Caltech CS 147
    Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords By BENJAMIN EDELMAN, MICHAEL OSTROVSKY, AND MICHAEL SCHWARZ* We investigate the generalized second-price (GSP) auction, a new mechanism used by se
     
  • search3
    Caltech CS 147
    Revenue Analysis of a Family of Ranking Rules for Keyword Auctions Sebastien Lahaie David M. Pennock School of Engineering and Applied Sciences Harvard University, Cambridge, MA 02138 Yahoo! Research New York, NY 10011 slahaie@eecs.harvard.edu pennockd@ya
     
  • smarteps-sigmetrics
    Caltech CS 147
    Scheduling despite inexact job-size information Adam Wierman Misja Nuyens California Institute of Technology 1200 E. California Blvd. Pasadena, CA 91125 Statkraft Lilleakerveien 6 Lilleaker, 0216 Oslo acw@caltech.edu misjanuyens@gmail.com ABSTRACT Motivat
     
  • thesis-proposal
    Caltech CS 147
    THESIS PROPOSAL A Theoretical Scheduling Toolbox Adam Wierman CMU-CS-05-? School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract Scheduling policies are fundamental components of a majority of modern computer systems. However,
     
  • VT_gittins
    Caltech CS 147
    Optimal scheduling of jobs with a DHR tail in the M/G/1 queue TKK Helsinki University of Technology Department of Communications and Networking P.O.Box 3000 02015 TKK Finland Samuli Aalto samuli.aalto@tkk.fi LAAS-CNRS Universit de Toulouse 7 Avenue Colone
     
  • learning-s
    Caltech CS 147
    Joint Strategy Fictitious Play Sherwin Doroudi \"Adapted\" from J. R. Marden, G. Arslan, J. S. Shamma, \"Joint strategy fictitious play with inertia for potential games,\" in Proceedings of the 44th IEEE Conference on Decision and Control, December 2005, pp.
     
  • pred-d
    Caltech CS 147
    Paul Milgrom and Nancy Stokey Journal of Economic Thoery,1982 Motivation Model I Model II There are L commodities in each state of the world. Assume consumption set is RL+. a Each trader i is described by: his endowment, ei: RL+ his utility function, Ui
     
  • search-c
    Caltech CS 147
    Sponsored Search Cory Pender Sherwin Doroudi Optimal Delivery of Sponsored Search Advertisements Subject to Budget Constraints Zoe Abrams Ofer Mendelevitch John A. Tomlin Introduction Search engines (Google, Yahoo!, MSN) auction off advertisement slots o
     
  • search-s
    Caltech CS 147
    Mediators Slides by Sherwin Doroudi Adapted from Mediators in Position Auctions by Itai Ashlagi, Dov Monderer, and Moshe Tennenholtz Bayesian & Pre-Bayesian Games Consider a game where every player has private information regarding his/her type A player
     
  • ton97.pdf
    Caltech CS 147
    IEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 5, NO. 6, DECEMBER 1997 835 Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes Mark E. Crovella, Member, IEEE, and Azer Bestavros, Member, IEEE Abstract-Recently, the notion of self-sim
     
  • lecture-01.pdf
    Caltech CS 147
    lecture 1 Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6 Perf Modeling Page 7 Perf Modeling Page 8 Perf Modeling Page 9 Perf Modeling Page 10 Perf Modeling Page
     
  • lecture-02a.pdf
    Caltech CS 147
    Lecture 2a Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6 Perf Modeling Page 7 Perf Modeling Page 8
     
  • lecture-02b.pdf
    Caltech CS 147
    Lecture 2b . Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6
     
  • lecture-03.pdf
    Caltech CS 147
    Lecture 3 Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6 Perf Modeling Page 7 Perf Modeling Page 8 Perf Modeling Page 9 Perf Modeling Page 10 Perf Modeling Page
     
  • lecture-04.pdf
    Caltech CS 147
    Lecture 4 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6
     
  • DeeperInsidePR.pdf
    Caltech CS 147
    Deeper Inside PageRank Amy N. Langville and Carl D. Meyer October 20, 2004 Abstract This paper serves as a companion or extension to the \"Inside PageRank\" paper by Bianchini et al. [19]. It is a comprehensive survey of all issues associated with Pag
     
