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.
• 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!
• 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
• It provides means to determine the quantum of common
equipment required to provide a particular level of service
for given traffic pattern and volume.
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
e.g: Operator oriented manual exchange
• 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:
2. Queuing theory
What is Blocking?
Calls arriving Blocked!
We need to figure out statistically what
the probability of blocking is!
What is the grade of service!
•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.
• 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
• 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.
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
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
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
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
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.
UNITS OF TELEPHONE TRAFFIC
Traffic Intensity is measured in two ways:
1. Erlangs (E): It represent one circuit occupied for
• 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.
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
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.
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
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
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:
2. Poisson Arrival Distribution:
• It provides a means of determining the distribution of inter-arrival
• It provides generally more desirable information of how many
arrivals can be expected to occur in some arbitrary time
• Using same assumptions, the probability of j arrivals in an
interval t, can be determined by Poisson Probability law given