COMT 4291 Computing Resource Requirements for Circuit Switched Networks Introduction.

Slides:



Advertisements
Similar presentations
Introduction to Traffic Engineering
Advertisements

E&CE 418: Tutorial-4 Instructor: Prof. Xuemin (Sherman) Shen
Lesson Objective Be able to calculate probabilities for Binomial situations Begin to recognise the conditions necessary for a Random variable to have a.
Contingency Tables Chapters Seven, Sixteen, and Eighteen Chapter Seven –Definition of Contingency Tables –Basic Statistics –SPSS program (Crosstabulation)
Florida Institute of technologies ECE 5221 Personal Communication Systems Prepared by: Dr. Ivica Kostanic Lecture 19: Traffic planning (3) Spring 2011.
AP Statistics: Section 8.1A Binomial Probability.
1 Capacity planning exercise M.Sc. Mika Husso
Data Analysis and Probability Presenters Aaron Brittain Adem Meta.
Interrupts (contd..) Multiple I/O devices may be connected to the processor and the memory via a bus. Some or all of these devices may be capable of generating.
Risk Pooling in Insurance If n policies, each has independent probability p of a claim, then the number of claims follows the binomial distribution. The.
CEE 320 Fall 2008 Trip Generation and Mode Choice CEE 320 Anne Goodchild.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference (Sec. )
Statistics.
1 Queuing Theory 2 Queuing theory is the study of waiting in lines or queues. Server Pool of potential customers Rear of queue Front of queue Line (or.
Inferences About Means of Two Independent Samples Chapter 11 Homework: 1, 2, 4, 6, 7.
Copyright © 2010 Pearson Education, Inc. All rights reserved Sec Linear Inequalities in One Variable.
Slide 1 Statistics Workshop Tutorial 7 Discrete Random Variables Binomial Distributions.
7/3/2015© 2007 Raymond P. Jefferis III1 Queuing Systems.
Trunking & Grade of Service
Queuing Theory. Queuing theory is the study of waiting in lines or queues. Server Pool of potential customers Rear of queue Front of queue Line (or queue)
1 Describing of parameters of traffic generated by user of multimedia services offered by telco operators Jacek Oko, Janusz Klink Institute of Telecommunication.
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
Chapter 5 Several Discrete Distributions General Objectives: Discrete random variables are used in many practical applications. These random variables.
Lesson 6 – 2b Hyper-Geometric Probability Distribution.
Applications of Poisson Process
People Fractions. Problem 1 of 20 Answer 1 = 10 Problem 2 of 20 Answer 2 = 5.
1 Overview This chapter will deal with the construction of probability distributions by combining the methods of Chapter 2 with the those of Chapter 4.
Basic teletraffic concepts An intuitive approach
Interference ,Trunking and GOS
Random variables Petter Mostad Repetition Sample space, set theory, events, probability Conditional probability, Bayes theorem, independence,
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Basic Business Statistics.
Designing a Voice over IP Network Chapter 9. Internet Telephony 2 Introduction The design of any network involves striking a balance between three requirements.
جلسه دهم شبکه های کامپیوتری به نــــــــــــام خدا.
Florida Institute of technologies ECE 5221 Personal Communication Systems Prepared by: Dr. Ivica Kostanic Lecture 17: Traffic planning Spring 2011.
Chapter 5 Lecture 2 Sections: 5.3 – 5.4.
Florida Institute of technologies ECE 5221 Personal Communication Systems Prepared by: Dr. Ivica Kostanic Lecture 20: Traffic planning (4) Spring 2011.
1 Voice Traffic Engineering & Management. 2 PSTN and PBX networks are designed with 2 objectives: Maximize usage of their circuits Maximize usage of their.
COMT 2201 Managing Telecommunications Systems. COMT 2202 Managing Telecommunications Configuration Management Security Management Accounting Management.
BINOMIALDISTRIBUTION AND ITS APPLICATION. Binomial Distribution  The binomial probability density function –f(x) = n C x p x q n-x for x=0,1,2,3…,n for.
Binomial Experiment A binomial experiment (also known as a Bernoulli trial) is a statistical experiment that has the following properties:
Binomial Probability Distribution
The Binomial Distribution
EE6610: Week 6 Lectures.
Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
EELE 5490, Fall, 2009 Wireless Communications Ali S. Afana Department of Electrical Engineering Class 4 Sep. 30 th, 2009.
Math b (Discrete) Random Variables, Binomial Distribution.
Graph of a Binomial Distribution Binomial distribution for n = 4, p = 0.4:
1 Chapter 4. Section 4-3. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION E LEMENTARY.
Statistics 3502/6304 Prof. Eric A. Suess Chapter 4.
Combinatorics Sep. 23, 2013.
Chapter 2.2 The Multiplication Principle of Equality.
 A probability function is a function which assigns probabilities to the values of a random variable.  Individual probability values may be denoted.
Module 5: Discrete Distributions
The Cellular Concept Early Mobile Communications The Cellular Concept
Continuous Random Variables Lecture 26 Section Mon, Mar 5, 2007.
1 Chapter 8 Random Variables and Probability Distributions IRandom Sampling A.Population 1.Population element 2.Sampling with and without replacement.
2/16/2016 Subject Name: Digital Switching Systems Subject Code:10EC82 Prepared By: Aparna.P, Farha Kowser Department: Electronics and Communication Date:
COMT 4291 Queuing Analysis COMT Call/Packet Arrival Arrival Rate, Inter-arrival Time, 1/ Arrival Rate measures the number of customer arrivals.
Thought of the day Thought of the day Difference between science and spirituality is same as the difference between word and silence. Sameer Trapasiya.
The Cellular Concept Early Mobile Communications The Cellular Concept
Slide 1 Copyright © 2004 Pearson Education, Inc. Chapter 5 Probability Distributions 5-1 Overview 5-2 Random Variables 5-3 Binomial Probability Distributions.
Probability Distributions ( 확률분포 ) Chapter 5. 2 모든 가능한 ( 확률 ) 변수의 값에 대해 확률을 할당하는 체계 X 가 1, 2, …, 6 의 값을 가진다면 이 6 개 변수 값에 확률을 할당하는 함수 Definition.
1 Lecture 06 EEE 441: Wireless And Mobile Communications BRAC University.
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
Questions about conditions and parameters
Tele traffic A telephone network is composed of a variety of common equipment, such as: Digital receivers Call processors Inter-stage switching links &
Fundamentals of Cellular Networks (Part IV)
Trunking Theory In general, there are many more users than channels--everyone can’t have a permanent personal channel! Fortunately, only a small fraction.
X and Y's of Scientific Method
Presentation transcript:

COMT 4291 Computing Resource Requirements for Circuit Switched Networks Introduction

COMT 4292 Resource Usage Prediction How many simultaneous requests? Resource

COMT 4293 Probability of Resource Usage Measure the subscriber resource usage over a specific period of time. The probability of resource usage is defined as the fraction of the observation time during which the subscriber was occupying the resource

COMT 4294 In Voice Networks (Circuit Switching) Total Amount of Usage per unit time (usually one hour). Erlangs –Hours of usage per hour of observation –30 3-minute calls in one hour = 1.5 Erlangs CCS –Number of 100 sec usage increments per hour of observation –30 3-minute calls in one hours = 54 CCS

COMT 4295 Resource Demand Compute the traffic per subscriber (in Erlangs) For example, a subscriber makes 6 minutes of calls in one hour –Traffic is 6/60 = 0.1 Erlangs Probability of trunk usage by one subscriber equals the traffic (in Erlangs) from that subscriber (10% in the example)

COMT 4296 In General We can measure the total traffic generated by a group of subscribers We may not know the exact number of subscribers, or We may not want to recompute our design if the number of subscribers changes slightly

COMT 4297 For now, however Assume that the number of subscribers is known, and “small” Assume for simplicity that each subscriber in the group generates the same amount of traffic Assume that the subscribers are “independent” in their choice to use network resources

COMT 4298 Possible Answers How many resource requests are possible? Resource range is 0 to N units of resources

COMT 4299 A simple question What is the probability of N simultaneous resource requests? For notation, we use “h” as the traffic per subscriber (in Erlangs) In our example –N = 4 –h = 0.1

COMT The Answer

COMT Another Simple Case The probability that there will be no resource request –One subscriber will not use a resource with probability (1-h)

COMT Another question The probability of exactly three simultaneous resource requests Three user have h*h*h probability of requesting a resource The remaining users must not be requesting resources

COMT A tempting answer For x requests, combine x probabilities of requesting a resource with N-x probabilities of not requesting a resource

COMT Are these different answers?

COMT Each combination of users has to be counted How many different selections of x subscribers can I pull from a total pool of N subscribers

COMT The General Answer Also called the binomial distribution

COMT Assignment Now: Compute the probabilities for all 6 cases possible with N=5, h=0.1 For next class: Build a spreadsheet which computes the binomial distribution (probability and cumulative) given N and h, and graph the distribution for N=15, h=0.02