Multiserver Optimal Bandwidth Monitoring for QoS based Multimedia Delivery Anup Basu, Irene Cheng and Yinzhe Yu


 Ernest Arron Blair
 4 years ago
 Views:
Transcription
1 Multiserver Optimal Badwidth Moitorig for QoS based Multimedia Delivery Aup Basu, Iree Cheg ad Yizhe Yu Departmet of Computig Sciece U. of Alberta
2 Architecture Applicatio Layer Request receptio coectio hadlig request processig Adaptatio Layer Moitor Prepare Trasmit moitor/poll badwidth determie object size trasform requested object to target size deliver object To cliet Lower Layer feedback from etwork Object delivery across etwork A..Basu,I.Cheg ad Y.Yu, U. of Alberta 2
3 Assumptios ad otatios We eed to make a oetime trasmissio of a multimedia object (servers to cliet. User specified a time limit T o cliet. It s epected that the trasmissio will fiish withi T by cofidece level. The first fractio t of T will be used for badwidth testig. Badwidth testig is performed by usig time slices of equal legth ts. Each time slice has badwidth sample i Ci / ts T / t s, badwidth populatio X, X 2,..., X t /, badwidth samples,..., t, s 2 A..Basu,I.Cheg ad Y.Yu, U. of Alberta 3
4 otatios s 2 i i X, is actual badwidth we try to estimate., is the average of badwidth samples. ( i i i i 2, is the variace of badwidth samples. Our Problem is: 2 s First, give,,,, give a estimatio of, so that P( <. est Secod, determie optimal value of, i order to maimize est ( ts. A..Basu,I.Cheg ad Y.Yu, U. of Alberta 4
5 Statistical BackgroudSamplig ad Estimate Assume the paret populatio coforms to the ormal distributio: (, σ, σ is ukow is the mea of samples, the s / coforms to Studet s tdistributio (tdistributio. t If samplig without replacemet from a fiite populatio, we should have a fiite populatio correctio factor: t s A..Basu,I.Cheg ad Y.Yu, U. of Alberta 5
6 tdistributio As, the tdistributio is idetical as ormal distributio. Robust: tdistributio works well, eve if the paret populatio is ot eactly ormally distributed. A..Basu,I.Cheg ad Y.Yu, U. of Alberta 6
7 A..Basu,I.Cheg ad Y.Yu, U. of Alberta 7 Safe Badwidth Estimatio, ~,. (.., (,, (, (, ( > < > < s t The s t P t s P t s kow we As P e i P where fid to problem is Our est est est est
8 Safe Badwidth Estimatio Safe badwidth estimatio: t est (, s tdistributio table: values (v  t, ( Alpha0.75 Alpha0.90 Alpha0.95 v v v v v v A..Basu,I.Cheg ad Y.Yu, U. of Alberta 8
9 Epected Object Size Epect Object Size: V ( est ( ( ts t(, ( ts Importat property of V(: Statistically (if we view radom variable ad s as costat, V( has a sigle maimum value. (Proof omitted Ituitio of the property: Whe is too large, too much time is used for badwidth testig, leavig little time for real object trasmissio; whe is too small, t (, value is too large, leadig to great margi of uderestimatio of badwidth. s A..Basu,I.Cheg ad Y.Yu, U. of Alberta 9
10 Multiserver Eviromet Cotet Storage Server Cliet Cotet Storage Server Iteret Cotet Storage Server From the perspective of the cliet, there are several server available to delivery the same cotet. Cliet ca request a strip of the object from each server. The size of the strips will be proportioed to relative badwidth of all the servers. A..Basu,I.Cheg ad Y.Yu, U. of Alberta 0
11 Multiserver Eviromet Suppose we have chaels available, the 2 We have 2 Vi ( est. i ( ts is the epected object strip size o ith chael. The total object size V ( V (. i i Theorem: The total object size has the same property as i sigle server eviromet. Statistically, it has a sigle maimum. A..Basu,I.Cheg ad Y.Yu, U. of Alberta
