EE 489 Telecommunication Systems Engineering University of Alberta Dept. of Electrical and Computer Engineering Introduction to Traffic Theory Wayne Grover TRLabs and University of Alberta

Material prepared by W. Grover ( ) 2 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering A note on sources of this material The following material on traffic theory / traffic engineering was initially developed as printed handwritten notes from 1998 to 2001 by W. Grover for EE589. In 2002 John Doucette set these materials into the present powerpoint format for use in EE589. The ppt versions of the original notes, with updating and some revisions by W. Grover, 2007, are made available courtesy J. Doucette for use in EE489. Related Reading in Bellamy 3 rd Edition: –Chapter 12, pp

Material prepared by W. Grover ( ) 3 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Traffic Engineering One billion+ terminals in voice network alone –Plus data, video, fax, finance, etc. Imagine all users want service simultaneously In practice, low overall utilization –Random duration at random times Balance cost and practicality with acceptably low chance of network blocking. We use traffic engineering to “dimension” the network, i.e. mainly to decide on how many transmission paths (trunks) are needed between node and the sizes of switches or routers needed.

Material prepared by W. Grover ( ) 4 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Characterization of Circuit-Switched Traffic Calling Rate Calling Rate (  ) – also called Arrival Rate –Average number of calls or “connections” initiated per unit time (units. “attempts per hour”) –Each arrival independent of other calls –Random in time  T If receive  calls from a terminal in time T: m If receive  calls from m terminals in time T: Group calling ratePer terminal calling rate

Material prepared by W. Grover ( ) 5 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Characterization of Telephone Traffic (2) Calling rate assumption: –Number of calls in time T is Poisson distributed:  Time between calls is negative exponentially distributed:

Material prepared by W. Grover ( ) 6 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Characterization of Telephone Traffic (3) Holding TimehHolding Time (h) –Mean length of time a call lasts –Probability of lasting time t or more is exponential in nature: –Real sampled voice data fits very closely to the negative exponential form above –As non-voice “calls” begin to dominate, more and more calls have a constant holding time characteristic Departure Rate Departure Rate (  ):

Material prepared by W. Grover ( ) 7 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Real Holding Time Sample Data

Material prepared by W. Grover ( ) 8 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Exponential Form of Holding Time Memory-less propertyMemory-less property –Call “forgets” that it has already survived to time T 1 Proof: Recall:

Material prepared by W. Grover ( ) 9 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Exponential Form of Holding Time Understanding Memorylessness in Call holding timesUnderstanding Memorylessness in Call holding times It means that: – whether a connection has already existed for 1 minute or one hour… –the probabiity that it will last another minute (or any other unit time)… –is the same. Counterintuitive (?) but very accurate actually. Can understand it (or any memoryless process) as being analogous to repeated coin tossing

Material prepared by W. Grover ( ) 10 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Traffic Volume (V)  = # calls in time period T h = mean holding time V = volume of calls in time period T ccsUnits can be “ccs”: –Hundred call seconds c“c”c“c” c“c”c“c” s“s”s“s” –1 ccs is volume of traffic equal to: –one circuit busy for 100 seconds, or –two circuits busy for 50 seconds, or –100 circuits busy for one second, etc.

Material prepared by W. Grover ( ) 11 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Traffic Intensity (A) traffic flowThis is the rate of “traffic flow”.  = # calls in time period T h = mean holding time T = time period of observations Units: ccs/hour –“ccs/hour”, or –dimensionless (if h and T are in the same units) Erlang “Erlang” unit  = # calls in time period T h = mean holding time T = time period of observations  = calling rate  = # calls in time period T h = mean holding time T = time period of observations  = calling rate  = departure rate Recall:  = # calls in time period T h = mean holding time T = time period of observations  = calling rate  = departure rate V = call volume

Material prepared by W. Grover ( ) 12 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering The “Erlang” Dimensionless unit of traffic intensity. Characterizes the intensity of any stream of demands for circuit-switched (or connection-oriented data) connections Named after Danish mathematician A. K. Erlang ( ) EUsually denoted by symbol E. 1 Erlang is equivalent to the traffic intensity that keeps: –one circuit busy 100% of the time, or –two circuits busy 50% of the time, or –four circuits busy 25% of the time, etc. e.g., 26 Erlangs is equivalent to traffic intensity that keeps : –26 circuits busy 100% of the time, or –52 circuits busy 50% of the time, or –104 circuits busy 25% of the time, etc.

