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Unformatted text preview: ns, and L(t) is n Ln (t) − n Sn (t). 3.6. APPLICATIONS OF RENEWALREWARD THEORY 121 where Zi is the required service time of the ith customer in the queue at time t. Since the
service times are independent of the arrival times and of the earlier service times, Lq (t) is
independent of Z1 , Z2 , . . . , ZLq (t) , so, taking expected values,
E [U (t)] = E [Lq (t)] E [Z ] + E [R(t)] . (3.64) Figure 3.15 illustrates how to ﬁnd the timeaverage of R(t). Viewing R(t) as a reward
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✛ Z1
✲✛ Z2
✲✛ Z3✲ 0 ❅
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S1 Figure 3.15: Sample value of the residual life function of customers in service.
function, we can ﬁnd the accumulated reward up to time t as the sum of triangular areas.
R
First, consider R(τ )dτ from 0 to SN (t) , i.e., the accumulated reward up to the last renewal
epoch before t. SN (t) is not only a renewal epoch for the renewal process, but also an
arrival epoch for the arrival process; in particular, it is the A(SN (t) )th arrival epoch, and
the A(SN (t) ) − 1 earlier arrivals are the customers that have received service up to time
SN (t) . Thus,
Z A(SN (t) )−1 SN (t) R(τ ) dτ = τ =0 X
i=1 A(t) 2
XZ
Zi2
i
≤
.
2
2
i=1 R SN
We can similarly upper bound the term on the right above by τ =0(t)+1 R(τ ) dτ . We also
Rt
know (from going through virtually the same argument many times) that (1/t) τ =0 R(τ )dτ
will approach a limit with probability 1 as t → 1, and that the limit will be unchanged if
t is replaced with SN (t) or SN (t)+1 . Thus, taking ∏ as the arrival rate,
lim t→1 Rt
0 R(τ ) dτ
= lim
t→1
t £§
∏E Z 2
Zi2 A(t)
=
2A(t)
t
2 PA(t)
i=1 W.P.1. From Corollary 3.2, we can replace the time average above with the limiting ensembleaverage, so that
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∏E Z 2
lim E [R(t)] =
.
(3.65)
t→1
2
Finally, we can use Little’s theorem, in the limiting ensembleaverage form of (3.61), to
assert that limt→1 E [Lq (t)] = ∏W q . Substituting this plus (3.65) into (3.64), we get
£§
∏E Z 2
lim E [U (t)] = ∏E [Z ] W q +
.
(3.66)
t→1
2 122 CHAPTER 3. RENEWAL PROCESSES This shows that limt→1 E [U (t)] exists, so that E [U (t)] is asymptotically independent of
t. It is now important to distinguish between E [U (t)] and W q . The ﬁrst is the expected
unﬁnished work at time t, which is the queue delay that a customer would incur by arriving
at t; the second is the timeaverage expected queue delay. For Poisson arrivals, the probability of an arrival in (t, t + δ ] is independent of U (t)11 . Thus, in the limit t → 1, each
arrival faces an expected delay limt→1 E [U (t)], so limt→1 E [U (t)] must be equal to W q .
Substituting this into (3.66), we obtain the celebrated Pol laczekKhinchin formula,
£§
∏E Z 2
Wq =
.
(3.67)
2(1 − ∏E [Z ])
This queueing delay has some of the peculiar features of residual life, and in particular,
£§
if E Z 2 = 1, the limiting expected queueing delay is inﬁnite even though the expected
service time is less than the expected interarrival interval.
£§
In trying to visualize why the queueing delay is so large when E Z 2 is large, note that while
a particularly long service is taking place, numerous arrivals are coming into the system,
and all are being delayed by this single long service. In other words, the number of new
customers held up by a long service is proportional to the length of the service, and the
amount each of them are held up is also proportional to the length of the service. This
visualization is rather crude, but does serve to explain the second moment of Z in (3.67).
This phenomenon is sometimes called the “slow truck eﬀect” because of the pile up of cars
behind a slow truck on a single lane road.
For a G/G/1 queue, (3.66) is still valid, but arrival times are no longer independent of U (t),
so that typically E [U (t)] 6= W q . As an example, suppose that the service time is uniformly
distributed between 1 − ≤ and 1 + ≤ and that the interarrival interval is uniformly distributed
between 2 − ≤ and 2 + ≤. Assuming that ≤ < 1/2, the system has no queueing and W q = 0.
On the other hand, for small ≤, limt→1 E [U (t)] ∼ 1/4 (i.e., the server is busy half the time
with unﬁnished work ranging from 0 to 1). 3.7 Delayed renewal processes We have seen a certain awkwardness in our discussion of Little’s theorem and the M/G/1
delay result because an arrival was assumed, but not counted, at time 0; this was necessary
for the ﬁrst interarrival interval to be statistically identical to the others. In this section, we
correct that defect by allowing the epoch at which the ﬁrst renewal occurs to be arbitrarily
distributed. The resulting type of process is a generalization of the class of renewal processes
known as delayed renewal processes. The word delayed does not necessarily imply that the
ﬁrst renewal epoch is in any sense larger than the other interrenewal intervals. Rather,
it means that the usual renewal...
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 Spring '09
 R.Srikant

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