12 Multiserver Algorithm The multiserver algorithm: obtai samples V V ( V ( i i ( 2 V ( 2 2 i i ; while (V(>V( { + ; ; ; obtai sample, 2, Calculate V V ; } i i retur ; est + 3 ( ( o each chael; o each chael; A..Basu,I.Cheg ad Y.Yu, U. of Alberta 2
13 Refiemet of the algorithm Actually, this simple etesio of the algorithm is ot always optimal. Whe icreases, 2 2 It s possible that V (, ( > V, + V2 (,. At this time, we d better drop chael #2. V( ( est, i V i Usig Usig 2 Y Y>Y2? Chael # < 2 2 Y2 Chael #2 O A..Basu,I.Cheg ad Y.Yu, U. of Alberta 3 t
14 Step 2 Refiemet of the algorithm V ( ( 2 V 2 ( V 3 ( V V ( 3 V ( V 2( 2 V 2( 3 V 2 ( V 3( 2 V 3( 3 V 3 ( V ( V ( ( Pick the largest k k: the umber of chaels for real trasmissio k 2 Sum the largest two V V ( 3 Sum the largest three Sum all values together Step Chael # Chael #2 Chael #3 Chael # Step 3 Pick the largest A..Basu,I.Cheg ad Y.Yu, U. of Alberta 4
15 Refied multiserver algorithm obtai samples o each chael; Calculate V, for ; ( GetMa( V ; while(true { } retur + i ; ;, 2 ( j i {,2,..., }, {,,..., } + obtai sample o each chael; CalculateVi (, j for i {,2,..., }, j {, 2,..., }; ; ( GetMa( V if (V(<V( break; est j 2 o each chael that costitutes V(; A..Basu,I.Cheg ad Y.Yu, U. of Alberta 5
16 Simulatio Results 60 Time Limit Cofidece Level Result # of Overtime ru (out of % Our Algorithm Fi Sample Size Method # of Fi Samples Average badwidth: 00kbps ad 0kbps. Parameters: alpha0.95, 00 total slices. Two chaels. Stadard deviatios is {0.025, 0.05, 0., 0.5, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6} of the badwidth average. Results are average of all combiatio of the stadard deviatio parameter. A..Basu,I.Cheg ad Y.Yu, U. of Alberta 6
17 Simulatio Results Chael Usage (Drop 30%, SD: Ch#60.0, Ch#26.0 umber of rus use oly Chael # (out of %, SD: Ch#60.0, Ch#2 vary from 0.25 to 6.0. Average of all variace o Chael #2 Variace 0.6(mu o Chael # Variace of Chael # Two chaels  00kbps ad 0kbps. Stadard deviatios is {0.025, 0.05, 0., 0.5, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6} of the badwidth average. A..Basu,I.Cheg ad Y.Yu, U. of Alberta 7
18 Summary Itroduce a statistical model with cofidece level to multiserver badwidth moitorig Dyamically determie the umber of samplig Drop the ureliable chaels A..Basu,I.Cheg ad Y.Yu, U. of Alberta 8
19 The Ed Questios ad Commets? A..Basu,I.Cheg ad Y.Yu, U. of Alberta 9
Optimal Adaptive Bandwidth Monitoring for QoS Based Retrieval
1 Optimal Adaptive Badwidth Moitorig for QoS Based Retrieval Yizhe Yu, Iree Cheg ad Aup Basu (Seior Member) Departmet of Computig Sciece Uiversity of Alberta Edmoto, AB, T6G E8, CAADA {yizhe, aup, li}@cs.ualberta.ca
More informationZTEST / ZSTATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown
ZTEST / ZSTATISTIC: used to test hypotheses about µ whe the populatio stadard deviatio is kow ad populatio distributio is ormal or sample size is large TTEST / TSTATISTIC: used to test hypotheses about
More informationCHAPTER 7: Central Limit Theorem: CLT for Averages (Means)
CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:
More informationI. Chisquared Distributions
1 M 358K Supplemet to Chapter 23: CHISQUARED DISTRIBUTIONS, TDISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad tdistributios, we first eed to look at aother family of distributios, the chisquared distributios.
