improving experience-based admission control through ...menth/papers/menth07l.pdf ·...

Improving Experience-Based Admission Controlthrough Traffic Type Awareness

Jens Milbrandt, Michael Menth, and Jan JunkerUniversity of Wurzburg, Institute of Computer Science, GermanyEmail: {milbrandt, menth, junker}@informatik.uni-wuerzburg.de

Abstract— Experience-based admission control (EBAC) is ahybrid approach combining the classical parameter-basedand measurement-based admission control. EBAC calculatesan appropriate overbooking factor used to overbook linkcapacities with resource reservations in packet-switched net-works. This overbooking factor correlates with the averagepeak-to-mean rate ratio of all admitted traffic flows on thelink. So far, a single overbooking factor is calculated forthe entire traffic aggregate. In this paper, we propose type-specific EBAC which provides a compound overbookingfactor considering different types of traffic that subsumeflows with similar peak-to-mean rate ratios. The concept canbe well implemented since it does not require measurementsof type-specific traffic aggregates. We give a proof of conceptfor this extension and compare it with the conventionalEBAC approach. We show that EBAC with type-specificoverbooking leads to better resource utilization under nor-mal conditions and to faster response times for changingtraffic mixes.

Index Terms— admission control, reservation overbooking,quality of service, resource & traffic management

I. I NTRODUCTION

Internet service providers operating next generationnetworks (NGNs) are supposed to offer quality of service(QoS) to their customers. As the packet-based Internetprotocol (IP) technology becomes more and more the ba-sis of these networks, QoS in terms of limited packet loss,packet delay, and jitter is required to support real-timeservices. There are two fundamentally different methodsto implement QoS: capacity overprovisioning (CO) andadmission control (AC). With CO the network has somuch capacity that congestion becomes very unlikely [1],[2], but this also implies that its utilization is very loweven in the busy hour. Although CO is basically simple, itrequires traffic forecasts and capacity provisioning mustbe done on a medium or long time scale.

In contrast, AC works on a smaller time scale. Itgrants access to flows with QoS requirements if thenetwork load is sufficiently low and rejects excessiveflow requests to shelter the network from overload beforecritical situations occur. QoS is thus realized by flow

This paper is based on “Experience-Based Admission Control withType-Specific Overbooking,” by J. Milbrandt, M. Menth, and J. Junker,which appeared in the Proceedings of the6

th IEEE InternationalWorkshop on IP Operations and Management (IPOM), Dublin, Ireland,October 2006.c© 2006 Springer.

This work was funded by Siemens AG, Munich, and the GermanResearch Foundation (DFG). The authors alone are responsible for thecontent of the paper.

blocking during overload situations. Compared to CO,AC requires less capacity and yields better resourceutilization at the expense of more signaling, coordinationand state management [3]–[5] especially in the context ofa network-wide AC [6].

A. Conventional Link Admission Control

In this work, we focus on link-based AC, i.e., onAC methods that protect a single link against overload.These methods are usually extended for application innetworks on, e.g., a link-by-link basis. AC approaches cancoarsely be classified into parameter-based AC (PBAC),measurement-based AC (MBAC), and derivatives thereof.

1) Parameter-Based Admission Control:PBAC, alsoknown as (a priori) traffic-descriptor-based AC, is anapproach appropriate for guaranteed network services [7],i.e., for traffic with imperative QoS requirements. It reliessolely on traffic descriptors that are signaled by sources orapplications and that describe the traffic characteristicsofa flow, e.g. its mean and peak rate together with tokenbucket parameters. If an admission request succeeds,bandwidth is reserved and guaranteed to the new flow.PBAC is often inefficient regarding its resource utilizationsince the traffic descriptors usually overestimate the actualrate to avoid packet delay and loss due to spacing or polic-ing. With PBAC, traffic is limited either by deterministicworst case considerations like network calculus [8], [9] orby stochastic approaches such as effective bandwidth [10].In addition, PBAC calculations for heterogeneous trafficmixes can be very complex.

