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1
Published in IEEE/ACM Transactions on Networking, February
1997.
A Measurement-based Admission Control Algorithmfor Integrated
Services Packet Networks (Extended Version)
Sugih Jamin, Peter B. Danzig, Scott J. Shenker, and Lixia
Zhang
Abstract— Many designs for integrated servicesnetworks offer a
bounded delay packet delivery ser-vice to support real-time
applications. To providebounded delay service, networks must use
admissioncontrol to regulate their load. Previous work on
ad-mission control mainly focused on algorithms thatcompute the
worst case theoretical queueing delayto guarantee an absolute delay
bound for all pack-ets. In this paper we describe a
measurement-basedadmission control algorithm for predictive
service,which allows occasional delay violations. We havetested our
algorithm through simulations on a widevariety of network
topologies and driven with vari-ous source models, including some
that exhibit long-range dependence, both in themselves and in
theiraggregation. Our simulation results suggest
thatmeasurement-based approach combined with the re-laxed service
commitment of predictive service en-ables us to achieve a high
level of network utilizationwhile still reliably meeting delay
bound.
I. BOUNDED DELAY SERVICES ANDPREDICTIVE SERVICE
There have been many proposals for supportingreal-time
applications in packet networks by pro-
Sugih Jamin was supported in part by the Uniforum Re-search
Award and by the Office of Naval Research Labora-tory under
contract N00173-94-P-1205. At USC, this researchis supported by
AFOSR award number F49620-93-1-0082, bythe NSF small-scale
infrastructure grant, award number CDA-9216321, and by equipment
loan from Sun Microsystems, Inc.At PARC, this research was
supported in part by the AdvancedResearch Projects Agency,
monitored by Fort Huachuca undercontract DABT63-94-C-0073. The
views expressed here donot reflect the position or policy of the
U.S. government.
Sugih Jamin and Peter B. Danzig are with the Com-puter Science
Department, University of Southern California,Los Angeles,
California 90089-0781 (email: [email protected],[email protected]).
Scott J. Shenker is with the Xerox Palo Alto Re-search Center,
Palo Alto, California 94304-1314
(email:[email protected]).
Lixia Zhang is with the Computer Science Department, Uni-versity
of California at Los Angeles, Los Angeles, California90095 (email:
[email protected]).
viding some form of bounded delay packet deliv-ery service. When
a flow requests real-time ser-vice, it must characterize its
traffic so that the net-work can make its admission control
decision. Typ-ically, sources are described by either peak and
av-erage rates [FV90] or a filter like a token bucket[OON88]; these
descriptions provide upper boundson the traffic that can be
generated by the source.The traditional real-time service provides
a hardor absolute bound on the delay of every packet;in [FV90],
[CSZ92], this service model is calledguaranteed service. Admission
control algorithmsfor guaranteed service use the a priori
characteriza-tions of sources to calculate the worst-case
behaviorof all the existing flows in addition to the incomingone.
Network utilization under this model is usu-ally acceptable when
flows are smooth; when flowsare bursty, however, guaranteed service
inevitablyresults in low utilization [ZF94].
Higher network utilization can be achieved byweakening the
reliability of the delay bound. Forinstance, the probabilistic
service described in[ZK94] does not provide for the worst-case
sce-nario, instead it guarantees a bound on the rate oflost/late
packets based on statistical characteriza-tion of traffic. In this
approach, each flow is allottedan effective bandwidth that is
larger than its averagerate but less than its peak rate. In most
cases theequivalent bandwidth is computed based on a sta-tistical
model [Hui88], [SS91] or on a fluid flow ap-proximation [GAN91],
[Kel91]) of traffic.1 If onecan precisely characterize traffic a
priori, this ap-proach will increase network utilization.
However,we think it will be quite difficult, if not impossi-ble, to
provide accurate and tight statistical modelsfor each individual
flow. For instance, the averagebit rate produced by a given codec
in a teleconfer-
�
We refer the interested readers to [Jam95] for a more
com-prehensive overview and bibliography of admission control
al-gorithms.
-
ence will depend on the participant’s body move-ments, which
can’t possibly be predicted in advancewith any degree of accuracy.
Therefore the a prioritraffic characterizations handed to admission
con-trol will inevitably be fairly loose upper bounds.
Many real-time applications, such as vat, nv,and vic, have
recently been developed for packet-switched networks. These
applications adapt toactual packet delays and are thus rather
tolerantof occasional delay bound violations; they do notneed an
absolutely reliable bound. For these toler-ant applications,
references [CSZ92], [SCZ93] pro-posed predictive service, which
offers a fairly, butnot absolutely, reliable bound on packet
deliverytimes. The ability to occasionally incur delay vi-olations
gives admission control a great deal moreflexibility, and is the
chief advantage of predictiveservice. The measurement based
admission con-trol approach advocated in [CSZ92], [JSZC92] usesthe
a priori source characterizations only for in-coming flows (and
those very recently admitted);it uses measurements to characterize
those flowsthat have been in place for a reasonable
duration.Therefore, network utilization does not suffer
sig-nificantly if the traffic descriptions are not tight.Because it
relies on measurements, and source be-havior is not static in
general, the measurementbased approach to admission control can
never pro-vide the completely reliable delay bounds neededfor
guaranteed, or even probabilistic, service; thus,measurement-based
approaches to admission con-trol can only be used in the context of
predic-tive service and other more relaxed service com-mitments.
Furthermore, when there are only afew flows present, the
unpredictability of individ-ual flow’s behavior dictates that these
measurementbased approaches must be very conservative—byusing some
worst-case calculation for example.Thus a measurement based
admission control algo-rithm can deliver significant gain in
utilization onlywhen there is a high degree of statistical
multiplex-ing.
In summary, predictive service differs in two im-portant ways
from traditional guaranteed service:(1) the service commitment is
somewhat less reli-able, (2) while sources are characterized by
tokenbucket filters at admission time, the behavior of ex-isting
flows is determined by measurement rather
than by a priori characterizations. It is importantto keep these
two differences distinct because whilethe first is commonplace, the
second, i.e. the useof measurement-based admission control, is
morenovel. On the reliability of service commitment,we note that
the definition of predictive service it-self does not specify an
acceptable level of delayviolations. This is for two reasons.
First, it is notparticularly meaningful to specify a failure rate
to aflow with a short duration [NK92]. Second, reliablyensuring
that the failure rate never exceeds a partic-ular level leads to
the same worst-case calculationsthat predictive service was
designed to avoid. In-stead, the CSZ approach [CSZ92] proposes that
thelevel of reliability be a contractual matter betweena network
provider and its customers—not some-thing specified on a per-flow
basis. We presume thatthese contracts would only specify the level
of vio-lations over some macroscopic time scale (e.g. daysor weeks)
rather than over a few hundred packettimes.2 In this paper we
describe a measurementbased admission control algorithm for
predictiveservice. We demonstrate affirmative answers to
thefollowing two questions. First, can one provide re-liable delay
bounds with a measurement-based ad-mission control algorithm?
Second, if one doesindeed achieve reliable delay bounds, does
offer-ing predictive service increase network utilization?Earlier
versions of this work have been published asreferences [JSZC92],
[JDSZ95]. Incidentally, thework reported in this paper has been
extended in[DKPS95] to support advance reservations. The au-thors
of [DKPS95] have also replicated some of ourresults on their
independently developed networksimulator.