More informationConfidence Intervals
Cofidece Itervals Cofidece Itervals are a extesio of the cocept of Margi of Error which we met earlier i this course. Remember we saw: The sample proportio will differ from the populatio proportio by more
More informationSampling Distribution And Central Limit Theorem
() Samplig Distributio & Cetral Limit Samplig Distributio Ad Cetral Limit Samplig distributio of the sample mea If we sample a umber of samples (say k samples where k is very large umber) each of size,
More information1. C. The formula for the confidence interval for a population mean is: x t, which was
s 1. C. The formula for the cofidece iterval for a populatio mea is: x t, which was based o the sample Mea. So, x is guarateed to be i the iterval you form.. D. Use the rule : pvalue
More information5: Introduction to Estimation
5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample
More informationConfidence Intervals for One Mean
Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a
More information1 Computing the Standard Deviation of Sample Means
Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.
More informationOverview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals
Overview Estimatig the Value of a Parameter Usig Cofidece Itervals We apply the results about the sample mea the problem of estimatio Estimatio is the process of usig sample data estimate the value of
More informationDetermining the sample size
Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors
More informationConfidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.
Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).
More informationHypothesis testing. Null and alternative hypotheses
Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate
More informationCenter, Spread, and Shape in Inference: Claims, Caveats, and Insights
Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the
More informationCase Study. Normal and t Distributions. Density Plot. Normal Distributions
Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca
More informationUniversity of California, Los Angeles Department of Statistics. Distributions related to the normal distribution
Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Istructor: Nicolas Christou Three importat distributios: Distributios related to the ormal distributio Chisquare (χ ) distributio.
More informationLECTURE 13: Crossvalidation
LECTURE 3: Crossvalidatio Resampli methods Cross Validatio Bootstrap Bias ad variace estimatio with the Bootstrap Threeway data partitioi Itroductio to Patter Aalysis Ricardo GutierrezOsua Texas A&M
More informationChapter 7: Confidence Interval and Sample Size
Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum
More informationPractice Problems for Test 3
Practice Problems for Test 3 Note: these problems oly cover CIs ad hypothesis testig You are also resposible for kowig the samplig distributio of the sample meas, ad the Cetral Limit Theorem Review all
More informationThe following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles
The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio
More informationPSYCHOLOGICAL STATISTICS
UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION B Sc. Cousellig Psychology (0 Adm.) IV SEMESTER COMPLEMENTARY COURSE PSYCHOLOGICAL STATISTICS QUESTION BANK. Iferetial statistics is the brach of statistics
More informationProperties of MLE: consistency, asymptotic normality. Fisher information.
Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout
More informationNormal Distribution.
Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued
More informationMath C067 Sampling Distributions
Math C067 Samplig Distributios Sample Mea ad Sample Proportio Richard Beigel Some time betwee April 16, 2007 ad April 16, 2007 Examples of Samplig A pollster may try to estimate the proportio of voters
More informationSTATISTICAL METHODS FOR BUSINESS
STATISTICAL METHODS FOR BUSINESS UNIT 7: INFERENTIAL TOOLS. DISTRIBUTIONS ASSOCIATED WITH SAMPLING 7.1. Distributios associated with the samplig process. 7.2. Iferetial processes ad relevat distributios.