2) Measurement-Based Admission Control:MBAC, incontrast, is an AC method adequate for controlled loadnetwork services [11], i.e. for traffic with less stringentQoS requirements. It measures the current link or networkload in real-time and takes a rate estimate of the newflow to make the admission decision. The determinationof the traffic characteristics is thus shifted from the sourceor application to the network, and the specified trafficdescriptor, e.g. the peak rate, can be very simple. MBACcan be classified into different approaches.

• Aggregate-oriented MBAC (A-MBAC)Most MBACapproaches measure the traffic properties of theentire traffic aggregate admitted to the link. Theeffective bandwidths of a flow is only required forthe initial admission decision when the requestedbandwidth is compared to the available link capacity.

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© 2007 ACADEMY PUBLISHER

For that purpose, the rate of the admitted trafficaggregate is sufficient. A-MBAC has two advantages.The traffic measurement is simple since no per flowmeasurement states have to be managed and thestatistical properties of a stationary traffic aggregateare more stable than those of individual traffic flows.On the other hand, the admission of new flows andthe termination of others make the traffic aggregatea non-stationary process which must be carefullyobserved [12]. Comparisons of different A-MBACapproaches can be found in [13]–[19].

• Flow-oriented MBAC (F-MBAC)Some MBAC ap-proaches use flow-specific measurements to assessthe bandwidth consumption of each traffic flowindividually. The initial effective bandwidth of anew flow is calculated based on its declared trafficdescriptor. As soon as the confidence in the measure-ments of an admitted flow is high enough, its initialbandwidth is substituted by a calculated update basedon the measured traffic parameters. Examples of F-MBAC methods are given in [20]–[23].

All previously addressed MBAC methods use real-timemeasurements and admit traffic as long as enough capac-ity is available. The downside of MBAC is its sensitivityto measurement accuracy and its susceptibility to traf-fic prediction errors which can occur, e.g., during QoSattacks, i.e., when admitted traffic flows are temporarily“silent” to provoke an underestimation of the admittedtraffic rate and congest the link later by simultaneouslysending at high bitrate.

B. Experience-Based Admission Control

To the best of our knowledege, experience-based AC(EBAC) is the first hybrid AC approach that takes ad-vantage of traffic measurements without real-time re-quirements. It uses information about previously admittedtraffic and past measurements to make current admissiondecisions.

The concept of EBAC is introduced in [24]. Its detailsare described in [25] and can be summarized as follows:with EBAC, a new flow is admitted to a link at timetif its peak rate together with the peak rates of alreadyadmitted flows does not exceed the link capacity mul-tiplied by an overbooking factorϕ(t). The overbookingfactor is calculated based on the reservation utilizationof the admitted flows in the past. Hence, this methodrelies on experience. EBAC also requires traffic measure-ments to compute the reservation utilization, but thesemeasurements do not have real-time requirements andthus influence the admission decision only indirectly. Theproof of concept for EBAC is given in [25] by simulationsand corresponding waiting time analyses of the admittedtraffic. In particular, the steady state performance ofEBAC is investigated for traffic with static characteristics.Since MBAC methods are sensitive to traffic variability,we investigate the behavior of EBAC in the presence oftraffic changes in [26], [27]. Those changes may be dueto variations on the packet and/or the flow scale level of

the traffic. In [26], traffic changes on the packet scalelevel are investigated with regard to the performance ofEBAC. A single overbooking factor is calculated basedon the traffic characteristics of the entire admitted trafficaggregate. This article is based on the results of [27] andfocuses on traffic changes on the flow scale level. Weextend EBAC towards type-specific overbooking (TSOB),i.e., we make the EBAC mechanism aware of differenttraffic types and use additional information about theindividual characteristics of these traffic types and aboutthe composition of the admitted traffic mix to calcu-late a compound overbooking factorϕc(t). We thereforeconsider different types of traffic subsuming flows withsimilar peak-to-mean rate ratio and also their share inthe currently admitted traffic mix. The TSOB extensionof EBAC can be well implemented since it does notrequire type-specific measurements. We give a proof ofconcept for EBAC with TSOB and compare it with theconventional EBAC approach. We show that EBAC withTSOB leads to better resource utilization under normaltraffic conditions and to faster response times in case ofchanging traffic mixes. Unlike conventional EBAC, theextension completely prevents congestion due to over-reservation if the fraction of flows with high reservationutilizations increases in the traffic mix, i.e., if the trafficintensity increases due to a changing composition of theadmitted traffic mix.