The authors of [HLP93], [GKK95] use measure-ments to determine
admission control, but the ad-mission decisions are pre-computed
based on theassumption that all sources are exactly described byone
of a finite set of source models. This approachis clearly not
applicable to a large and heteroge-neous application base, and is
very different fromour approach to admission control that is
based
�
A network provider might promise to give its customerstheir
money back if the violations exceed some level over theduration of
their flow, no matter how short the flow; howeverwe contend that
the provider cannot realistically assure that ex-cessive violations
will never occur.
2
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on ongoing measurements. In references [SS91],[AS94] the authors
use measurement to learn theparameters of certain assumed traffic
distributions.The authors of [DJM97], [Flo96] use measure-ment of
existing traffic in their calculation of equiv-alent bandwidth,
providing load, but not delay,bound. In references [Hir91],
[CLG95], a neuralnetwork is used for dynamic bandwidth
allocation.In [LCH95], the authors use pre-computed low fre-quency
of flows to renegotiate bandwidth alloca-tion. Hardware
implementation of measurementmechanisms are studied in [C
�91], [WCKG94].
II. MEASUREMENT-BASED ADMISSIONCONTROL FOR ISPN
Our admission control algorithm consists of twologically
distinct aspects. The first aspect is theset of criteria
controlling whether to admit a newflow; these are based on an
approximate model oftraffic flows and use measured quantities as
inputs.The second aspect is the measurement process it-self, which
we will describe in Section III. In thissection we present the
analytical underpinnings ofour admission control criteria.
Sources requesting service must characterize theworst-case
behavior of their flow. In [CSZ92] thischaracterization is done
with a token bucket filter.A token bucket filter for a flow has two
parame-ters: its token generation rate, � , and the depth ofits
bucket,
�. Each token represents a single bit;
sending a packet consumes as many tokens as thereare bits in the
packet. Without loss of generality, inthis paper we assume packets
are of fixed size andthat each token is worth a packet; sending a
packetconsumes one token. A flow is said to conform toits token
bucket filter if no packet arrives when thetoken bucket is empty.
When the flow is idle ortransmitting at a lower rate, tokens are
accumulatedup to
�tokens. Thus flows that have been idle for a
sufficiently long period of time can dump a wholebucket full of
data back to back. Many non-constantbit rate sources do not
naturally conform to a to-ken bucket filter with token rate less
than their peakrates. It is conceivable that future real-time
applica-tions will have a module that can, over time, learn
asuitable � and
�to bound their traffic.
We have studied the behavior of our admissioncontrol algorithm
mostly under the CSZ scheduling
discipline [CSZ92]. Under the CSZ scheduling dis-cipline, a
switch can support multiple levels of pre-dictive service, with
per-level delay bounds that areorder of magnitude different from
each other. Theadmission control algorithm at each switch
enforcesthe queueing delay bound at that switch. We leavethe
satisfaction of end-to-end delay requirements tothe end systems. We
also assume the existence ofa reservation protocol which the end
systems coulduse to communicate their resource requirements tothe
network.
When admitting a new flow, not only must the ad-mission control
algorithm decide whether the flowcan get the service requested, but
it must also decideif admitting the flow will prevent the network
fromkeeping its prior commitments. Let us assume, forthe moment,
that admission control cannot allowany delay violations. Then, the
admission controlalgorithm must analyze the worst-case impact of
thenewly arriving flow on existing flows’ queueing de-lay. However,
with bursty sources, where the tokenbucket parameters are very
conservative estimatesof the average traffic, delays rarely
approach theseworst-case bounds. To achieve a fairly reliablebound
that is less conservative, we approximate themaximal delay of
predictive flows by replacing theworst-case parameters in the
analytical models withmeasured quantities. We call this
approximation theequivalent token bucket filter. This
approximationyields a series of expressions for the expected
max-imal delay that would result from the admission of anew flow.
In CSZ, switches serve guaranteed trafficwith the weighted fair
queueing scheduling disci-pline (WFQ) and serve different classes
of predictivetraffic with priority queueing. Hence, the
computa-tion of worst-case queueing delay is different
forguaranteed and predictive services. In this section,we will
first look at the worst-case delay computa-tion of predictive
service, then that of guaranteedservice. Following the worst-case
delay computa-tions, we present the equivalent token bucket
filter.We close this section by presenting details of theadmission
control algorithm based on the equiva-lent token bucket filter
approximations.
A. Worst-case Delay: Predictive Service
To compute the effect of a new flow on exist-ing predictive
traffic, we first need a model for
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the worst-case delay of priority queues. Cruz, in[Cru91],
derived a tight bound for the worst-casedelay, ���� , of priority
queue level � . Our deriva-tion follows Parekh’s [Par92], which is
a simpler,but looser, bound for � �� that assumes small
packetsizes, i.e. the transmission time of each packet
issufficiently small (as compared to other delays)and hence can be
ignored. This assumption ofsmall packet sizes further allows us to
ignore de-lays caused by the lack of preemption. Further, weassume
that the aggregate rate, aggregated over alltraffic classes, is
within the link capacity ( � � ���� ).
Theorem 1: Parekh [Par92]: The worst-caseclass j delay, with
FIFO discipline within the classand assuming infinite peak rates
for the sources, is
��� � � ������� ���� � ����������� � (1)
for each class j. Further, this delay is achieved fora strict
priority service discipline under which classj has the least
priority.3
The theorem says that the delay bound for class� is the one-time
delay burst that accrues if theaggregate bucket of all classes �
through � flowsare simultaneously dumped into the switch and
allclasses � through ����� sources continue to send attheir
reserved rates.
We now use Eq. 1 as the base equation to modelthe effect of
admitting a new flow on existing pre-dictive traffic. First we
approximate the traffic fromall flows belonging to a predictive
class � as a singleflow conforming to a !#" �%$ � �'& token
bucket filter. Aconservative value for " � would be the aggregate
re-served rate of all flows belonging to class � . Next,we
recognize that there are three instances when thecomputed
worst-case delay of a predictive class canchange: (1) when a flow
of the same class is ad-mitted, (2) when a flow of a higher
priority class isadmitted, and (3) when a guaranteed flow is
admit-ted. The switch priority scheduling isolates higherpriority (
(*) ) classes from a new flow of class ) ,so their worst-case delay
need not be re-evaluatedwhen admitting a flow of class ) . In the
remain-der of this section, we compute each of the three ef-fects
on predictive traffic individually. At the end of+
For a proof of Theorem 1, we refer interested readers to[Par92],
Theorem 2.4 or [Jam95], Theorem 1.
these computations, we will observe that admittinga higher
priority predictive flow “does more harm”to lower priority
predictive traffic than admitting ei-ther a guaranteed flow or a
predictive flow of thesame priority.
In the equations below, we denote newly com-puted delay bound by
� �-, . We denote the sumof guaranteed flows’ reservation by "/. .
The linkbandwidth available for serving predictive traffic isthe
nominal link bandwidth minus those reservedby guaranteed flows: �
�0"1. .1. Effect of new predictive flow on same prioritytraffic..
