Basic Queueing Theory M/M/* Queues. Introduction
|
|
|
- Spencer Kelly
- 9 years ago
- Views:
Transcription
1 Basc Queueng Theory M/M/* Queues These sldes are created by Dr. Yh Huang of George Mason Unversty. Students regstered n Dr. Huang's courses at GMU can ake a sngle achne-readable copy and prnt a sngle copy of each slde for ther own reference, so long as each slde contans the copyrght stateent, and GMU facltes are not used to produce paper copes. ersson for any other use, ether n achnereadable or prnted for, ust be obtaned fro the author n wrtng. CS 756 Introducton Queueng theory provdes a atheatcal bass for understandng and predctng the behavor of councaton networks. Basc Model Arrvals Queue Server Departures CS 756
2 Major paraeters: nterarrval-te dstrbuton servce-te dstrbuton nuber of servers queueng dscplne how custoers are taken fro the queue, for exaple, FCFS nuber of buffers, whch custoers use to wat for servce A coon notaton: A/B/, where s the nuber of servers and A and B are chosen fro M: Markov exponental dstrbuton D: Deternstc G: General arbtrary dstrbuton CS M/M/ Queueng Systes Interarrval tes are exponentally dstrbuted, wth average arrval rate. Servce tes are exponentally dstrbuted, wth average servce rate. There s only one server. The buffer s assued to be nfnte. The queung dscplne s frst-coe-frstserve FCFS. CS 756 4
3 Syste State Due to the eoryless property of the exponental dstrbuton, the entre state of the syste, as far as the concern of probablstc analyss, can be suarzed by the nuber of custoers n the syste,. the past/hstory how we get here does not atter When a custoer arrves or departs, the syste oves to an adjacent state ether + or -. CS State Transton Dagra In equlbru, Let { syste n state } We have + + The rate of oveents n both drectons should be equal CS
4 4 CS Equatons fro the state transton dagra: Solve What s? 3 k k CS Snce we have That s,. Note that ust be less than, or else the syste s unstable. k k k k. k k
5 5 CS Average Nuber of Custoers? k k k k k k N CS 756 Average delay per custoer te n queue plus servce te: Average watng te n queue: Average nuber of custoers n queue: N T W W N Q
6 Applcatons Consder 4 coputer users, each of whch produces n average 48 packets per second. For every custoer, the nterarrval tes of hs packets are exponentally dstrbuted. The lengths of packets are also exponentally dstrbuted, wth ean 5 bytes. CS 756 Scenaro Users share a T lne usng the standard T te-dvson ultplexng. Assue that each user s assocated wth an nfnte buffer that s, queue. In a T lne, t takes /8 seconds to delver or serve each byte. However, due to ther varable lengths, the delvery or servce tes of packets are stll exponentally dstrbuted. The average servce rate? CS 756 6
7 The syste can be consdered as 4 M/M/ queues: acket Arrvals Queue Server, /4th of the T lne Departs to the other end of the T lne We have 48 75% N 3.75 T sec CS Scenaro Users share a.544 Mbps lne through an I router. ackets fro 4 users The entre T as the server CS
8 The aggregated arrval rate s The servce rate s We have 48/ 64 75% T sec Ths syste s 4 tes faster than TDM! CS Dscusson Flaws n the analyss? Stll such a drastc dfference n results convncngly reveals the neffcency of TDM. Ths partly explans the oentu toward usng the Internet as the unversal nforaton nfrastructure. In general, allowng custoers to share a pool of resources s far ore effcent than allocatng a fxed porton to each custoers. CS
9 M/M/ Queueng Systes Arrvals Queue Departs Servers All servers are dentcal, wth servce rate CS State Transton Dagra 3 + Balance equatons:,, for for > CS
10 CS Soluton Where Notcng that we have > p for,! for,!., [ ]!! + CS 756 The probablty that an arrvng custoer has to wat n queue: Ths s known as the Erlang C forula.!!! Q
11 CS 756 Average nuber of watng custoers: + +!!! N Q Q CS 756 Average watng te n queue: Average te n the syste: Average nuber of custoers n the syste: N W Q Q W T Q Q + + T N Q Q
12 M/M//K Queueng Systes Slar to M/M/, except that the queue has a fnte capacty of K slots. That s, there can be at ost K custoers n the syste. If a custoer arrves when the queue s full, he/she s dscarded leaves the syste and wll not return. CS Analyss Notce ts slarty to M/M/, except that there are no states greater than K. We have Notcng that K- K, for K, for > K K + K we have CS 756 4