  • lecture-05.pdf
    Caltech CS 147
    Lecture 5 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11 CS 286a Page 12 CS 286a Page 13 CS 286a Page 14 CS 286a Pa
     
  • lecture-06.pdf
    Caltech CS 147
    Lecture 6 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10
     
  • lecture-07.pdf
    Caltech CS 147
    Lecture 7 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11 CS 286a Page 12
     
  • lecture-08b.pdf
    Caltech CS 147
    Lecture 8b CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11 CS 286a Page 12
     
  • lecture-09a.pdf
    Caltech CS 147
    L Lecture 9 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6
     
  • lecture-09b.pdf
    Caltech CS 147
    CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11 CS 286a Page 12 CS 286a Page 13 CS 286a Page 14 CS 286a Page 15 CS 286a Page 16 CS 286a Page 17
     
  • lecture-10.pdf
    Caltech CS 147
    Lecture 10 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11
     
  • lecture-12.pdf
    Caltech CS 147
    Lecture 12 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11
     
  • lecture-13.pdf
    Caltech CS 147
    Lecture 13 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11 CS 286a Page 12 CS 286a Page 13 CS 286a Page 14 CS 286a P
     
  • lecture-14.pdf
    Caltech CS 147
    Lecture 14 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10
     
  • lecture-15.pdf
    Caltech CS 147
    Lecture 15 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6 CS 286a Page 7 CS 286a Page 8 CS 286a Page 9 CS 286a Page 10 CS 286a Page 11
     
  • lecture-16.pdf
    Caltech CS 147
    Review CS 286a Page 1 CS 286a Page 2 CS 286a Page 3
     
  • winter-1.pdf
    Caltech CS 147
    Lecture 1 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5 286b Page 6 286b Page 7 286b Page 8 286b Page 9
     
  • winter-2.pdf
    Caltech CS 147
    Lecture 2 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5 286b Page 6
     
  • winter-3.pdf
    Caltech CS 147
    Lecture 3 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5 286b Page 6 286b Page 7 286b Page 8 286b Page 9 286b Page 10 286b Page 11 286b Page 12 286b Page 13
     
  • winter-4.pdf
    Caltech CS 147
    Lecture 4 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5 286b Page 6 286b Page 7
     
  • urtzi-conservation.pdf
    Caltech CS 147
    GI/GI/1
     
  • winter-5.pdf
    Caltech CS 147
    Lecture 5 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5
     
  • winter-6.pdf
    Caltech CS 147
    Lecture 6 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5
     
  • winter-7.pdf
    Caltech CS 147
    Lecture 7 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5 286b Page 6
     
  • 2008-UCSD-helpthelittleguy-1.pdf
    Caltech CS 147
    Doeshelpingthelittleguyhelpeveryone? Adam Wierman > Caltech BabyScheduling response time What policy minimizes mean response time, E[T]? optimal E[T] mean response time SRPT Shortest Remaining Processing Time SRPTwinsbig PS scheduling in real
     
  • winter-8.pdf
    Caltech CS 147
    Lecture 8 286b Page 1 286b Page 2 286b Page 3 286b Page 4
     
  • minghong.pdf
    Caltech CS 147
    Fairness and efficiency in web server protocols MinghongLin (Presentation for CS286b) 1 Motivation This paper is motivated by the question: For a server under Processor Sharing policy, Is it possible to reduce the response time of every job, simply
     
  • matthew.pdf
    Caltech CS 147
    Gittins Policy on NBUE + DHR(k ) Job Sizes Matthew Maurer Performance Modeling, 2009 Matthew Maurer () Gittins Policy CS 286.2b, 2009 1 / 25 Outline 1 Gittins Policy Gittins Index Gittins Policy Application 2 NBUE + DHR(k ) Distributions Gi
     
  • raga.pdf
    Caltech CS 147
     
     
  • zwart-lecturenotes.pdf
    Caltech CS 147
    RARE EVENTS AND HEAVY TAILS IN STOCHASTIC SYSTEMS BERT ZWART Abstract. This is a set of lecture notes on large deviations and heavy tails in stochastic systems. After an introduction, a short and biased review on heavy-tailed distribution is given.
     