We can model the effect of admitting a newflow of predictive class
) by changing the class’stoken bucket parameters to !#"1243 �%52 $
� 263 � 52 & , where! �%52 $ � 52 & are the token bucket
parameters of the newflow:
879 � 9 ���:���;� �
���=@� � 9 ������� < ��A
� 9 A ��B9������ � 9 ������� < �
9CA ��B9���=@� � 9 ������� < �ED (2)
We see that the delay of class ) grows by a termthat is
proportional to flow ’s bucket size.2. Effect of predictive flow on
lower priority traf-fic.. We compute the new delay bound for class�
, where � is greater than the requested class, ) , di-rectly from
Eq. 1, adding in the bucket depth
� 52 andreserved rate � 52 of flow . 87� � 9 �
������F� � A � 9 A ��B9 A � � ��� 9HG �I� ����=@� � 9 �������
< � �=< 9 � �JB9 � � ���
���� 9HG � < �
� ���=@� � ���
��:�K� < ��L�=@� � ��������� < � � �JB9 A��B9���=< >
� � ���
������ < � � �JB9NM O�P@QSRUTVM(3)where W is the number of
predictive classes. The
first term reflects a squeezing of the pipe, in thatthe
additional bandwidth required by the new flowreduces the bandwidth
available for lower priorityflows. The second term is similar to
the delay calcu-lated above, and reflects the effect of the new
flow’sburstiness.
3. Effect of guaranteed flow on predictive traffic..Again, we
compute the new delay bound � �-, for allpredictive classes
directly from Eq. 1, adding in the
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reserved rate, �45. , of flow . 87� � � �:�K��� ��L�=< > �
� ���
������ < � � �JB>
�� ���=�� � ���
������ < ��L�=@� � �����:�K� < � � �?B> M �FR@QSRUT
D(4)
Notice how the new guaranteed flow simplysqueezes the pipe,
reducing the available bandwidthfor predictive flows; new
guaranteed flows do notcontribute any delay due to their buckets
becausethe WFQ scheduling algorithm smooths out theirbursts. Also
observe that the first term of Eq. 3 isequivalent to Eq. 4: the
impact of a new guaranteedflow is like adding a zero-size bucket,
higher prior-ity, predictive flow.
Contrasting these three equations, we see that theexperienced
delay of lower priority predictive traf-fic increases more when a
higher priority predictiveflow is admitted than when a guaranteed
flow or asame-priority predictive flow is admitted. The
WFQscheduler isolates predictive flows from attempts byguaranteed
flows to dump their buckets into the net-work as bursts. In
contrast, lower priority predictivetraffic sees both the rates and
buckets of higher pri-ority predictive flows. A higher priority
predictiveflow not only squeezes the pipe available to
lowerpriority traffic, but also preempts it.
B. Worst-case Delay: Guaranteed Service
In reference [Par92], the author proves that in anetwork with
arbitrary topology, the WFQ schedul-ing discipline provides
guaranteed delay boundsthat depend only on flows’ reserved rates
and bucketdepths. Under WFQ, each guaranteed flow is iso-lated from
the others. This isolation means that,as long as the total reserved
rate of guaranteedflows is below the link bandwidth, new
guaranteedflows cannot cause existing ones to miss their
delaybounds. Hence, when accepting a new guaranteedflow, our
admission control algorithm only needsto assure that (1) the new
flow will not cause pre-dictive flows to miss their delay bound
(see Eq. 4above), and that (2) it will not over-subscribe thelink:
"4. 3 � 5. ��� � , where � is the link bandwidthand � is the
utilization target (see Section III-B for adiscussion on
utilization target). In addition to pro-tecting guaranteed flows
from each other, WFQ also
isolates (protects) guaranteed flows from all predic-tive
traffic.
C. Equivalent Token Bucket Filter
The equations above describe the aggregate traf-fic of each
predictive class with a single tokenbucket filter. How do we
determine a class’s to-ken bucket parameters? A completely
conservativeapproach would be to make them the sum of theparameters
of all the constituent flows; when datasources are bursty and flows
declare conservativeparameters that cover their worst-case bursts,
usingthe sum of declared parameters will result in lowlink
utilization. Our algorithm is approximate andoptimistic: we take
advantage of statistical multi-plexing by using measured values,
instead of pro-viding for the worst possible case, to gain
higherutilization, risking that some packets may occasion-ally miss
their delay bounds. In essence, we de-scribe existing aggregate
traffic of each predictiveclass with an equivalent token bucket
filter with pa-rameters determined from traffic measurement.
The equations above can be equally described interms of current
delays and usage rates as in bucketdepths and usage rates. Since it
is easier to mea-sure delays than to measure bucket depths, we
dothe former. Thus, the measured values for a predic-tive class �
are the aggregate bandwidth utilizationof the class, �" � , and the
experienced packet queue-ing delay for that class, �� � . For
guaranteed service,we count the sum of all reserved rates, " . ,
and wemeasure the actual bandwidth utilization, �" . , of
allguaranteed flows. Our approximation is based onsubstituting, in
the above equations, the measuredrates �" � and �"%. for the
reserved rates, and substi-tuting the measured delays �� �4$
���*������� W for themaximal delays. We now use the previous
compu-tations and these measured values to formulate anadmission
control algorithm.
D. The Admission Control Algorithm
New Predictive Flow.. If an incoming flow re-quests service at
predictive class ) , the admissioncontrol algorithm:
1. Denies the request if the sum of the flow’s re-quested rate,
� 52 , and current usage would exceed
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the targeted link utilization level:
� ��� �JB9 A � < > A�� �:��� � < � M (5)
2. Denies the request if admitting the new flowcould violate the
delay bound, � 2 , of the same pri-ority level:
9 � � 9 A ��B9��� � @� � 9 ������� �< � M (6)
or could cause violation of lower priority classes’delay bound,
� � : � � � � �L� � @� � ���
��:�K� � < ��L� � < > � � ��������� � < � � �?B9
A��B9��� � @� � ���
������ � < � � �JB9NM O�P@QSRUT D(7)New Guaranteed Flow.. If
an incoming flow requests guaranteed service, the admission
controlalgorithm:1. Denies the request if either the bandwidth
checkin Eq. 5 fails or if the reserved bandwidth of allguaranteed
flows exceeds the targeted link utiliza-tion level: � ��� �JB> A
< > D (8)2. Denies the request if the delay bounds of
pre-dictive classes can be violated when the bandwidthavailable for
predictive service is decreased by thenew request:
� � � � ��� � @� � �����:�K� � < ��L� � < > � � ���
������ � < � � �JB> M �FR�QSRUT D(9)
If the request satisfies all of these inequalities, thenew flow
is admitted.
III. A SIMPLE TIME-WINDOW MEASUREMENTMECHANISM
The formulae described in the previous sectionrely on the
measured values �� � , �"%. , and �" � as in-puts. We describe in
this section the time-windowmeasurement mechanism we use to measure
thesequantities. While we believe our admission controlequations
have some fundamental principles under-lying them, we make no such
claim for the mea-surement process. Our measurement process usesthe
constants � $�� , and ; discussion of their rolesas performance
tuning knobs follows our descrip-tion of the measurement
process.
A. Measurement Process
We take two measurements: experienced delayand utilization. To
estimate delays, we measure thequeueing delay � of every packet. To
estimate uti-lization, we sample the usage rate of guaranteed
ser-vice, �"��. , and of each predictive class � , �"�� , overa
sampling period of length � packet transmissionunits. Following we
describe how these measure-ments are used to compute the estimated
maximaldelay �� � and the estimated utilization �"%. and �" �
.Measuring delay.. The measurement variable �� �tracks the
estimated maximum queueing delay forclass � . We use a measurement
window of packettransmission units as our basic measurement
block.The value of �� � is updated on three occasions. Atthe end of
the measurement block, we update �� �to reflect the maximal packet
delay seen in the pre-vious block. Whenever an individual delay
mea-surement exceeds this estimated maximum queue-ing delay, we
know our estimate is wrong and im-mediately update �� � to be �
times this sampled de-lay. The parameter � allows us to be more
conser-vative by increasing �� � to a value higher than theactual
sampled delay. Finally, we update �� � when-ever a new flow is
admitted, to the value of pro-jected delay from our admission
control equations.Algebraically, the updating of �� � is as
follows:
���� �������� �������
������� ���� M of past T measurement window,� �� M if �� � � �
,Right sideof Eq. 6, 7,or 9,
when adding a new flow,depending on the serviceand class
requested by theflow.