13 osson rocess Let rando varable N be a counter of the nuber of occurrences of a partcular type of events. Clearly, the value of N ncreases over te. Let N t be the value of N at te t. Moreover, f N, Nt s sad to be a countng process. The countng process Nt s sad to be a osson process havng rate f the nuber of events n any nterval of length t s osson dstrbuted wth ean t. That s, for all s, t t t { N t + s N s n} e, n,,... n! CS n Dscusson The nterarrval tes of a osson process wth rate s exponentally dstrbuted wth average The reverse s also true: f the nterarrval tes of events are exponentally dstrbuted wth average then the event countng process s osson wth rate. Thus, the custoer arrval processes of M/M/* queueng systes are osson. / A osson custoer count and exponentally dstrbuted custoer nterarrval tes are the two sdes of the con. / CS
14 Saplng osson Arrvals Consder a osson custoer arrval process of wth average rate. Each custoer can be classfed as Type I or Type II, wth probablty p and -p respectvely. Then, the arrval process of Type I custoers s also osson wth average rate p. Lkewse, the arrvals of Type II custoers s osson wth average rate p. CS Applcaton We know that the custoer arrvals at a barbershop for osson process wth average rate of custoers per hour. Aong the custoers, 4% are ales and 6% are feales. Then the nterarrval tes of ale custoers are exponentally dstrbuted wth an average rate of 4 per hour. The nterarrval tes of feale custoers are exponentally dstrbuted wth an average rate of 6 per hour. CS
15 Exercse Consder the router confguraton below. ackets er sec. ort 4% ort Mbps 6% ort Mbps The lengths of arrvng packets are exponentally dstrbuted wth an average of bts. CS Questons Argue that queues A and B are ndependent M/M/ systes. Copute the average length of queue A n bts. For a packet destned for port, copute ts expected te at the router ncludng transsson te. CS
16 Copute the average te a packet spent at the router ncludng transsson te. Copute the average nuber of packets at the routerncludng the ones n transsson. CS Mergng osson Arrvals Gven two exponental varables X and X wth rates and, the rando varable X n{ X, X } s also exponental, wth rate +. Consder two osson arrvals, wth average rates and. The erged arrval process wll also be a osson process, wth the average rate + CS
17 Applcaton Consder the router confguraton below. acket arrval ort ort acket arrval ort Router CS The lengths of arrvng packets are exponentally dstrbuted wth an average of bts. Why do we care about packet lengths? acket arrvals at ports and are exponentally dstrbuted wth average rates of and 3 packets per second, respectvely. The transsson rate of port s Mbps. The whole syste can be odeled as a sngle M/M/ queueng syste, wth an arrval rate of 5 and servce rate of,. CS
18 Burke's Theore In ts steady state, an M/M/ queueng syste wth arrval rate and per-server servce rate produces exponentally dstrbuted nter-departure tes wth average rate. Applcaton: Two cascaded, ndependently operatng M/M/ systes can be analyzed separately. Departs Server Server M/M/ syste M/M/ syste CS tfall Consder the syste below where the servers are transsson lnes. 5 ackets er sec. Mbps Mbps ackets lengths are exponentally dstrbuted wth an average of bts. Can the two queues be analyzed separately? Why? CS
19 Dscusson In general: any feedforward network of ndependently-operatng M/M/ systes can be analyzed n ths syste-by-syste decoposton. p 4 - p 3 CS Queston: How about networks that do contan feedbacks? Answer: the nterarrval tes of soe systes ay not be exponentally dstrbuted and thus cannot be analyzed as ndependent M/M/ queues CS
20 Jackson's Theore For an arbtrary network of k M/M/ queueng systes, where n, n,..., nk n n... k nk j n j n j j j., That s, n ters of the nuber of custoers n each syste, ndvdual systes act as f they are ndependent M/M/ queues they ay not. CS Applcaton Consder the network below. The arrval rate and probabltes p and p are known. p CU I / O p We frst copute the arrval rates and : + p, / p, p / p CS 756 4