  • stabledistributions.pdf
    Caltech CS 147
    Stable Distributions Models for Heavy Tailed Data John P. Nolan jpnolan@american.edu Math/Stat Department American University Copyright c 2008 John P. Nolan Processed April 8, 2008 ii Contents I Univariate Stable 1 Basic Properties of Univariate S
     
  • carey-webworkloads.pdf
    Caltech CS 147
    Chapter 1 WEB WORKLOAD CHARACTERIZATION: TEN YEARS LATER Adepele Williams, Martin Arlitt, Carey Williamson, and Ken Barker Department of Computer Science, University of Calgary 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 {awilliam,arlitt,c
     
  • elizabeth.pdf
    Caltech CS 147
    Network Models: Pick your characteristic Elizabeth Bodine CS 286B 2/26/09 Outline Motivation: Why do we care about models? A first random graph model Preferential Attachment (the rich get richer) Social Network Models (the highlights) Conclusio
     
  • jk.pdf
    Caltech CS 147
    \'I) 0 lAh ~ ntlN-!>t ~ - ~ ~<!-,;5 J:},c:l ;0 s-r vh ow Jxr u>L ~ ;:;,)1l\'1 IJl f\\ ,L; Qf) v9 hdL ~\"th 2:J 0. cr, cf\' pa.nk J ~h H8 _ 1< [\" Su (~roe. cf\'1w.t-) _ 1(?UIC- (~ ~W\'o cfh CJf\'O<!-S bo.J<. U-f\' ~ cf 0 in&p
     
  • generatingpowerlaws.pdf
    Caltech CS 147
    Internet Mathematics Vol. 1, No. 2: 226-251 A Brief History of Generative Models for Power Law and Lognormal Distributions Michael Mitzenmacher Abstract. Recently, I became interested in a current debate over whether le size distributions are best
     
  • winter-10.pdf
    Caltech CS 147
    Lecture 10 286b Page 1 286b Page 2 286b Page 3 286b Page 4 286b Page 5 286b Page 6 286b Page 7 286b Page 8 286b Page 9 286b Page 10 286b Page 11 286b Page 12 286b Page 13 286b Page 14 286b Page 15 286b Page 16 286b Page 17
     
  • largedeviations.pdf
    Caltech CS 147
    An Introduction to Large Deviations for Teletrac Engineers John T. Lewis1 Raymond Russell1 November 1, 1997 1 What Large Deviation Theory is About Roughly speaking, Large Deviations is a theory of rare events. It is probably the most active eld in
     
  • elizabeth.ppt
    Caltech CS 147
    NetworkModels: Pickyourcharacteristic ElizabethBodine CS286B 2/26/09 Outline Motivation:Whydowecareaboutmodels? Afirstrandomgraphmodel PreferentialAttachment(therichgetricher) SocialNetworkModels(thehighlights) Conclusions Whydowecare? Ingene
     
  • generatingHTs.pdf
    Caltech CS 147
    Internet Mathematics Vol. 1, No. 2: 226-251 A Brief History of Generative Models for Power Law and Lognormal Distributions Michael Mitzenmacher Abstract. Recently, I became interested in a current debate over whether le size distributions are best
     
  • lecture-02.pdf
    Caltech CS 147
    Lecture 2 Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6 Perf Modeling Page 7 Perf Modeling Page 8
     
  • lecture-2.pdf
    Caltech CS 147
    Lecture 2 Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6 Perf Modeling Page 7 Perf Modeling Page 8
     
  • lecture-2a.pdf
    Caltech CS 147
    Lecture 2a Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6 Perf Modeling Page 7 Perf Modeling Page 8
     
  • lecture-2b.pdf
    Caltech CS 147
    Lecture 2b . Perf Modeling Page 1 Perf Modeling Page 2 Perf Modeling Page 3 Perf Modeling Page 4 Perf Modeling Page 5 Perf Modeling Page 6
     
  • lecture-4.pdf
    Caltech CS 147
    Lecture 4 CS 286a Page 1 CS 286a Page 2 CS 286a Page 3 CS 286a Page 4 CS 286a Page 5 CS 286a Page 6
     
  • sherwin.pptx
    Caltech CS 147
    What do real networks look like? Sherwin Doroudi Click to edit Master subtitle style February 2009 6/5/09 Where we\'re going? We\'ll look at the internet as a web graph and draw some conclusions Discuss some problems with past measurements made in
     
 
 
 
 
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