(10)
Measuring rate.. The measurement variables �" .and �" � track
the highest sampled aggregate rate ofguaranteed flows and each
predictive class respec-tively (heretofore, we will use “ �" ” as a
shorthandfor “ �"%. and/or �" � ,” and “ �" � ” for “ �" �. and/or
�" �� .”)The value of �" is updated on three occasions. Atthe end
of the measurement block, we update �" toreflect the maximal
sampled utilization seen in theprevious block. Whenever an
individual utilizationmeasurement exceeds �" , we immediately
update �"with the new sampled value. Finally, we update �"whenever
a new flow is admitted. Algebraically, the
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updating of �" is as follows:
� < � ����� ����������� �
-
not admit anymore flow until the end of a period.During its
lifetime, � , a flow will see approximately� � � � � number of
flows admitted every period.Thus at the end of its average
lifetime, � , an averageflow would have seen approximately � � ���
� � number of flows. If the average rate of an averageflow is � � ,
ideally we want � � � � , a link’s stable uti-lization level, to be
near � . However, flows alsodepart from the network. The expected
number ofadmitted flow departures during the period de-pends on the
number of flows and their duration. Ifthis number of departures is
significant, a flow willsee a much smaller number of flows during
its life-time, i.e. the stable � � � � becomes much smallerthan � .
For the same average reservation rate, � ,and a given , the size of
the stable � is determinedby the average flow duration, � . A
shorter averageflow duration means more departure per . In thelong
run, we aim for � � � ��� � , or equivalently,� � �� � � � � . If
all flows use exactly what they re-served, we have � � � � ,
meaning that we shouldnot try to give away the flows’ reservations.
Wepresent further illustrative simulation results on theimportance
of the � � ratio in Section IV-E. Notethat when is infinite, we
only use our computedvalues, which are conservative bounds, and
ignorethe measurements entirely. That is, we will neversuffer any
delay violations at a given hop if we usean infinite value for .
Thus, the parameter al-ways provides us with a region of
reliability.
IV. SIMULATIONS
Admission control algorithms for guaranteed ser-vice can be
verified by formal proof. Measurement-based admission control
algorithms can only be ver-ified through experiments on either real
networks ora simulator. We have tested our algorithm
throughsimulations on a wide variety of network topolo-gies and
driven with various source models; we de-scribe a few of these
simulations in this paper. Ineach case, we were able to achieve a
reasonable de-gree of utilization (when compared to
guaranteedservice) and a low delay bound violation rate (wetry to
be very conservative here and always aim forno delay bound
violation over the course of all oursimulations). Before we present
the results fromour simulations, we first present the topologies
andsource models used in these simulations.
HostA
Switch2Switch1
HostB
L1 L2L3
(a) One-Link
(b) Two-Link (c) Four-Link
HostA
Switch2Switch1
HostB
L1 L2L4
L5
Switch3L3
HostA
Switch1 Switch2
HostB
L1 L2
L7
L6
Switch3L3
Switch4
HostD
L9
L4
L8
HostE
Switch5
L5
HostC
HostC
Fig. 1. The ONE-LINK, TWO-LINK and FOUR-LINKtopologies
A. Simulated Topologies
For this paper, we ran our simulations on fourtopologies: the
ONE-LINK, TWO-LINK, FOUR-LINK, and TBONE topologies depicted in
Fig-ures 1(a), (b), (c), and 2 respectively. In thefirst three
topologies, each host is connected to aswitch by an infinite
bandwidth link. The con-nection between switches in these three
topolo-gies are all 10 Mbps links, with infinite buffers.In the
ONE-LINK topology, traffic flows fromHostA to HostB. In the
TWO-LINK case, traf-fic flows between three host pairs (in
source–destination order): HostA–HostB, HostB–HostC,HostA–HostC.
Flows are assigned to one of thesethree host pairs with uniform
probability. In theFOUR-LINK topologies, traffic flows between
sixhost pairs: HostA–HostC, HostB–HostD, HostC–HostE, HostA–HostD,
HostB–HostE, HostD–HostE;again, flows are distributed among the six
host pairswith uniform probability. In Figure 1, these hostpairs
and the paths their packets traverse are indi-cated by the directed
curve lines.
The TBONE topology consists of 10, 45, and 100Mbps links as
depicted in Figure 2(a). Traffic flowsbetween 45 host-pairs
following four major “cur-rents” as shown in Figure 2(b): the
numbers 1, 2, 3,4 next to each directed edge in the figure denote
the“current” present on that edge. The 45 host-pairsare listed in
Table I. Flows between these host-pairsride on only one current,
for example flows fromhost H1 to H26 rides on current 4. In Figure
2(a),a checkered box on a switch indicates that we haveinstrumented
the switch to study traffic flowing outof that switch onto the link
adjacent to the check-ered box.
8
-
TABLE IFORTY-FIVE HOST PAIRS ON TBONE
Source Destination(s) Source Destination(s)
H1 H5, H7, H11, H14 H23 and H25H12, H14, and H26 H15 H11 and
H17
H2 H10 and H25 H16 H5 and H9H3 H4 and H19 H17 H12H4 H18 H18 H5,
H6, and H11H5 H14 and H25 H19 H5H6 H18 H20 H5H7 H17 H21 H9H8 H4,
H5, H26 H22 H6H9 H3 and H19 H24 H12 and H17H10 H3 and H18 H25 H6
and H14H12 H4 H26 H9 and H14H13 H17 H27 H4
S13 S12 S8
S9
S10
S11
S4
S3S2
S5S6
S1S7H1
H2
H3
H4
H5H6H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
H23
H24 H25
H26
H27
L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L17
L18L19
L24
L26
L27
L36
L28
L29
L30
L31
L32
L33
L34
L35
L37 L38L39
L40
L12L13
L14L15
L16
L20L21
L22L23
L25
= Instrumentation
= 10 Mbps= 45 Mbps= 100 Mbps
(a) TBone topology
S13 S12 S8
S9
S10
S11
S4
S3S2
S5S6
S1S7H1
H2
H3 H4
H5H6H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
H23
H24 H25
H26
H27
1
1 1
1
1 1
11
1
1
1
11
1
1
1
1
1
11
1
1
1
1
11
1
2 2
2
2
2
2
2
22
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3 3
3
3
3 332
44
4
4
4
4
4
4
4
4
4
4
4
4
44
4
44
3
4
(b) Four traffic "currents" on TBone
Fig. 2. The TBONE topology
B. Source Models
We currently use three kinds of source model inour simulations.
All of them are ON/OFF processes.They differ in the distribution of
their ON time andcall holding time (CHT, which we will also
call“flow duration” or “flow lifetime”). One of these isthe
two-state Markov process used widely in the lit-erature. Recent
studies ([LTWW94], [DMRW94],[PF94], [KM94], [GW94], [BSTW95]) have
shownthat network traffic often exhibits long-range de-pendence
(LRD), with the implications that con-gested periods can be quite
long and a slight in-crease in number of active connections can
result
in large increase in packet loss rate [PF94]. Refer-ence [PF94]
further called attention to the possiblydamaging effect long-range
dependent traffic mighthave on measurement-based admission control
al-gorithms. To investigate this and other LRD relatedquestions, we
augmented our simulation study withtwo LRD source models.