21 Let / and /. By Jackson's theore, j, j And N, N Total nuber of custoers n syste s N N + N. Average te n syste s T N +. CS Dscusson Consder the packet swtchng network below. Router ackets Router Router 4 Router 3 Can we cte the Jackson's theore, odel the transsson lnes as servers, and analyze the as separate M/M/ queues? Why? CS 756 4
1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET
Yugoslav Journal of Operatons Research (0), Nuber, 65-78 DOI: 0.98/YJOR0065Y BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET Peng-Sheng YOU Graduate Insttute of Marketng and Logstcs/Transportaton,
Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters
Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn [email protected]; [email protected]
Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters
Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn [email protected]; [email protected]
An Electricity Trade Model for Microgrid Communities in Smart Grid
An Electrcty Trade Model for Mcrogrd Countes n Sart Grd Tansong Cu, Yanzh Wang, Shahn Nazaran and Massoud Pedra Unversty of Southern Calforna Departent of Electrcal Engneerng Los Angeles, CA, USA {tcu,
Inventory Control in a Multi-Supplier System
3th Intl Workng Senar on Producton Econocs (WSPE), Igls, Autrche, pp.5-6 Inventory Control n a Mult-Suppler Syste Yasen Arda and Jean-Claude Hennet LAAS-CRS, 7 Avenue du Colonel Roche, 3077 Toulouse Cedex
Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters
01 Proceedngs IEEE INFOCOM Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva heja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn [email protected];
A Statistical Model for Detecting Abnormality in Static-Priority Scheduling Networks with Differentiated Services
A Statstcal odel for Detectng Abnoralty n Statc-Prorty Schedulng Networks wth Dfferentated Servces ng L 1 and We Zhao 1 School of Inforaton Scence & Technology, East Chna Noral Unversty, Shangha 0006,
Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application
Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdo-dong,
Recurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
How Much to Bet on Video Poker
How Much to Bet on Vdeo Poker Trstan Barnett A queston that arses whenever a gae s favorable to the player s how uch to wager on each event? Whle conservatve play (or nu bet nzes large fluctuatons, t lacks
An Analytical Model of Web Server Load Distribution by Applying a Minimum Entropy Strategy
Internatonal Journal of Coputer and Councaton Engneerng, Vol. 2, No. 4, July 203 An Analytcal odel of Web Server Load Dstrbuton by Applyng a nu Entropy Strategy Teeranan Nandhakwang, Settapong alsuwan,
Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t
Indeternate Analyss Force Method The force (flexblty) ethod expresses the relatonshps between dsplaceents and forces that exst n a structure. Prary objectve of the force ethod s to deterne the chosen set
benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12
14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
This paper concerns the evaluation and analysis of order
ORDER-FULFILLMENT PERFORMANCE MEASURES IN AN ASSEMBLE- TO-ORDER SYSTEM WITH STOCHASTIC LEADTIMES JING-SHENG SONG Unversty of Calforna, Irvne, Calforna SUSAN H. XU Penn State Unversty, Unversty Park, Pennsylvana
Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts
Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
Retailers must constantly strive for excellence in operations; extremely narrow profit margins
Managng a Retaler s Shelf Space, Inventory, and Transportaton Gerard Cachon 300 SH/DH, The Wharton School, Unversty of Pennsylvana, Phladelpha, Pennsylvana 90 [email protected] http://opm.wharton.upenn.edu/cachon/
Analysis of Energy-Conserving Access Protocols for Wireless Identification Networks
From the Proceedngs of Internatonal Conference on Telecommuncaton Systems (ITC-97), March 2-23, 1997. 1 Analyss of Energy-Conservng Access Protocols for Wreless Identfcaton etworks Imrch Chlamtac a, Chara
The Greedy Method. Introduction. 0/1 Knapsack Problem
The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton
Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
BERNSTEIN POLYNOMIALS
On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
What is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
The OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
CALL ADMISSION CONTROL IN WIRELESS MULTIMEDIA NETWORKS
CALL ADMISSION CONTROL IN WIRELESS MULTIMEDIA NETWORKS Novella Bartoln 1, Imrch Chlamtac 2 1 Dpartmento d Informatca, Unverstà d Roma La Sapenza, Roma, Italy [email protected] 2 Center for Advanced
INTRODUCTION TO MERGERS AND ACQUISITIONS: FIRM DIVERSIFICATION
XV. INTODUCTION TO MEGES AND ACQUISITIONS: FIM DIVESIFICATION In the ntroducton to Secton VII, t was noted that frs can acqure assets by ether undertakng nternally-generated new projects or by acqurng
Optimal outpatient appointment scheduling
Health Care Manage Sc (27) 1:217 229 DOI 1.17/s1729-7-915- Optmal outpatent appontment schedulng Gudo C. Kaandorp Ger Koole Receved: 15 March 26 / Accepted: 28 February 27 / Publshed onlne: 23 May 27 Sprnger
Enabling P2P One-view Multi-party Video Conferencing
Enablng P2P One-vew Mult-party Vdeo Conferencng Yongxang Zhao, Yong Lu, Changja Chen, and JanYn Zhang Abstract Mult-Party Vdeo Conferencng (MPVC) facltates realtme group nteracton between users. Whle P2P
Conferencing protocols and Petri net analysis
Conferencng protocols and Petr net analyss E. ANTONIDAKIS Department of Electroncs, Technologcal Educatonal Insttute of Crete, GREECE [email protected] Abstract: Durng a computer conference, users desre
v a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
Maximizing profit using recommender systems
Maxzng proft usng recoender systes Aparna Das Brown Unversty rovdence, RI [email protected] Clare Matheu Brown Unversty rovdence, RI [email protected] Danel Rcketts Brown Unversty rovdence, RI [email protected]
AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE
AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE Yu-L Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent
Rapid Estimation Method for Data Capacity and Spectrum Efficiency in Cellular Networks
Rapd Estmaton ethod for Data Capacty and Spectrum Effcency n Cellular Networs C.F. Ball, E. Humburg, K. Ivanov, R. üllner Semens AG, Communcatons oble Networs unch, Germany [email protected] Abstract
Small-Signal Analysis of BJT Differential Pairs
5/11/011 Dfferental Moe Sall Sgnal Analyss of BJT Dff Par 1/1 SallSgnal Analyss of BJT Dfferental Pars Now lets conser the case where each nput of the fferental par conssts of an entcal D bas ter B, an
Extending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set
II. THE QUALITY AND REGULATION OF THE DISTRIBUTION COMPANIES I. INTRODUCTION
Fronter Methodology to fx Qualty goals n Electrcal Energy Dstrbuton Copanes R. Rarez 1, A. Sudrà 2, A. Super 3, J.Bergas 4, R.Vllafáfla 5 1-2 -3-4-5 - CITCEA - UPC UPC., Unversdad Poltécnca de Cataluña,
Formulating & Solving Integer Problems Chapter 11 289
Formulatng & Solvng Integer Problems Chapter 11 289 The Optonal Stop TSP If we drop the requrement that every stop must be vsted, we then get the optonal stop TSP. Ths mght correspond to a ob sequencng
Vasicek s Model of Distribution of Losses in a Large, Homogeneous Portfolio
Vascek s Model of Dstrbuton of Losses n a Large, Homogeneous Portfolo Stephen M Schaefer London Busness School Credt Rsk Electve Summer 2012 Vascek s Model Important method for calculatng dstrbuton of
Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School
Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management
n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)
MATH 16T Exam 1 : Part I (In-Class) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total
A NOTE ON THE PREDICTION AND TESTING OF SYSTEM RELIABILITY UNDER SHOCK MODELS C. Bouza, Departamento de Matemática Aplicada, Universidad de La Habana
REVISTA INVESTIGACION OPERACIONAL Vol., No. 3, 000 A NOTE ON THE PREDICTION AND TESTING OF SYSTEM RELIABILITY UNDER SHOCK MODELS C. Bouza, Departaento de Mateátca Aplcada, Unversdad de La Habana ABSTRACT
Little s Law & Bottleneck Law
Lttle s Law & Bottleneck Law Dec 20 I professonals have shunned performance modellng consderng t to be too complex and napplcable to real lfe. A lot has to do wth fear of mathematcs as well. hs tutoral
Support Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada [email protected] Abstract Ths s a note to explan support vector machnes.
