delay models in data networks
DESCRIPTION
Shanghai Jiao Tong University. Delay models in data networks. Data networks and Queueing. R. R. R. R. R. R. R. S. General Methodologies of Queueing Analysis. We are given: Packet arrival behavior Packet length distribution Packet routing / handling policies We want to deduce: - PowerPoint PPT PresentationTRANSCRIPT
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Weiqiang Sun
DELAY MODELS IN DATA NETWORKSShanghai Jiao Tong University
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Weiqiang SunWeiqiang Sun
Data networks and Queueing
R
R
R R
R
R
R
S
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General Methodologies of Queueing Analysis
• We are given:– Packet arrival behavior– Packet length distribution– Packet routing / handling policies
• We want to deduce:– Packet delay– Queue length– Packet loss
• Queueing theory can also be applied in other areas, such as in analyzing Circuit Switched Net.
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In this chapter• Poisson process• The Little’s Theorem• M/M/x Queueing systems• Burke’s Theorem and Jackson’s Theorem• M/G/1• Reservation systems and priority queue
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Weiqiang Sun
ARRIVAL MODEL AND THE LITTLE’S THEOREM
Weiqiang SunShanghai Jiao Tong University
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The arrival process
• The arrival process can normally be described – by the number of arrivals in a unit time– or can be described by inter-arrival time
• Poisson process– the most commonly used arrival model in telecom
network– Named after the French Mathematician Simeon-Denis
Poisson (1781 – 1840)
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Examples of Poisson process• The number of page request arriving at a web server (no
attack, please)
• The number of telephone calls arrives at an switch
• The number of photons hitting a photon detector, when lit by a laser
• The execution of trades on a stock exchange
• …
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Three ways to define a Poisson process
(1) In an infinitesimal time interval dt, there may occur only one arrival, and this happens with probability λdt
dt
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Three ways to define a Poisson process
(2)The number of arrivals N(t) in a finite interval of length t – Obeys Poisson distribution with parameter λt– The number of arrivals in non-overlapped intervals are
independent
!)())((
ntetNP
nt
T1Poisson(λT1)
T2Poisson(λT2)
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Three ways to define a Poisson process
(3) The interval times are independent and obey exponential distribution with rate λ
tetP 1)(
exp(λ)
• Proof of 23) within arrival 0(1)(1)( tPtPtP
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Properties of Poisson process
• Think about the coin-tossing process, though not Poisson, it is memory-less– X is the number of trials until the first “head”– Additional trials to get a “head” is independent of previous trials
• 1st of all: memory-less• The additional time to wait is independent
on when it starts– P(X>40|X>30) = P(X=10)
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Properties of Poisson process (cont.)
• Merging property– Let A1, A2, …, Ak be independent Poisson process
of rate λ1, λ2,…, λk, A= ∑Ai is also Poisson with rate λ= ∑ λi
λ1
λ2
λk
∑ λiλ1
λ2
λ1+λ2
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Properties of Poisson process (cont.)
• Selection property– Suppose a random selection is made from a Poisson process
(λ), each arrival is selected with probability p, independent of the others, the resulting process is a Poisson process with rate pλ
λ1
λ2
λk
∑ λiλ
pλ
Splitting property The above property also leads to random splitting
property, why and how?
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Properties of Poisson process (cont.)
• PASTA: Poisson Arrival See Time Average– One of the central tools in queueing theory– An arrival customer always see the system in average
state, in terms of number of customers in the system
Siλ
πi: the probability that an outside observer sees the system in state Si at a random instant
πi*: the probability that an arriving customer
sees the system in state Si just before arrivalIn general, πi ≠πi
*. But for Poisson arrivals, they equal.