EXP Model.. Our first model is an ON/OFFmodel with exponentially
distributed ON and OFFtimes. During each ON period, an
exponentiallydistributed random number of packets, with
aver-age
, are generated at fixed rate � packet/sec. Let�
milliseconds be the average of the exponentiallydistributed OFF
times, then the average packet gen-eration rate � is given by � � �
� ��� 3 � � � . TheEXP1 model described in the next section is a
modelfor packetized voice encoded using ADPCM at 32Kbps.
LRD: Pareto-ON/OFF.. Our next model is anON/OFF process with
Pareto distributed ON and OFFtimes (for ease of reference, we call
this the Pareto-ON/OFF model). During each ON period, a
Paretodistributed number of packets, with mean
and
Pareto shape parameter � , are generated at peakrate �
packet/sec. The OFF times are also Paretodistributed with mean
�milliseconds and shape pa-
rameter � . Pareto shape parameter less than 1 givesdata with
infinite mean; shape parameter less than2 results in data with
infinite variance. The Paretolocation parameter is ������ � !�
������� ��� & �
������� .Each Pareto-ON/OFF source by itself does not gen-erate
LRD series. However, the aggregation of themdoes [WTSW95].
LRD: Fractional ARIMA.. We use each num-ber generated by the
fractional autoregressive inte-grated moving average (fARIMA)
process ([HR89])as the number of fixed-size packets to be sent
backto back in each ON period. Interarrivals of ON peri-ods are of
fixed length. For practical programmingreasons, we generate a
series of 15,000 fARIMAdata points at the beginning of each
simulation.Each fARIMA source then picks an uniformly dis-tributed
number between 1 and 15,000 to be usedas its index into that
series. On reaching the end ofthe series, the source wraps around
to the begin-ning. This method is similar to the one used bythe
authors of [GW94] to simulate data from sev-
9
-
Tokensgeneratedat rate r
b tokens
Token BucketFilter
User Process
N packeton time
1/pM packetoff time
Host maxtransmissionrate C
C
Host
Network
Packets transmitted
at rate G, r
-
TABLE IISIX INSTANTIATIONS OF THE THREE SOURCE MODELS
Model’s Parameters Token Bucket Parameters Bound (ms)
Model Name � pkt/ � � ������
tkn/�
cut maxsec msec pkts sec tkns rate qlen
�EXP1 64 325 20 2 64 1 0 0 16 16EXP2 1024 90 10 10 320 50 2.1e-3
17 160 160EXP3 � 684 9 � 512 80 9.4e-5 1 160 160�POO1 64 2925 20
1.2 64 1 0 0 16 16POO2 256 360 10 1.9 240 60 4.5e-5 220 256 160
fARIMA( 0.75 � ,0.15, -)
� 125 8 13 1024 100 1.1e-2 34 100 160
time ( � ) of the Pareto-ON/OFF sources are selectedfollowing
the observations in [WTSW95]. Accord-ing to the same reference, the
shape parameter ofthe Pareto distributed OFF time ( � ) stays
mostlybelow 1.5; in this paper we use � of 1.1 for allPOO sources.
For the POO1 model, we use a to-ken bucket rate equals to the
source’s peak rate suchthat the token bucket filter does not
reshape the traf-fic. For the POO2 model, some of the
generatedpackets were queued; this means during some of thesource’s
alleged “OFF” times, it may actually still bedraining its data
queue onto the network. Thus forthe POO2 model, the traffic seen on
the wire maynot be Pareto-ON/OFF.
When a flow with token bucket parameters ! � $ � &requests
guaranteed service, the maximal queue-ing delay (ignoring terms
proportional to a singlepacket time) is given by
� �� [Par92]. Column 10
of the table, labeled � � , lists the guaranteed de-lay bound
for each source given its assigned tokenbucket filter. Column 11,
labeled � � , lists the pre-dictive delay bound assigned to each
source. Wesimulate only two classes of predictive service.
Apredictive bound of 16 msecs. means first class pre-dictive
service, 160 msecs. second class. We havechosen the token bucket
parameters so that, in mostcases, the delay bounds given to a flow
by predic-tive and guaranteed services are the same. This
fa-cilitates comparison between the utilization levelsachieved with
predictive and guaranteed services.In the few cases where the
delays are not the same,
such as in the POO2 and fARIMA cases, the utiliza-tion
comparison is less meaningful. In the POO2case, for example, the
predictive delay bound issmaller than the guaranteed bound, so the
utiliza-tion gain we find here understates the true gain.
For the fARIMA source, we use an autoregres-sive process of
order 1 (with weight 0.75) and de-gree of integration 0.15
(resulting in a generatedseries with Hurst parameter 0.65). The
first orderautoregressive process with weight 0.75 means ourfARIMA
traffic also has strong short-range depen-dence, while maintaining
stationarity ([BJ76], p.53). The interarrival time between ON
periods is1/8th of a second. The Gaussian innovation fed tothe
fARIMA process has a mean of 8 packets withstandard deviation
13.
Except for simulations on the TBONE topology,flow interarrival
times are exponentially distributedwith an average of 400
milliseconds. Because ofsystem memory limitation, we set the
average flowinterarrivals of simulations on the TBONE topol-ogy to
5 seconds. The average holding time of allEXP sources is 300
seconds. The POO and fARIMAsources have lognormal distributed
holding timeswith median 300 seconds and shape parameter 2.5.
We ran most of our simulations for 3000 secondssimulated time.
The data presented are obtainedfrom the later half of each
simulation. By visualinspection, we determined that 1500 simulated
sec-onds is sufficient time for the simulation to warmup. However,
simulations with long-range depen-
11
-
dent sources requesting predictive service requiresa longer
warmup period. We ran all simulation in-volving such sources for
5.5 hours simulation time,with reported data taken from the later
10000 sec-onds.
We divide the remainder of this section up intothree
subsections. First, we show that predictiveservice indeed yields
higher level of link utilizationthan guaranteed service does. We
provide support-ing evidence from results of simulations with
bothhomogeneous and heterogeneous traffic sources, onboth
single-hop and multi-hop networks. Depend-ing on traffic
burstiness, the utilization gain rangesfrom twice to order of
magnitude. This is the basicconclusion of this paper.
Second, we provide some simulation results to il-lustrate the
effect of the � � ratio on network per-formance, as discussed in
Section III-B. We showthat a larger � � ratio yields higher
utilization butless reliable delay bound, while a smaller one
pro-vides more stable delay estimate at lower utiliza-tion. We also
present a few sample path snapshotsillustrating the effect of .
Finally, we close this section with a discussionof some general
allocation properties of admissioncontrol algorithms when flows are
not equivalent;we believe these properties to be inherent in all
ad-mission control algorithms whose only admissioncriterion is to
avoid service commitment violations.
D. On the Viability of Predictive Service
We considered six different source models, fourdifferent network
topologies (one single hop andthree multi-hop), and several
different traffic mixes.In particular, some of our traffic loads
consisted ofidentical source models requesting the same ser-vice
(the homogeneous case), and others had ei-ther different source
models and/or different levelsof service (the heterogeneous case).
The organiza-tion of our presentation in this section is: (1)
ho-mogeneous sources, single hop, (2) homogeneoussources,
multi-hop, (3) heterogeneous sources, sin-gle hop, and (4)
heterogeneous sources, multi-hop.