Development of TIF for transaction cost allocation in deregulated power system
ISSN (Onlne) 31 004 ISSN (Prnt) 31 556 Development of TIF for transacton cost allocaton n deregulated power system Noolu.Narendra Reddy 1, Kurakula.Vmala Kumar P.G. Scholar, Department of EEE, JNTUA College
8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
Multiple stage amplifiers
Multple stage amplfers Ams: Examne a few common 2-transstor amplfers: -- Dfferental amplfers -- Cascode amplfers -- Darlngton pars -- current mrrors Introduce formal methods for exactly analysng multple
Online Algorithms for Uploading Deferrable Big Data to The Cloud
Onlne lgorths for Uploadng Deferrable Bg Data to The Cloud Lnquan Zhang, Zongpeng L, Chuan Wu, Mnghua Chen Unversty of Calgary, {lnqzhan,zongpeng}@ucalgary.ca The Unversty of Hong Kong, [email protected] The
DEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
PERRON FROBENIUS THEOREM
PERRON FROBENIUS THEOREM R. CLARK ROBINSON Defnton. A n n matrx M wth real entres m, s called a stochastc matrx provded () all the entres m satsfy 0 m, () each of the columns sum to one, m = for all, ()
Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
Bandwdth Packng E. G. Coman, Jr. and A. L. Stolyar Bell Labs, Lucent Technologes Murray Hll, NJ 07974 fegc,[email protected] Abstract We model a server that allocates varyng amounts of bandwdth
21 Vectors: The Cross Product & Torque
21 Vectors: The Cross Product & Torque Do not use our left hand when applng ether the rght-hand rule for the cross product of two vectors dscussed n ths chapter or the rght-hand rule for somethng curl
Case Study: Load Balancing
Case Study: Load Balancng Thursday, 01 June 2006 Bertol Marco A.A. 2005/2006 Dmensonamento degl mpant Informatc LoadBal - 1 Introducton Optmze the utlzaton of resources to reduce the user response tme
Modeling and Assessment Performance of OpenFlow-Based Network Control Plane
ISSN (Onlne): 2319-7064 Index Coperncus Value (2013): 6.14 Ipact Factor (2013): 4.438 Modelng and Assessent Perforance of OpenFlo-Based Netork Control Plane Saer Salah Al_Yassn Assstant Teacher, Al_Maon
How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008
Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn
Stochastic epidemic models revisited: Analysis of some continuous performance measures
Stochastc epdemc models revsted: Analyss of some contnuous performance measures J.R. Artalejo Faculty of Mathematcs, Complutense Unversty of Madrd, 28040 Madrd, Span A. Economou Department of Mathematcs,
A Novel Dynamic Role-Based Access Control Scheme in User Hierarchy
Journal of Coputatonal Inforaton Systes 6:7(200) 2423-2430 Avalable at http://www.jofcs.co A Novel Dynac Role-Based Access Control Schee n User Herarchy Xuxa TIAN, Zhongqn BI, Janpng XU, Dang LIU School
"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
Sketching Sampled Data Streams
Sketchng Sampled Data Streams Florn Rusu, Aln Dobra CISE Department Unversty of Florda Ganesvlle, FL, USA [email protected] [email protected] Abstract Samplng s used as a unversal method to reduce the
Least Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2016. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng
Logical Development Of Vogel s Approximation Method (LD-VAM): An Approach To Find Basic Feasible Solution Of Transportation Problem