More informationChapter 7 Methods of Finding Estimators
Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of
More information15.075 Exam 3. Instructor: Cynthia Rudin TA: Dimitrios Bisias. November 22, 2011
15.075 Exam 3 Istructor: Cythia Rudi TA: Dimitrios Bisias November 22, 2011 Gradig is based o demostratio of coceptual uderstadig, so you eed to show all of your work. Problem 1 A compay makes highdefiitio
More informationStatistical inference: example 1. Inferential Statistics
Statistical iferece: example 1 Iferetial Statistics POPULATION SAMPLE A clothig store chai regularly buys from a supplier large quatities of a certai piece of clothig. Each item ca be classified either
More informationInference on Proportion. Chapter 8 Tests of Statistical Hypotheses. Sampling Distribution of Sample Proportion. Confidence Interval
Chapter 8 Tests of Statistical Hypotheses 8. Tests about Proportios HT  Iferece o Proportio Parameter: Populatio Proportio p (or π) (Percetage of people has o health isurace) x Statistic: Sample Proportio
More informationChapter 7  Sampling Distributions. 1 Introduction. What is statistics? It consist of three major areas:
Chapter 7  Samplig Distributios 1 Itroductio What is statistics? It cosist of three major areas: Data Collectio: samplig plas ad experimetal desigs Descriptive Statistics: umerical ad graphical summaries
More informationOutput Analysis (2, Chapters 10 &11 Law)
B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should
More informationChapter 6: Variance, the law of large numbers and the MonteCarlo method
Chapter 6: Variace, the law of large umbers ad the MoteCarlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value
More informationMeasures of Spread and Boxplots Discrete Math, Section 9.4
Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,
More informationOnesample test of proportions
Oesample test of proportios The Settig: Idividuals i some populatio ca be classified ito oe of two categories. You wat to make iferece about the proportio i each category, so you draw a sample. Examples:
More informationParametric (theoretical) probability distributions. (Wilks, Ch. 4) Discrete distributions: (e.g., yes/no; above normal, normal, below normal)
6 Parametric (theoretical) probability distributios. (Wilks, Ch. 4) Note: parametric: assume a theoretical distributio (e.g., Gauss) Noparametric: o assumptio made about the distributio Advatages of assumig
More informationTHE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n
We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample
More informationSTA 2023 Practice Questions Exam 2 Chapter 7 sec 9.2. Case parameter estimator standard error Estimate of standard error
STA 2023 Practice Questios Exam 2 Chapter 7 sec 9.2 Formulas Give o the test: Case parameter estimator stadard error Estimate of stadard error Samplig Distributio oe mea x s t (1) oe p ( 1 p) CI: prop.
More informationMEI Structured Mathematics. Module Summary Sheets. Statistics 2 (Version B: reference to new book)
MEI Mathematics i Educatio ad Idustry MEI Structured Mathematics Module Summary Sheets Statistics (Versio B: referece to ew book) Topic : The Poisso Distributio Topic : The Normal Distributio Topic 3:
More informationQuadrat Sampling in Population Ecology
Quadrat Samplig i Populatio Ecology Backgroud Estimatig the abudace of orgaisms. Ecology is ofte referred to as the "study of distributio ad abudace". This beig true, we would ofte like to kow how may
More information0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5
Sectio 13 KolmogorovSmirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.
More information(VCP310) 18004186789
Maual VMware Lesso 1: Uderstadig the VMware Product Lie I this lesso, you will first lear what virtualizatio is. Next, you ll explore the products offered by VMware that provide virtualizatio services.
More informationLesson 15 ANOVA (analysis of variance)
Outlie Variability betwee group variability withi group variability total variability Fratio Computatio sums of squares (betwee/withi/total degrees of freedom (betwee/withi/total mea square (betwee/withi
More informationCONTROL CHART BASED ON A MULTIPLICATIVEBINOMIAL DISTRIBUTION
www.arpapress.com/volumes/vol8issue2/ijrras_8_2_04.pdf CONTROL CHART BASED ON A MULTIPLICATIVEBINOMIAL DISTRIBUTION Elsayed A. E. Habib Departmet of Statistics ad Mathematics, Faculty of Commerce, Beha
More informationHypergeometric Distributions
7.4 Hypergeometric Distributios Whe choosig the startig lieup for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you
More informationCOMPARISON OF THE EFFICIENCY OF SCONTROL CHART AND EWMAS 2 CONTROL CHART FOR THE CHANGES IN A PROCESS
COMPARISON OF THE EFFICIENCY OF SCONTROL CHART AND EWMAS CONTROL CHART FOR THE CHANGES IN A PROCESS Supraee Lisawadi Departmet of Mathematics ad Statistics, Faculty of Sciece ad Techoology, Thammasat
More informationDefinition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean
1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.
More informationOverview of some probability distributions.
Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability
More information1 The Gaussian channel
ECE 77 Lecture 0 The Gaussia chael Objective: I this lecture we will lear about commuicatio over a chael of practical iterest, i which the trasmitted sigal is subjected to additive white Gaussia oise.
