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1 Chapter 8 Queueing models. 2 Delay and Queueing Main source of delay Transmission (e.g., n/R) Propagation (e.g., d/c) Retransmission (e.g., in ARQ)

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Presentation on theme: "1 Chapter 8 Queueing models. 2 Delay and Queueing Main source of delay Transmission (e.g., n/R) Propagation (e.g., d/c) Retransmission (e.g., in ARQ)"— Presentation transcript:

1 1 Chapter 8 Queueing models

2 2 Delay and Queueing Main source of delay Transmission (e.g., n/R) Propagation (e.g., d/c) Retransmission (e.g., in ARQ) Processing (e.g., running time of protocols) Queueing Queueing Theory Study of mathematical queueing models Since early 1900’s by Erlang

3 3 Queue Customer System Service Delay box : Multiplexer switch network Message, packet, cell arrivals Message, packet, cell departures T seconds Lost or blocked

4 4 Queueing Discipline One customer at a time First-in first-out (FIFO) LIFO Round robin Priorities Multiple customers at a time FIFO Separate queues/separate servers Blocking rule Discard when full Drop randomly Block a certain class

5 5 Definitions T: Time spent in the system A(t): # of arrivals in [0,t] B(t): # of blocked customers in [0,t] D(t): # of departures in [0,t] N(t): # of customers in the system at t N(t)=A(t)-D(t)-B(t) Long term arrival rate Throughput Average number in the system Fraction of blocked customers

6 6 A(t) t n-1 n n+1 Time of nth arrival =  1 +   n Arrival Rate n arrivals  1 +   n seconds = 1 = 1 (  1 +   n )/n E[]E[] 11 22 33 nn  n+1 Arrivals

7 7 Little’s Law If the system does not block customers, then E{N} = λ E{T} If the block rate is Pb, then E{N} = (1-Pb) λ E{T} Proof

8 8 Arrivals and Departures A(t) D(t) T1T1 T2T2 T3T3 T4T4 T5T5 T6T6 T7T7 Assumes first-in first-out C1C1 C2C2 C3C3 C4C4 C5C5 C6C6 C7C7 C1C1 C2C2 C3C3 C4C4 C5C5 C6C6 C7C7 Arrivals Departures

9 9 Examples Let the arrival rate be 100 packets/sec. If 10 packets are found in the queue in average, then the average delay is 10/100=0.1 sec. Traffic is bad in a rainy day. For the same volume of customers, a fast food restaurant requires smaller dining area.

10 10 Example E{N} = λ E{T}

11 11 Basic Queueing Models 1 2 c A(t) t D(t) t B(t) Queue Servers Arrival process X Service time ii  i+1 

12 12 Arrival Processes Interarrival times  1,  2,… Arrival rate  =1/E{  } Statistics Deterministic Exponential interarrival times Poisson arrival process

13 13

14 14 Service Processes Service times X 1, X 2,… Processing capacity  =1/E{X}

15 15 Service times X M = exponential D = deterministic G = general Service Rate:  = 1/E[X] Arrival Process / Service Time / # of Servers / Max Occupancy Interarrival times  M = exponential D = deterministic G = general Arrival Rate:  = 1/E[  1 server c servers infinite K customers unspecified if unlimited Multiplexer Models: M/M/1/K, M/M/1, M/G/1, M/D/1 Trunking Models: M/M/c/c, M/G/c/c User Activity: M/M/ , M/G/  Queueing System Classification

16 c X N q (t) N s (t) N(t) = N q (t) + N s (t) T = W + X W  P b  (1 – P b ) N(t) = number in system N q (t) = number in queue N s (t) = number in service T = total delay W = waiting time X = service time  Queueing System Variables E{N} E{N q } E{N s } Traffic load Utilization

17 17 Poisson arrivals rate K – 1 buffer Exponential service time with rate  The M/M/1/K Model Average packet transmission time E{X} = E{L}/R Maximum service rate  =R/E{L} P{1 arrival in Δt} =  Δt + o(Δt)

18 n-1 n n (  t 1 -  t  t  t M/M/1 Steady State Probabilities Average number of customers in the system? Average delay? Average wait time?

19 19    m separate systems versus One consolidated system m mm Example: Effect of Scale What is the average delay of the combined system?

20 20 Poisson arrivals rate Infinite buffer  = 1/E[X] The M/G/1 Model The average wait time: Similar to M/M/1 except that the service time X may not be exponentially distributed.

21 21 Proof

22 22 Blocked calls are cleared from the system; no waiting allowed. Performance parameter: P b = fraction of arrivals that are blocked P b = P[N(t)=c] = B(c,a) where a=  The Erlang B formula, valid for any service time distribution B(c,a)  a c c! a j j! j=0 c  Many lines Limited number of trunks 1 2 c N(t ) PbPb  = (1–P b ) Poisson arrivals E[X]=1/   Erlang B Formula: M/M/c/c

23 c-1 c 1 - (  t1 - (  t 1 - (  c-1)  t1 - c  t1 -  t  t  t  tc  t State Transition Diagram


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