Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

of

Traffic analysis Slide 1 Traffic analysis Slide 2 Traffic analysis Slide 3 Traffic analysis Slide 4 Traffic analysis Slide 5 Traffic analysis Slide 6 Traffic analysis Slide 7 Traffic analysis Slide 8 Traffic analysis Slide 9 Traffic analysis Slide 10 Traffic analysis Slide 11 Traffic analysis Slide 12 Traffic analysis Slide 13 Traffic analysis Slide 14 Traffic analysis Slide 15 Traffic analysis Slide 16 Traffic analysis Slide 17 Traffic analysis Slide 18 Traffic analysis Slide 19 Traffic analysis Slide 20
Upcoming SlideShare
Switching systems lecture3
Next
Download to read offline and view in fullscreen.

16 Likes

Share

Download to read offline

Traffic analysis

Download to read offline

Traffic analysis

  1. 1. Traffic Analysis & Characterization Prepared By: Srashti Vyas
  2. 2. TRAFFIC In communication networks it refers to the aggregate of all user requests being serviced by the network; as far as the n/w is concerned • The service requests arrive randomly. • Usually requires unpredictable service times.
  3. 3. Basic Concept • Performance analysis methods applied to telephony are usually referred to as “traffic engineering” • Motivated by two factors – Unpredictable behavior of users • You never know when they call! – Users have to share resources • Users have to be happy!
  4. 4. TRAFFIC ANALYSIS • Traffic Engineering provides the basis for analysis and design of telecom n/w or model. • Used to provide a method for determining the cost-effectiveness of various sizes and configurations of networks. • It provides means to determine the quantum of common equipment required to provide a particular level of service for given traffic pattern and volume.
  5. 5. Techniques of Traffic Analysis It is divided into Two general categories: 1. Loss System: In a loss system overload traffic is rejected without being serviced. e.g: conventional automatic telephone exchange 2. Delay System: In a delay system overload traffic is held in a queue until the facilities become available to service it. e.g: Operator oriented manual exchange
  6. 6. • Traffic Engineering also determine the ability of a telecom network to carry a given traffic at a particular loss probability. • There are two theories used to estimate the probability of the occurrence of call blocking: 1.Traffic theory 2. Queuing theory
  7. 7. What is Blocking? Trunk Call Center Call Calls arriving Blocked! randomly We need to figure out statistically what the probability of blocking is!  What is the grade of service! No Blocking! Call Duration Trunk Call Center Trunk
  8. 8. TRAFFIC CHARACTERIZATION •Because of random nature of network traffic, fundamentals of probability theory are applied. The unpredictable nature of traffic arises as a result of : • Call Arrivals • Holding Times In either case, the traffic load depends on frequency of arrivals and average holding time for each arrival.
  9. 9. TRAFFIC MEASUREMENT • Measurement of traffic within a network allows a network managers & analysts to both make day to day decisions about operations and plan for long-term developments. • These measurements are conducted on a continuous basis and the results compiled into reports for further n/w management. • Traffic measurements are used in many fundamental activities such as calculating traffic intensity in specific circuit or group, identification of traffic pattern and trends, monitoring services etc.
  10. 10. TRAFFIC STATISTICS The statistical description is important for the analysis and design of any switching network. 1. Call rate(λ): It is the average number of requests for connection that are made per unit time. It is also referred as Average Arrival Rate. If ‘n’ is the average number of calls to and from a terminal during a period ‘T’ seconds, the calling rate is given as: Calls / hour
  11. 11. 2. Holding Time(h):It is the average duration of occupancy of a traffic path by a call. • It is also known as Service Time. • For voice traffic, it is average holding time per call in hours or 100 seconds. • For data traffic, it is average transmission per message in seconds. • The reciprocal of holding time referred to as “Service rate(μ)”.