More information3 Basic Definitions of Probability Theory
3 Basic Defiitios of Probability Theory 3defprob.tex: Feb 10, 2003 Classical probability Frequecy probability axiomatic probability Historical developemet: Classical Frequecy Axiomatic The Axiomatic defiitio
More informationCentral Limit Theorem and Its Applications to Baseball
Cetral Limit Theorem ad Its Applicatios to Baseball by Nicole Aderso A project submitted to the Departmet of Mathematical Scieces i coformity with the requiremets for Math 4301 (Hoours Semiar) Lakehead
More informationChapter 14 Nonparametric Statistics
Chapter 14 Noparametric Statistics A.K.A. distributiofree statistics! Does ot deped o the populatio fittig ay particular type of distributio (e.g, ormal). Sice these methods make fewer assumptios, they
More informationA Mathematical Perspective on Gambling
A Mathematical Perspective o Gamblig Molly Maxwell Abstract. This paper presets some basic topics i probability ad statistics, icludig sample spaces, probabilistic evets, expectatios, the biomial ad ormal
More informationA probabilistic proof of a binomial identity
A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two
More informationTO: Users of the ACTEX Review Seminar on DVD for SOA Exam MLC
TO: Users of the ACTEX Review Semiar o DVD for SOA Eam MLC FROM: Richard L. (Dick) Lodo, FSA Dear Studets, Thak you for purchasig the DVD recordig of the ACTEX Review Semiar for SOA Eam M, Life Cotigecies
More informationIncremental calculation of weighted mean and variance
Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically
More informationConfidence intervals and hypothesis tests
Chapter 2 Cofidece itervals ad hypothesis tests This chapter focuses o how to draw coclusios about populatios from sample data. We ll start by lookig at biary data (e.g., pollig), ad lear how to estimate
More informationTopic 5: Confidence Intervals (Chapter 9)
Topic 5: Cofidece Iterval (Chapter 9) 1. Itroductio The two geeral area of tatitical iferece are: 1) etimatio of parameter(), ch. 9 ) hypothei tetig of parameter(), ch. 10 Let X be ome radom variable with
More informationAuthentication  Access Control Default Security Active Directory Trusted Authentication Guest User or Anonymous (unauthenticated) Logging Out
FME Server Security Table of Cotets FME Server Autheticatio  Access Cotrol Default Security Active Directory Trusted Autheticatio Guest User or Aoymous (uautheticated) Loggig Out Authorizatio  Roles
More informationUnbiased Estimation. Topic 14. 14.1 Introduction
Topic 4 Ubiased Estimatio 4. Itroductio I creatig a parameter estimator, a fudametal questio is whether or ot the estimator differs from the parameter i a systematic maer. Let s examie this by lookig a
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis
Ruig Time ( 3.) Aalysis of Algorithms Iput Algorithm Output A algorithm is a stepbystep procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More information, a Wishart distribution with n 1 degrees of freedom and scale matrix.
UMEÅ UNIVERSITET Matematiskstatistiska istitutioe Multivariat dataaalys D MSTD79 PA TENTAMEN 00409 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multivariat dataaalys D, 5 poäg.. Assume that
More informationHypothesis testing using complex survey data
Hypotesis testig usig complex survey data A Sort Course preseted by Peter Ly, Uiversity of Essex i associatio wit te coferece of te Europea Survey Researc Associatio Prague, 5 Jue 007 1 1. Objective: Simple
More informationDepartment of Computer Science, University of Otago
Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS200609 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly
More informationResearch Method (I) Knowledge on Sampling (Simple Random Sampling)
Research Method (I) Kowledge o Samplig (Simple Radom Samplig) 1. Itroductio to samplig 1.1 Defiitio of samplig Samplig ca be defied as selectig part of the elemets i a populatio. It results i the fact
More informationRecovery time guaranteed heuristic routing for improving computation complexity in survivable WDM networks
Computer Commuicatios 30 (2007) 1331 1336 wwwelseviercom/locate/comcom Recovery time guarateed heuristic routig for improvig computatio complexity i survivable WDM etworks Lei Guo * College of Iformatio
More informationSPC for Software Reliability: Imperfect Software Debugging Model
IJCSI Iteratioal Joural of Computer Sciece Issues, Vol. 8, Issue 3, o., May 0 ISS (Olie: 694084 www.ijcsi.org 9 SPC for Software Reliability: Imperfect Software Debuggig Model Dr. Satya Prasad Ravi,.Supriya
More informationJune 3, 1999. Voice over IP
Jue 3, 1999 Voice over IP This applicatio ote discusses the Hypercom solutio for providig edtoed Iteret protocol (IP) coectivity i a ew or existig Hypercom Hybrid Trasport Mechaism (HTM) etwork, reducig
More informationDomain 1  Describe Cisco VoIP Implementations
Maual ONT (6428) 18004186789 Domai 1  Describe Cisco VoIP Implemetatios Advatages of VoIP Over Traditioal Switches Voice over IP etworks have may advatages over traditioal circuit switched voice etworks.