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME, ISSUE, FEBRUARY ISSN 77-866 Logcal Development Of Vogel s Approxmaton Method (LD- An Approach To Fnd Basc Feasble Soluton Of Transportaton
RELIABILITY, RISK AND AVAILABILITY ANLYSIS OF A CONTAINER GANTRY CRANE ABSTRACT
Kolowrock Krzysztof Joanna oszynska MODELLING ENVIRONMENT AND INFRATRUCTURE INFLUENCE ON RELIABILITY AND OPERATION RT&A # () (Vol.) March RELIABILITY RIK AND AVAILABILITY ANLYI OF A CONTAINER GANTRY CRANE
Ring structure of splines on triangulations
www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAM-Report 2014-48 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon
A Design Method of High-availability and Low-optical-loss Optical Aggregation Network Architecture
A Desgn Method of Hgh-avalablty and Low-optcal-loss Optcal Aggregaton Network Archtecture Takehro Sato, Kuntaka Ashzawa, Kazumasa Tokuhash, Dasuke Ish, Satoru Okamoto and Naoak Yamanaka Dept. of Informaton
Section 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign
PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng
A Lyapunov Optimization Approach to Repeated Stochastic Games
PROC. ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING, OCT. 2013 1 A Lyapunov Optmzaton Approach to Repeated Stochastc Games Mchael J. Neely Unversty of Southern Calforna http://www-bcf.usc.edu/
Using Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry [email protected] www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1.
HIGHER DOCTORATE DEGREES SUMMARY OF PRINCIPAL CHANGES General changes None Secton 3.2 Refer to text (Amendments to verson 03.0, UPR AS02 are shown n talcs.) 1 INTRODUCTION 1.1 The Unversty may award Hgher
A Distributed Algorithm for Least Constraining Slot Allocation in MPLS Optical TDM Networks
A Dstrbuted Algorthm for Least Constranng Slot Allocaton n MPLS Optcal TDM Networks Hassan Zeneddne and Gregor V. Bochmann, Unversty of Ottawa, Ottawa, ON-K1N 6N5 Abstract - In ths paper, we propose a
A Probabilistic Theory of Coherence
A Probablstc Theory of Coherence BRANDEN FITELSON. The Coherence Measure C Let E be a set of n propostons E,..., E n. We seek a probablstc measure C(E) of the degree of coherence of E. Intutvely, we want
1 OPTIMIZATION ISSUES IN WEB
1 OPTIMIZATIO ISSUES I WEB SEARCH EGIES Zhen Lu 1 and Phlppe an 2 1 IBM Research Hawthorne, Y 10532, USA [email protected] 2 IRIA B.P. 93, 06902, Sopha Antpols Cedex, France [email protected] Abstract: Crawlers
Capacity Planning for Virtualized Servers
Capacty Plannng for Vrtualzed Servers Martn Bchler, Thoas Setzer, Benjan Spetkap Departent of Inforatcs, TU München 85748 Garchng/Munch, Gerany (bchler setzer benjan.spetkap)@n.tu.de Abstract Today's data
Chapter 22 Heat Engines, Entropy, and the Second Law of Thermodynamics
apter 22 Heat Engnes, Entropy, and te Seond Law o erodynas 1. e Zerot Law o erodynas: equlbru -> te sae 2. e Frst Law o erodynas: de d + d > adabat, sobar, sovoluetr, soteral 22.1 Heat Engnes and te Seond
An Adaptive Cross-layer Bandwidth Scheduling Strategy for the Speed-Sensitive Strategy in Hierarchical Cellular Networks
An Adaptve Cross-layer Bandwdth Schedulng Strategy for the Speed-Senstve Strategy n erarchcal Cellular Networks Jong-Shn Chen #1, Me-Wen #2 Department of Informaton and Communcaton Engneerng ChaoYang Unversty
1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
Luby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