Homogeneous Sources: The Single-hop Case..By homogeneous sources
we mean sources that notonly employ just one kind of traffic model,
but alsoask for only one kind of service. For this and
TABLE IIISINGLE-HOP HOMOGENEOUS SOURCES
SIMULATION RESULTS
Model Guaranteed Predictive
Name %Util #Actv %Util #Actv � � ��� � � �EXP1 46 144 80 250 3
60EXP2 28 28 76 75 42 300EXP3 2 18 62 466 33 600POO1 7 144 74 1637
5 60POO2 3 38 64 951 8 60fARIMA 55 9 81 13 72 60
all subsequent single-hop simulations, we use thetopology
depicted in Figure 1(a). For each source,we ran two kinds of
simulation. The first has allsources requesting guaranteed service.
The secondhas all sources requesting predictive service. Theresults
of the simulations are shown in Table III.The column labeled
“%Util” contains the link uti-lization of the bottleneck link, L3.
The “#Actv” col-umn contains a snapshot of the average number
ofactive flows concurrently running on that bottlenecklink. The “ �
��� ” column contains the maximum ex-perienced delay of predictive
class � packets. The“ � � ” column lists the ratio of average flow
dura-tion to measurement window used with each sourcemodel.
We repeated the predictive service simulationsnine times, each
time with a different random seed,to obtain confidence intervals.
We found the con-fidence interval for the all the numbers to be
verytight. For example, the utilization level of POO1sources under
predictive service has a 99% con-fidence interval of (74.01,
74.19); the 99% confi-dence interval for the maximum experience
delay is(4.41, 4.84) (the number reported in the table is
theceiling of the observed maximum).
As mentioned in Section IV-B, we consider theperformance of our
admission control algorithm“good” if there is no delay bound
violation dur-ing a simulation run. Even with this very
restric-tive requirement, one can see from Table III thatpredictive
service consistently allows the networkto achieve higher level of
utilization than guaran-teed service does. The utilization gain is
not large
12
-
when sources are smooth. For instance, the sourcemodel EXP1 has
a peak rate that is only twice itsaverage rate. Consequently, the
data only shows anincrease in utilization from 46% to 80%. (One
canargue that the theoretical upper bound in the uti-lization
increase is the peak to average ratio.) Incontrast, bursty sources
allow predictive service toachieve several orders of magnitude
higher utiliza-tion compared to that achievable under
guaranteedservice. Source model EXP3, for example, is a verybursty
source; it has an infinite peak rate (i.e. sendsout packets back to
back) and has a token bucketof size 80. The EXP3 flows request
reservations of512 Kbps, corresponding to the token bucket rateat
the sources. Under guaranteed service, only 18flows can be admitted
to the 10 Mbps bottlenecklink (with 90% utilization target). The
actual linkutilization is only 2%.4 Under predictive service,466
flows are served on the average, resulting in ac-tual link
utilization of 62%.
In this homogeneous scenario with only one classof predictive
service and constantly oversubscribedlink, our measurement-based
admission control al-gorithm easily adapts to LRD traffic between
thecoming and going of flows. The utilization in-creased from 7% to
74% and from 3% to 64%for the POO1 and POO2 sources respectively.
Theutilization gain for the fARIMA sources was moremodest, from 55%
to 81%. This is most proba-bly because the source’s maximum ON time
is atmost twice its average (an artifact of the shiftingwe do, as
discussed in Section IV-B, to obtain non-negative values from the
fARIMA generated series).In all cases, we were able to achieve high
levels ofutilization without incurring delay violations. Tofurther
test the effect of long OFF times on ourmeasurement-based
algorithm, we simulated POO1sources with infinite duration. With
utilization tar-get of 90% link capacity, we did see a rather
highpercentage of packets missing their delay bound.Lowering the
utilization target to 70%, however,provided us enough room to
accommodate trafficbursts. Thus for these scenarios, we see no
reason toconclude that LRD traffic poses special challenges
�Parameter-based admission control algorithms may not
need to set a utilization target and thus can achieve a
some-what higher utilization; for the scenario simulated here,
twomore guaranteed flows could have been admitted.
TABLE IVMULTI-HOP HOMOGENEOUS SOURCES LINK
UTILIZATION
Link Model Guaranteed PredictiveTopology Name Name %Util %Util
�������
EXP1 45 67 2L4 EXP3 2 44 20
POO2 3 59 7TWO-LINK
EXP1 46 78 3L5 EXP3 2 58 30
POO2 3 70 17
EXP2 17 42 6L6 POO1 4 31 1
fARIMA 38 54 36EXP2 28 71 31
L7 POO1 7 66 2fARIMA 55 77 40
FOUR-LINKEXP2 28 72 24
L8 POO1 8 75 7fARIMA 53 74 29EXP2 28 71 31
L9 POO1 8 59 2fARIMA 53 80 44
to our measurement-based approach.
Homogeneous Sources: The Multi-hop Case..Next we ran simulations
on multi-hop topologiesdepicted in Figures 1(b) and (c). The top
half ofTable IV shows results from simulations on theTWO-LINK
topology. The utilization numbers arethose of the two links
connecting the switches inthe topology. The source models employed
here arethe EXP1, EXP3, and POO2 models, one per simu-lation. The
bottom half of Table IV shows the re-sults from simulating source
models EXP2, POO1,and fARIMA on the FOUR-LINK topology. For
eachsource model, we again ran one simulation whereall sources
request guaranteed service, and anotherone where all sources
request one class of predictiveservice.
The most important result to note is that, onceagain, predictive
service yielded reasonable lev-els of utilization without incurring
any delay vio-lations. The utilization levels, and the
utilizationgains compared to guaranteed service, are
roughlycomparable to those achieved in the single hop case.
Heterogeneous Sources: The Single-hop Case..We now look at
simulations with heterogeneoussources. For each of the simulation,
we used twoof our six source model instantiations. Each sourcewas
given the same token bucket as listed in Ta-ble II and, when
requesting predictive service, re-quests the same delay bound as
listed in the saidtable. We ran three kinds of simulation with
hetero-
13
-
TABLE VSINGLE-HOP, SINGLE SOURCE MODEL, MULTIPLE
PREDICTIVE SERVICES LINK UTILIZATION
Model PP GP GPP
EXP1 77 77 –EXP2 71 70 –EXP3 31 31 –POO1 70 69 69POO2 60 57
–fARIMA 79 79 78
geneous sources: (1) single source model request-ing multiple
levels of predictive service, (2) multi-ple source models
requesting a single class of pre-dictive service, and (3) multiple
source models re-questing multiple levels of predictive service. In
allcases, we compared the achieved utilization withthose achieved
under guaranteed service. For thefirst and third cases, we also
experimented withsources that request both guaranteed and
predic-tive services. When multiple source and/or servicemodels
were involved, each model was given anequal probability of being
assigned to the next newflow. In all these simulations, the
experienced de-lays were all within their respective bounds.
Table V shows the utilization achieved whenflows with the same
source model requested: twoclasses of predictive service (PP),
guaranteed andone predictive class (GP), and guaranteed and
twopredictive classes (GPP). In the GP case, flows re-quest the
predictive class “assigned” to the sourcemodel under study (see
Table II). In the othercases, both predictive classes, of bounds 16
and 160msecs. were requested. Compare the numbers ineach column of
Table V with those in the “%Util”column of Table III under
guaranteed service. Thepresence of predictive traffic invariably
increasesnetwork utilization.