  12. 12. 3. Distribution of Destinations: It is described as the probability of a call request being for particular destination. This helps in determining the number of trunks needed between individual centers. 4. User Behavior: The switching system are function of the behavior of users and the system behaves differently for different users. 5.Average Occupancy: It is the ratio of average arrival rate to the average service rate. If ‘n’ is the average number of calls to and from a terminal during a period ‘T’ seconds and average holding time is ‘h’ seconds, the average occupancy is given by:
  13. 13. TRAFFIC PATTERN It helps in determining the amount of lines required to serve the subscriber needs. 1. Busy Hour: It is defined as the 60 minutes interval in a day, in which the traffic is the highest. It is further defined as: • Peak Busy Hour: It is busy hour each day varies from day to day, over a number of days. • Time consistent busy hour: The 1 hour period starting at the same time each day for which the average traffic volume or the number of call attempts is greatest over the days under consideration.
  14. 14. 2. Call Completion Rate(CCR): It is defined as the ratio of the number of successful calls to the number of call attempts. A CCR value of 0.75 is excellent and CCR of 0.70 is usually expected.
  15. 15. UNITS OF TELEPHONE TRAFFIC Traffic Intensity is measured in two ways: 1. Erlangs (E): It represent one circuit occupied for one hour. • The maximum capacity of a single server (or channel) is 1 Erlang i.e. server is always busy. • Used to represent traffic intensity for present n/w such as voice, data etc.
  16. 16. 2. Cent call seconds(CCS): It is used to measure the amount of traffic expressed in units of 100 seconds. Also referred as hundred call seconds (CS). Sometimes also expressed in call minutes(CM). • It is valid only in telephone circuits. • Relation between Erlang, CS &CM: 1E = 36 CCS = 3600 CS = 60 CM
  17. 17. Example: A subscriber makes 3 phone calls of 4 minutes, 3 minutes and 3 minutes duration in a 1 hour period. Estimate the subscriber traffic in Erlangs, CCS and CM. Solution: Busy Period= 4+3+3 minutes Total Period=60 min(1 hr) Subscriber traffic in Erlangs= Busy Period/Total Period = 10/60 = 0.016 E Traffic in CCS=(4+3+3) X 60/100 = 6 CCS Traffic in CM=4+3+3 = 10 CM
  18. 18. ARRIVAL DISTRIBUTIONS 1.Negative Exponential Inter-arrival Time: It gives the average call arrival rate from a rare group of independent sources (subscriber lines) as λ. Use the following assumptions: i. Only one arrival can occur in any sufficiently small interval. ii.The probability of an arrival in any sufficiently small interval is directly proportional to the length of the interval. The probability of an arrival is lΔt, where Δt is the interval length. iii. The probability of an arrival in any particular interval is independent of what has occurred in other intervals. The probability distribution inter-arrival time is: P0(λt)=e-λt
  19. 19. 2. Poisson Arrival Distribution: • It provides a means of determining the distribution of inter-arrival times. • It provides generally more desirable information of how many arrivals can be expected to occur in some arbitrary time interval. • Using same assumptions, the probability of j arrivals in an interval t, can be determined by Poisson Probability law given as:
  • charleschipanzawe

    Jul. 30, 2021
  • KumarAmreshanand

    Jun. 24, 2020
  • MalikAlrayes

    Feb. 21, 2020
  • RibanBudhathoki

    Sep. 17, 2019
  • PranavPadmakumar

    Oct. 29, 2018
  • DominicPaulBalinsoy

    Aug. 12, 2018
  • akshaytathod1

    May. 2, 2018
  • viltwisted

    Jan. 28, 2018
  • omarfaruk76

    Aug. 18, 2017
  • DarshanChavda3

    Jun. 16, 2017
  • DakshaSindal

    Nov. 12, 2016
  • Rehansaiyed1

    Apr. 12, 2016
  • ManarMagdyMansour

    Mar. 30, 2016
  • AlinGiuverdea

    May. 21, 2015
  • AyuNovita2

    Feb. 10, 2015
  • srashtivyas7

    Sep. 28, 2014

Views

Total views

11,270

On Slideshare

0

From embeds

0

Number of embeds

3

Actions

Downloads

560

Shares

0

Comments

0

Likes

16

×