To prove, show that:
( lim [ ( ) | an arrival occured just after ])n ta P N t n t
n na p
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Examples
• Problem in Text, 3.6
• Problem in Text, 3.10(d)
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Data networks and Queueing
R
R
R R
R
R
R
S
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Little’s Theorem
• Named after John Little, an MIT Sloan prof.Little J. D. C. “A proof of the Queueing Formula L= λw,” Operation Research, 9,
383-387 (1961)
A queueing system(N, T)
λ
• N= λT– λ: arrival rate of customers into the system– N: number of customers in the system– T: average amount of time a customer spends in the system
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Some observations of Little’s Theorem
• The result is very useful because of its generality– Nothing is assumed about the system
• Can be applied to the whole system, or• Any part of the system• Treat system as a blackbox
– The arrival process can be anything• Not necessarily Poisson process• But, it has to be stationanry
• And it can naturally explain why– On a rainy day, traffic moves more slower and the streets are more
crowded– A fast-food restaurant needs a smaller waiting room
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A simple justification of Little’s Theorem
• N(t) the number of customers in the system
• N: average number of customers in the system, can be calculated by dividing the above shaded area by t
• T: on average, each customer contributes T
• the average number of arrivals during t is λt
• Thus the area is λt×T, hence N = λT
t
N(t)
N
0
Graphical proof, see text 3.2.1
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Application examples
1. A transmission system
2. A complex system with multiple streams
queue transmitter transmission line
R
R
R R
R
R
R
λ1
λ2
λ3
λ3
λ2
λ1
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Single server queues
• M/M/1– Poisson arrivals, exponential service times
• M/G/1– Poisson arrivals, general service times
• M/D/1– Poisson arrivals, deterministic service time (fixed)
Sλ customers per second
μ customers served per second
queue
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Discrete-time Markov chains
• The memory-less property of both arrival process and service time – allow us to use the Markov chain theory to analyze M/M/1
queueing systems• Discrete-time Markov chains
p0,1 p1,2 p2,3 pk-1,k pk,k+1
S0 S1 S2 Sk
p1,0 p2,1 p3,2 pk,k-1 Pk+1,k
Si : states, i=0,1,… pi,j : probability of state transition from i to j
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M/M/1 systems and Markov chain• Define state k k customers in the system
• p(i, j): the probability that number of customers in the systems changes from i to j, within a very small time interval δ
• It can be shown that the probability of more than one arrival / departure is o(δ)
• Hence as δ0, we have:p(0, 0) = 1 - λδ p(j, j) = 1 – λδ – μδ, for j > 0
p(j-1, j) = λδp(j+1, j) = μδ p(i, j) = 0, for |i-j|>1
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Markov chain for M/M/1 systems
• In equilibrium, the transition from state n to n+1 is the same as the transition in the reverse direction
• λp(n) = μp(n+1) for all n– Local balance equations between two states (n, n+1)– p(n+1) = (λ/μ)p(n) = ρp(n), ρ=λ/μp(n) = ρnp(0)– By axiom of probability:
λδ λδ λδ λδ λδ
0 1 2 K
μδ μδ μδ μδ μδ
)1()(,1)0(
11
)0()0(1)(00
n
i
n
i
npp
ppip
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Some results of M/M/1
)/()1/()(0
n
nnpN
• Average number of customers in the system: N
• The average amount of time a customer spends in the system can be derived from the Little Theorem
)/(1/ NT
• The average amount of time a customer waiting in queue
11/1
TW
• Average number of customers in the queue
NWNQ
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The example of circuit switching vs. packet switching
Packet switching (multiplexing) Circuit switching (dedicated)
• T = ?
μ
λ/Mqueue
λ/M
λ/M…
λ/M
λ/M
λ/M… M
M
• T = ?
M
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m server systems: M/M/m
• Departure rate is proportional to the number of servers in use
μ
λ
….
queue
μ
μm servers μ customer per second, per server
λδ λδ λδ λδ λδ
0 1 2 m m+1
μδ 2μδ 3μδ mμδ mμδ
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M/M/m systems
• Local balance equations( 1) ( ),( 1) ( ),
p n n p n n mp n m p n n m
• Solve for p(0) and p(n) using ∑p(n)=1
(0)( ) / !, ( )
(0) / !,
n
m n
p m n n mp n
p m m n m
1
0
( ) ( )(0)! !(1 )
n mm
n
m mpn m
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The Erlang C formula and other results
• And the average number of customers in queue
• The probability of being queued(0)( )( )
!(1 )
m
Qn m
p mP p nm
0
( )1Q Q
n
N np n m P
the Erlang C formula
• With the Little’s Theorem, average time in queue and in systemQN
W
1 QN
T
• And of course, the average number of customers in system
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M/M/m example
• Text problem 3.7 M/M/2 systems with heterogeneous servers
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M/M/∞ (M/M/inf)• Infinitive number of servers Customers will no longer
experience queueing delaysλδ λδ λδ λδ λδ
0 1 2 m m+1
μδ 2μδ 3μδ mμδ (m+1)μδ
• Local balance equations( 1) ( )p n n p n
/(0)( ) ( / )( )! !
nnp ep n
n n
1/
1(0) 1 ( / ) / !n
np n e
/N / 1/T N
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M/M/m/m
• Same as M/M/m, but there is no queue– M/M/m no queue version
• Customers who arrive finding all server busy will leave (they are blocked)
• Blocking probability– The probability that a customer will come and find all server in service
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M/M/m/m systems• Up to m customers in the system
λδ λδ λδ λδ
0 1 2 m
μδ 2μδ 3μδ mμδ
• Local balance equations( 1) ( ), 1p n n p n n m
1
0(0) ( / ) / !m n
np n
(0)( / )( )
!
npp nn
0
( / ) / !( )( / ) / !
m
B m nn
mP p mn
The probability that a customer finds the
system busy, the Erlang B Formula
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The Erlang B formula• Define
– The systems load in Erlang– Formula sensitive to the ratio of λ and μ
• Can be used to dimensioning network capacity– Given tolerable PB and the load, find the
number of server needed
0
/ !/ !
m
B m nn
A mPA n
/A