Next we look at the simulation results of multiplesource models
requesting a single service model.Table VI shows the utilization
achieved for selectedpairings of the models. The column headings
namethe source model pairs. The first row shows the uti-lization
achieved with guaranteed service, the sec-ond predictive service.
We let the numbers speakfor themselves.
Finally in Table VII we show utilization num-
TABLE VISINGLE-HOP, MULTIPLE SOURCE MODELS, SINGLE
SERVICE LINK UTILIZATION
EXP1– EXP2– EXP2– EXP2– EXP3– POO2–Service POO1 EXP3 POO2 fARIMA
fARIMA fARIMAGuaranteed 15 21 5 38 18 32Predictive 75 70 63 79 81
69
TABLE VIISINGLE-HOP, MULTIPLE SOURCE MODELS,
MULTIPLE PREDICTIVE SERVICES LINKUTILIZATION
EXP1– EXP1– EXP1– EXP2– EXP3– POO1–Service EXP2 fARIMA POO2 POO1
POO1 fARIMAGuaranteed 43 50 29 10 7 23Guar./Pred. 73 74 65 61 51
65Predictive 75 78 65 62 60 65
bers for flows with multiple source models request-ing multiple
service models. The first row showsthe utilization achieved when
all flows asked onlyfor guaranteed service. The second row shows
theutilization when half of the flows requests guar-anteed service
and the other half requests the pre-dictive service suitable for
its characteristics (seeTable II). And the last row shows the
utilizationachieved when each source requests a predictiveservice
suitable for its characteristics.
Heterogeneous Sources: The Multi-hop Case..We next ran
simulations with all six source mod-els on all our topologies. In
Table VIII we showthe utilization level of the bottleneck links of
thedifferent topologies. Again, contrast the utilizationachieved
under guaranteed service alone with thoseunder both guaranteed and
predictive services. Theobserved low predictive service utilization
on linkL6 is not due to any constraint enforced by its ownadmission
decisions, but rather is due to lack oftraffic flows caused by
rejection of multi-hop flowsby later hops, as we will explain in
Section IV-F.Utilization gains on the TBONE topology are notso
pronounced as on the other topologies. Thisis partly because we are
limited by our simula-tion resources and cannot drive the
simulations with
14
-
TABLE VIIISINGLE- AND MULTI-HOP, ALL SOURCE MODELS,
ALL SERVICES LINK UTILIZATION
Topology Link Guaranteed Guaranteed and PredictiveName Name
%Util %Util ������� ����� �ONE-LINK L3 24 66 3. 45.
L4 15 72 2. 54.TWO-LINK L5 21 72 2. 41.
L6 19 47 1. 36.L7 24 70 2. 46.
FOUR-LINK L8 20 72 2. 49.L9 18 75 1. 53.
L2 9 14 0.02 0.15L10 17 31 0.15 5.35L11 27 32 0.37 21.9
TBONE L12 22 23 0.1 5.84L20 8 21 0.22 16.6L30 32 52 0.49
34.7
higher offered load. Recall that flow interarrivals
onsimulations using the TBONE topology have an av-erage of 5
seconds, which is an order of magnitudelarger than the 400
milliseconds used on the othertopologies.
Our results so far indicate that a measurement-based admission
control algorithm can provide rea-sonable reliability at
significant utilization gains.These conclusions appear to hold not
just for singlehop topologies and smooth traffic sources, but
alsofor multi-hop configurations and long-range depen-dent traffic
as we have tested. We cannot, withinreasonable time, verify our
approach in an exhaus-tive and comprehensive way, but our
simulation re-sults are encouraging.
E. On the Appropriate Value of In Section III-B we showed that
has two re-
lated effects on the admission control algorithm: (1)too small a
results in more delay violations andlower link utilization, (2) too
long a depresses uti-lization by keeping the artificially
heightened mea-sured values for longer than necessary. While
thefirst effect is linked to flow duration only if the flowexhibits
long-range dependence, the second effectis closely linked to the
average flow duration in gen-eral. The results in this section are
meant to becanonical illustrations on the effect of on the
ad-mission control algorithm, thus we do not providethe full
details of the simulations from which theyare obtained.
In Table IX(a) we show the average link utiliza-tion and maximum
experienced delay from simu-lations of flows with average duration
of 300 sec-
TABLE IXEFFECT OF
�AND �
(a)�%Util � � ���
1e4 82 255e4 81 221e5 77 152e5 75 135e5 68 5
(b) �� 1e4 1e5
%Util � � � � %Util � � � �3000 86 48 82 24900 84 32 80 16300 82
25 77 15100 81 21 76 11
30 78 15 69 7
onds. We varied the measurement window, , from� � � packet times
to � ��� packet times. Notice howsmaller yields higher utilization
at higher expe-rienced delay and larger keeps more reliable de-lay
bounds at the expense of utilization level. Nextwe fixed and varied
the average flow duration.Table IX(b) shows the average link
utilization andmaximum experienced delay for different values
ofaverage flow duration with fixed at � � � and � ��� .We varied
the average flow duration from 3000 sec-onds (practically infinite,
given our simulation du-ration of the same length) to 30 seconds.
Noticehow longer lasting flows allow higher achieved
linkutilization while larger measurement periods yieldlower link
utilization. Link utilization is at its high-est when the � � ratio
is the largest and at its low-est when this ratio is the smallest.
On the otherhand, the smaller � � ratio means lower experi-enced
delay and larger � � means the opposite—thus lowering the � � ratio
is one way to decreasedelay violation rate.
In Figures 4 and 5 we provide sample path snap-shots showing the
effect of on delay and linkutilization. We note however, a that
yields ar-tificially low utilization when used in conjunctionwith
one source model may yield appropriate uti-lization when used with
burstier sources or sourceswith longer burst time.
15
-
(a) Smaller (b) Larger
2035 2050
010
30
Simulated Time (secs.)
Del
ay (
mse
cs.)
Act
ual/M
easu
red
2035 2050
010
30
Simulated Time (secs.)
Del
ay (
mse
cs.)
Act
ual/M
easu
red
Fig. 4. Effect of�
on Experienced Delay
(a) Larger (b) Smaller
1880 1940 2000
01
2
Simulated Time (secs.)
Util
izat
ion
Mea
sdA
ctua
l/
# F
low
s74
90
1880 1940 2000
01
2
Simulated Time (secs.)
Util
izat
ion
Util
izat
ion
Mea
sdM
easd
Act
ual/
# F
low
s17
3Fig. 5. Effect of
�on Link Utilization
F. On Unequal Flow Rejection Rates
Almost all admission control algorithms in theliterature are
based on the violation preventionparadigm: each switch decides to
admit a flow ifand only if the switch can still meet all of its
ser-vice commitments. In other words, the only criteriaconsidered
by admission control algorithms basedon the violation prevention
paradigm is whether anyservice commitments will be violated as a
result of anew admission. In this section we discuss some pol-icy
or allocation issues that arise when not all flowsare completely
equivalent. When flows with dif-ferent characteristics—either
different service re-quests, different holding times, or different
pathlengths—compete for admission, admission con-trol algorithms
based purely on violation preventioncan sometimes produce
equilibria with some cate-gories of flows experiencing higher
rejection ratethan other categories do. In particular, we iden-tify
two causes of unequal rejection rate: (1) flowstraversing a larger
number of hops have a higherchance of being rejected by the
network, and (2)flows requesting more resources are more likely
tobe rejected by the network.