More informationChair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics
Chair for Network Architectures ad Services Istitute of Iformatics TU Müche Prof. Carle Network Security Chapter 2 Basics 2.4 Radom Number Geeratio for Cryptographic Protocols Motivatio It is crucial to
More informationPage 1. Real Options for Engineering Systems. What are we up to? Today s agenda. J1: Real Options for Engineering Systems. Richard de Neufville
Real Optios for Egieerig Systems J: Real Optios for Egieerig Systems By (MIT) Stefa Scholtes (CU) Course website: http://msl.mit.edu/cmi/ardet_2002 Stefa Scholtes Judge Istitute of Maagemet, CU Slide What
More informationLecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)
18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the BruMikowski iequality for boxes. Today we ll go over the
More informationMODELING SERVER USAGE FOR ONLINE TICKET SALES
Proceedigs of the 2011 Witer Simulatio Coferece S. Jai, R.R. Creasey, J. Himmelspach, K.P. White, ad M. Fu, eds. MODELING SERVER USAGE FOR ONLINE TICKET SALES Christie S.M. Currie Uiversity of Southampto
More information*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.
Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.
More informationMaximum Likelihood Estimators.
Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio
More informationCOMPUSOFT, An international journal of advanced computer technology, 3 (3), March2014 (VolumeIII, IssueIII)
COMPUSOFT, A iteratioal joural of advaced computer techology, 3 (3), March2014 (VolumeIII, IssueIII) ISSN:23200790 Adaptive Workload Maagemet for Efficiet Eergy Utilizatio o Cloud M.Prabakara 1, M.
More informationNonlife insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring
Nolife isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy
More informationThe Case for a Hybrid Passive/Active Network Monitoring Scheme in the Wireless Internet
The Case for a Hybrid Passive/Active Network Moitorig Scheme i the Wireless Iteret Björ Ladfeldt*, Pipat Sookavataa*,** ad Arua Seevirate** Dept. of Electrical Egieerig ad Telecommuicatios The Uiversity
More informationNow here is the important step
LINEST i Excel The Excel spreadsheet fuctio "liest" is a complete liear least squares curve fittig routie that produces ucertaity estimates for the fit values. There are two ways to access the "liest"
More informationQueuing Systems: Lecture 1. Amedeo R. Odoni October 10, 2001
Queuig Systems: Lecture Amedeo R. Odoi October, 2 Topics i Queuig Theory 9. Itroductio to Queues; Little s Law; M/M/. Markovia BirthadDeath Queues. The M/G/ Queue ad Extesios 2. riority Queues; State
More informationAnalyzing Longitudinal Data from Complex Surveys Using SUDAAN
Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical
More informationData Center Ethernet Facilitation of Enterprise Clustering. David Flynn, Linux Networx Orlando, Florida March 16, 2004
Data Ceter Etheret Facilitatio of Eterprise Clusterig David Fly, Liux Networx Orlado, Florida March 16, 2004 1 2 Liux Networx builds COTS based clusters 3 Clusters Offer Improved Performace Scalability
More informationSystems Design Project: Indoor Location of Wireless Devices
Systems Desig Project: Idoor Locatio of Wireless Devices Prepared By: Bria Murphy Seior Systems Sciece ad Egieerig Washigto Uiversity i St. Louis Phoe: (805) 6985295 Email: bcm1@cec.wustl.edu Supervised
More informationCME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8
CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive
More informationOMG! Excessive Texting Tied to Risky Teen Behaviors
BUSIESS WEEK: EXECUTIVE EALT ovember 09, 2010 OMG! Excessive Textig Tied to Risky Tee Behaviors Kids who sed more tha 120 a day more likely to try drugs, alcohol ad sex, researchers fid TUESDAY, ov. 9
More informationINVESTMENT PERFORMANCE COUNCIL (IPC)
INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks
More informationBASIC STATISTICS. f(x 1,x 2,..., x n )=f(x 1 )f(x 2 ) f(x n )= f(x i ) (1)
BASIC STATISTICS. SAMPLES, RANDOM SAMPLING AND SAMPLE STATISTICS.. Radom Sample. The radom variables X,X 2,..., X are called a radom sample of size from the populatio f(x if X,X 2,..., X are mutually idepedet
More informationModified Line Search Method for Global Optimization
Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o
More informationHandbook on STATISTICAL DISTRIBUTIONS for experimentalists
Iteral Report SUF PFY/96 Stockholm, December 996 st revisio, 3 October 998 last modificatio September 7 Hadbook o STATISTICAL DISTRIBUTIONS for experimetalists by Christia Walck Particle Physics Group
More informationTHE TWOVARIABLE LINEAR REGRESSION MODEL
THE TWOVARIABLE LINEAR REGRESSION MODEL Herma J. Bieres Pesylvaia State Uiversity April 30, 202. Itroductio Suppose you are a ecoomics or busiess maor i a college close to the beach i the souther part
More informationInteger Factorization Algorithms
Iteger Factorizatio Algorithms Coelly Bares Departmet of Physics, Orego State Uiversity December 7, 004 This documet has bee placed i the public domai. Cotets I. Itroductio 3 1. Termiology 3. Fudametal
More informationExample: Probability ($1 million in S&P 500 Index will decline by more than 20% within a
Value at Risk For a give portfolio, ValueatRisk (VAR) is defied as the umber VAR such that: Pr( Portfolio loses more tha VAR withi time period t)
More informationCapacity of Wireless Networks with Heterogeneous Traffic
Capacity of Wireless Networks with Heterogeeous Traffic Migyue Ji, Zheg Wag, Hamid R. Sadjadpour, J.J. GarciaLuaAceves Departmet of Electrical Egieerig ad Computer Egieerig Uiversity of Califoria, Sata
More informationQUADRO tech. PST Flightdeck. Put your PST Migration on autopilot
QUADRO tech PST Flightdeck Put your PST Migratio o autopilot Put your PST Migratio o Autopilot A moder aircraft hardly remids its pilots of the early days of air traffic. It is desiged to eable flyig as
More informationConsider these sobering statistics
Idetity Theft is a form of fraud or Idetity theft cotiues to icrease every year ad has impacted millios of Americas. cheatig of aother perso s idetity i which someoe preteds to be someoe else by assumig
More informationSECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES
SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,
More informationParameter estimation for nonlinear models: Numerical approaches to solving the inverse problem. Lecture 11 04/01/2008. Sven Zenker
Parameter estimatio for oliear models: Numerical approaches to solvig the iverse problem Lecture 11 04/01/2008 Sve Zeker Review: Trasformatio of radom variables Cosider probability distributio of a radom
More informationUC Berkeley Department of Electrical Engineering and Computer Science. EE 126: Probablity and Random Processes. Solutions 9 Spring 2006
Exam format UC Bereley Departmet of Electrical Egieerig ad Computer Sciece EE 6: Probablity ad Radom Processes Solutios 9 Sprig 006 The secod midterm will be held o Wedesday May 7; CHECK the fial exam
More informationTI83, TI83 Plus or TI84 for NonBusiness Statistics
TI83, TI83 Plu or TI84 for NoBuie Statitic Chapter 3 Eterig Data Pre [STAT] the firt optio i already highlighted (:Edit) o you ca either pre [ENTER] or. Make ure the curor i i the lit, ot o the lit
More informationAn optical illusion. A statistical illusion. What is Statistics? What is Statistics? An Engineer, A Physicist And A Statistician.
A optical illusio Yalçı Akçay CASE 7 56 yakcay@ku.edu.tr A statistical illusio A Egieer, A Physicist Ad A Statisticia Real estate aget sellig a house to a sob customer: typical mothly icome i the eighborhood
More information