Material prepared by W. Grover ( ) 13 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Erlang (2) ErlangccsHow does the Erlang unit correspond to ccs? Percentage of time a terminal is busy is equivalent to the traffic generated by that terminal in Erlangs, or Average number of circuits in a group busy at any time Typical usages: –residence phone -> 0.02 E –business phone -> 0.15 E –interoffice trunk -> 0.70 E

Material prepared by W. Grover ( ) 14 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Traffic Offered, Carried, and Lost Offered Traffic T O AOffered Traffic ( T O ) equivalent to Traffic Intensity (A) –Takes into account all attempted calls, whether blocked or not, and uses their expected holding times Carried Traffic T C Lost Traffic T LAlso Carried Traffic ( T C ) and Lost Traffic ( T L ) dedicated serviceConsider a group of 150 terminals, each with 10% utilization (or in other words, 0.1 E per source) and dedicated service: each terminal has an outgoing trunk (i.e. terminal:trunk ratio = 1:1) T O = A = 150 x 0.10 E = 15.0 E T C = 150 x 0.10 E = 15.0 E T L = 0 E

Material prepared by W. Grover ( ) 15 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Traffic Offered, Carried, and Lost (2) A = T O = T C + T L Traffic Intensity Offered Traffic Carried Traffic Lost Traffic T L = T O x Prob. Blocking (or congestion) = P(B) x T O = P(B) x A Circuit Utilization  Circuit EfficiencyCircuit Utilization (  ) - also called Circuit Efficiency –proportion of time a circuit is busy, or –average proportion of time each circuit in a group is busy

Material prepared by W. Grover ( ) 16 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Example #1 Traffic Engineered solution for the 150 terminals at 0.1 E...

Material prepared by W. Grover ( ) 17 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Grade of Service (gos) In general, the term used for some traffic design objective Indicative of customer satisfaction In systems where blocked calls are cleared, usually use: Typical gos objectives: –in busy hour, range from 0.2% to 5% for local calls, however –generally no more that 1% –long distance calls often slightly higher In systems with queuing, gos often defined as the probability of delay exceeding a specific length of time

Material prepared by W. Grover ( ) 18 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Grade of Service Related Terms Busy HourBusy Hour –One hour period during which traffic volume or call attempts is the highest overall during any given time period Peak (or Daily) Busy HourPeak (or Daily) Busy Hour –Busy hour for each day, usually varies from day to day Busy SeasonBusy Season –3 months (not consecutive) with highest average daily busy hour High Day Busy Hour (HDBH)High Day Busy Hour (HDBH) –One hour period during busy season with the highest load

Material prepared by W. Grover ( ) 19 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Hourly Traffic Variations

Material prepared by W. Grover ( ) 20 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Daily Traffic Variations

Material prepared by W. Grover ( ) 21 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Seasonal Traffic Variations

Material prepared by W. Grover ( ) 22 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Seasonal Traffic Variations (2)

Material prepared by W. Grover ( ) 23 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Grade of Service Related Terms (2) HighestABSBH Average Busy Season Busy Hour (ABSBH)Average Busy Season Busy Hour (ABSBH) –One hour period with highest average daily busy hour during the busy season Average Busy Season Busy Hour (ABSBH)Average Busy Season Busy Hour (ABSBH) –One hour period with highest average daily busy hour during the busy season –For example, assume days shown below make up the busy season: Note: Red indicates daily busy hour

Material prepared by W. Grover ( ) 24 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Grade of Service Related Terms (3) Ten High Day Busy Hour (10HDBH)Ten High Day Busy Hour (10HDBH) –One hour period with highest average load for the 10 highest day loads for that hourHighest10HDBH Ten High Day Busy Hour (10HDBH)Ten High Day Busy Hour (10HDBH) –One hour period with highest average load for the 10 highest day loads for that hour –For example: Note: Red indicates 10 highest hourly loads for each hour

Material prepared by W. Grover ( ) 25 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Grade of Service Related Terms (4) Examples of grade-of-service type specification statements: –1.5% of calls in ABSBH have dial tone delay more than 3 seconds –blocking on trunk groups < 3% –blocking through switch matrix < 0.1% –probability of packet delay > x msec less than 5% –probability of dropped connection in progress < 1% per minute –etc. Note implications of designing to “busy hour” g.o.s. objectives: –simplifies design and forecasting problems –busy hour may change (unpredictably!) –the resulting network is “peak engineered” - same as the power network …may be greatly underutilized at off busy-hour times Q. What could you do with this?