Effect of Hop Count on Flow Rejection Rates. .As expected, when
the network is as loaded asin our simulations, multi-hop flows face
an in-creased chance of being denied service by the net-work. For
example, in our simulation with homo-
geneous sources on the TWO-LINK network, as re-ported in Table
IV, more than 75% of the 700 newEXP1 sources admitted under
guaranteed serviceare single-hop flows. This is true for both of
thebottleneck links. A somewhat smaller percentageof the more than
1000 flows admitted under pre-dictive service are single-hop flows.
This effectis even more pronounced for sources that requestlarger
amount of resources, e.g. the POO2 or thefARIMA sources. And it is
exacerbated by sourceswith longer lifetimes: with fewer departures
fromthe network, new flows see an even higher rejectionrate.
Aside from disparity in the kinds of flow presenton the link,
this phenomenon also affects linkutilization; upstream switches
(switches closer tosource hosts) could yield lower utilization
thandownstream switches. We observe two causes tothis: (1) switches
that carry only multi-hop flowscould be starved by admission
rejections at down-stream switches. The utilization numbers of link
L6in both Tables IV and VIII are consistently lowerthan the
utilization of the other links in the FOUR-LINK topology. Notice
that we set these simula-tions up with no single hop flow on link
L6. Thelow utilization is thus not due to the constraint puton by
link L6’s own admission decisions, but ratheris due to multi-hop
flows being rejected by down-stream switches. (2) Non-consummated
reserva-tions depress utilization at upstream switches; to
il-lustrate: a flow admitted by an upstream switch islater rejected
by a downstream switch; meanwhile,the upstream switch has increased
its measurementestimates in anticipation of the new flow’s
traffic,traffic that never come. It takes time (to the ex-piration
of the current measurement window) forthe increased values to come
back down. Duringthis time, the switch cannot give the reserved
re-sources away to other flows. We can see this ef-fect by
comparing the utilization at the two bottle-neck links of the
TWO-LINK topology as reportedin Table IV. Note, however, even with
the presenceof this phenomenon, the utilization achieved
underpredictive service with our measurement-based ad-mission
control algorithm still outperforms thoseachieved under guaranteed
service.
16
-
Effect of Resource Requirements on Flow RejectionRates. .
Sources that request smaller amount of re-sources can prevent those
requesting larger amountof resources from entering the network. For
exam-ple, in the simulation using the EXP2–EXP3 sourcepair reported
in Table VI, 80% of the 577 new guar-anteed flows admitted after
the simulation warmupperiod were EXP2 flows, which are less
resource de-manding. In contrast, 40% of flows admitted
underpredictive service with our measurement-based ad-mission
control algorithm were the more resourcedemanding EXP3 flows.
Another manifestation ofthis case is when there are sources with
large bucketsizes trying to get into a high priority class.
Be-cause the delay of a lower priority class is affectedby both the
rate and bucket size of the higher prior-ity flow (as explained in
Section II-A), the admis-sion control algorithm is more likely to
reject flowswith a large bucket size and high priority than
thosewith a smaller bucket size or low priority. We seethis
phenomenon in the simulation of source modelEXP3 reported in Table
V. When all sources re-quest either of the two classes of
predictive servicewith equal probability, of the 1162 flows
admittedafter the simulation warmup period, 83% were ofclass 2.
When sources request guaranteed or sec-ond class predictive
service, only 8% of the 1137new flows ends up being guaranteed
flows. In bothof these scenarios, the link utilization achieved
is31%, which is lower than the 62% achieved whenall flows request
only class 2 predictive service (seeTable III), but still order of
magnitude higher thanthe 2% achieved when all flows request only
guar-anteed service (again, see Table III).
We consider the unequal rejection rate phe-nomenon a policy
issue (or rather, several policyissues) because there is no delay
violations andthe network is still meeting all its service
commit-ments (which is the original purpose of admissioncontrol);
the resulting allocation of bandwidth is,however, very uneven and
might not meet somepolicy requirements of the network. We want
tostress that this unequal rejection rate phenomenonarises in all
admission control algorithms basedon the violation prevention
paradigm. In fact, ourdata shows that these uneven allocations
occur insharper contrast when all flows request guaranteed
service, when admission control is a simple band-width check.
Clearly, when possible service com-mitment violations is the only
admission controlcriteria, one cannot ensure that policy goals will
bemet. Our purpose in showing these policy issuesis to highlight
their existence. However, we do notoffer any mechanisms to
implement various policychoices; that is the subject of future
research andis quite orthogonal to our focus on measurement-based
admission control.
V. MISCELLANEOUS PRACTICAL DEPLOYMENTCONSIDERATIONS
We have not yet addressed the issue of how toadjust the level of
conservatism (through ) au-tomatically, and this will be crucial
before suchmeasurement-based approaches can be widely de-ployed.
The appropriate values of , and the otherparameters, must be
determined from observed traf-fic over longer time scales than
discussed (and sim-ulated) here. We have not yet produced such
anhigher order control algorithm. In the simulationspresented in
this paper, we chose a value of foreach simulation that yielded no
delay bound viola-tion over the course of the simulation at
“accept-able” level of utilization.
We should also note that our measurement-basedapproach is
vulnerable to spontaneous correlationof sources, such as when all
the TV channels aircoverage of a major event. If all flows
suddenlyburst at the same time, delay violations will result.We are
not aware of any way to prevent this kindof delay violation, since
the network cannot predictsuch correlations beforehand. Instead, we
rely onthe uncorrelated nature of statistically multiplexedflows to
render this possibility a very unlikely event.
As we mentioned earlier, when there are onlya few flows present,
or when a few large-grainflows dominate the link bandwidth, the
unpre-dictability of individual flow’s behavior dictatesthat a
measurement-based admission control algo-rithm must be very
conservative. One may needto rely less on measurements and more on
theworst-case parameters furnished by the source, andperform the
following bandwidth check instead of
17
-
Eqn. 5:
� �����"%.=3������
�" �-$ (12)where,
�"%. � �"%.=3��! MAX !� $ "%.U� �"%. &H& $�" � � �" � 3�
! MAX !� $ " � � �" � &H& $ � � ������� W $"%. is the sum
of all reserved guaranteed rates, " � isthe sum of all reserved
rates in class � , W is numberof predictive classes, and is a
fraction between 0and 1. For � � , we have the completely
conser-vative case. Similarly, one could do the followingdelay
check:
� � � �� ���� � 5�� � ��� � 5�� ��I"%.U���� ��������� " � �
(13)
for every predictive class � for which one needs todo a delay
check as determined in Section II-D.
VI. CONCLUSION
In this paper we presented a measurement basedadmission control
algorithm that consists of twologically distinct pieces, the
criteria and the esti-mator. The admission control criteria are
based onan equivalent token bucket filter model, where
eachpredictive class aggregate traffic is modeled as con-forming to
a single token bucket filter. This enablesus to calculate worst
case delays in a straightfor-ward manner. The estimator produces
measuredvalues we use in the equations representing our ad-mission
control criteria. We have shown that evenwith the most simple
measurement estimator, it ispossible to provide a reliable delay
bound for pre-dictive service. Thus we conclude that
predictiveservice is a viable alternative to guaranteed servicefor
those applications willing to tolerate occasionaldelay violations.
For bursty sources, in particu-lar, predictive service provides
fairly reliable delaybounds at network utilization significantly
higherthan those achievable under guaranteed service.
APPRECIATIONS
This extended version of our ACM SIGCOMM’95paper [JDSZ95] has
benefited from discussionswith Sally Floyd, Srinivasan Keshav, and
Walter
Willinger; it has also been improved by incorporat-ing
suggestions from the anonymous referees. Wethank them.
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