Material prepared by W. Grover ( ) 26 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Typical Call Attempts Breakdown Calls Completed % Called Party No Answer % Called Party Busy % Call Abandoned - 2.6% Dialing Error - 1.6% Number Changed or Disconnected - 0.4% Network Blockage or Failure - 1.9%

Material prepared by W. Grover ( ) 27 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Traffic Theoretic Models for Blocked Calls BCCBlocked Calls Cleared (BCC) –Blocked calls leave system and do not return –Good approximation for calls in 1 st choice trunk group with overflow available. BCHBlocked Calls Held (BCH) –Blocked calls remain in the system for the amount of time it would have normally stayed for –If a server frees up, the call picks up in the middle and continues –Not a good model of real world behaviour (mathematical approximation only) –Tries to approximate call reattempt efforts BCWBlocked Calls Wait (BCW) –Blocked calls enter a queue until a server is available –When a server becomes available, the call’s holding time begins

Material prepared by W. Grover ( ) 28 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Source #1 Offered Traffic Source #2 Offered Traffic minutes Total Traffic Offered: T O = 0.4 E E T O = 0.7 E 2 sources Blocked Calls Cleared (BCC) Only one server Traffic Carried 1 st call arrives and is served 1 2 nd call arrives but server already busy 2 2 nd call is cleared 1 3 rd call arrives and is served 3 4 th call arrives and is served 4 Total Traffic Carried: T C = 0.5 E

Material prepared by W. Grover ( ) 29 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Source #1 Offered Traffic Source #2 Offered Traffic minutes Total Traffic Offered: T O = 0.4 E E T O = 0.7 E 2 sources Blocked Calls Held (BCH) Traffic Carried Only one server 1 st call arrives and is served 2 nd call arrives but server busy 2 nd call is served 3 rd call arrives and is served 4 th call arrives and is served Total Traffic Carried: T C = 0.6 E 2 nd call is held until server free

Material prepared by W. Grover ( ) 30 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Source #1 Offered Traffic Source #2 Offered Traffic minutes Total Traffic Offered: T O = 0.4 E E T O = 0.7 E 2 sources Blocked Calls Wait (BCW) Only one server Traffic Carried 1 st call arrives and is served 1 2 nd call arrives but server busy 2 2 nd call waits until server free 2 nd call served 12 3 rd call arrives, waits, and is served 3 4 th call arrives, waits, and is served 4 Total Traffic Carried: T C = 0.7 E

Material prepared by W. Grover ( ) 31 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Blocking Probabilities Steady StateSystem must be in a Steady State –Also called state of statistical equilibrium –Arrival RateDeparture Rate –Arrival Rate of new calls equals Departure Rate of disconnecting calls –Why? If calls arrive faster that they depart? If calls depart faster than they arrive?

Material prepared by W. Grover ( ) 32 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Binomial Distribution Model Assumptions: –m –m sources –A –A Erlangs of offered traffic per source: T O = A/m probability that a specific source is busy: P(B) = A/m kCan use Binomial Distribution to give the probability that a certain number (k) of those m sources is busy:

Material prepared by W. Grover ( ) 33 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Binomial Distribution Model (2) N serversWhat does it mean if we only have N servers (N<m)? –We can have at most N busy sources at a time –What about the probability of blocking? All N servers must be busy before we have blocking Remember:

Material prepared by W. Grover ( ) 34 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Binomial Distribution Model (3) What does it mean if k>N? –Impossible to have more sources busy than servers to serve them –Doesn’t accurately represent reality In reality, P(k>N) = 0 –In this model, we still assign P(k>N) = A/m –Acts as good model of real behaviour Some people call back, some don’t Which type of blocking model is the Binomial Distribution? –Blocked Calls Held (BCH)

Material prepared by W. Grover ( ) 35 EE489 – Telecommunication Systems Engineering –University of Alberta, Dept. of Electrical and Computer Engineering Time Congestions vs. Call Congestion Time Congestion –Proportion of time a system is congested (all servers busy) –Probability of blocking from point of view of servers Call Congestion –Probability that an arriving call is blocked –Probability of blocking from point of view of calls Why/How are they different? Time Congestion: Probability that all servers are busy. Call Congestion: Probability that there are more sources wanting service than there are servers.