The remainder of this article is organized as follows.In Section II, we review the EBAC concept. Section IIIdescribes our simulation design and the applied trafficmodel. Section IV proposes the extension of EBAC to-wards type-specifc overbooking (TSOB). The simulationresults in Section V show the superiority of EBAC withTSOB over conventional EBAC. Further work on EBACis briefly presented in Section VI. Section VII finallysummarizes this work and gives a conclusion.

II. FUNDAMENTALS OF EXPERIENCE-BASED

ADMISSION CONTROL (EBAC)

Experience-based AC (EBAC) is a hybrid approachcombining functional elements of PBAC and MBAC in anovel AC concept. It therefore implements link admissioncontrol, but can be easily extended to a network-widescope. EBAC relies on peak rate traffic descriptors whichmay be significantly overestimated in the signaled flowrequests. The utilization of the overall reserved capacitygives an estimate for the peak-to-mean rate ratio (PMRR)of the traffic aggregate and allows for the calculation of afactor to overbook the link capacity. The idea is simple,but safety margins are required to provide sufficient QoSand questions arise regarding its robustness against trafficflows with varying rates and burstiness. In this section,we elaborate the EBAC concept and describe its basicfunctional components.

A. Admission Decision on a Single Link

EBAC makes an admission decision as follows. An ACentity limits the access to a linkl with capacitycl and

12 JOURNAL OF NETWORKS, VOL. 2, NO. 2, APRIL 2007


records the admitted flowsF(t) at any timet togetherwith their requested peak rates{rf : f ∈ F(t)}. When anew flow fnew arrives, it requests for a peak raterfnew

.If

rfnew+∑

f∈F(t)

rf ≤ cl · ϕ(t) · ρmax (1)

holds, admission is granted andfnew joins F(t). Other-wise, the new flow request is rejected. Flows are removedfrom F(t) on termination. The experience-based over-booking factorϕ(t) is calculated by statistical analysisand indicates how much more bandwidth thancl canbe safely allocated for reservations. The maximum linkutilization thresholdρmax limits the traffic admissionsuch that the expected packet delayW exceeds an upperthresholdWmax only with probabilitypW .

B. Calculation of the MaximumLinkUtilizationThreshold

The value ofρmax depends significantly on the trafficcharacteristics and the capacitycl of the EBAC-controlledlink. Simple solutions are based on theM/M/1−∞ andtheN ·D/D/1−∞ queuing system. Real-time traffic pro-duced from, e.g., voice or video applications has a ratherconstant output rate that can be controlled by a spacersuch that a maximum flow rate is enforced. Therefore, wecalculate the thresholdρmax based on theN ·D/D/1−∞approach, which assumesN homogeneous traffic flows inF , each sending packets of constant sizeB (in bits) andwith constant packet inter-arrival timesA (in seconds).An analysis method for this queueing system is presentedSection 15.2.4 of [10]. More details on the computationof ρmax can be found in [25]. For the ease of simulationof changing traffic, we set the maximum link utilizationto a conservative and constant value ofρmax =0.95.

C. Calculation of the Overbooking Factor

The overbooking factorϕ(t) depends on the admittedtraffic F(t) which, in turn, depends on timet becausenew flows are admitted and existing ones terminate. Forthe computation ofϕ(t), we defineR(t) =

∑

f∈F(t) rf

as the reserved bandwidth of all admitted flows at timetand C(t) denotes their unknown cumulated mean rate.EBAC measures the consumed link bandwidthM(t) ofthe overall reservationR(t). To obtain M(t), we usemeasurements based on equidistant, disjoint intervals suchthat for an intervalI(ti)= [ti, ti+∆] with length∆, themeasured rateM(ti) = Γ(ti)

∆ is determined by meteringthe traffic volumeΓ(ti) sent duringI(ti). For the ratesR(t) and M(t), a time statistic for the reservation uti-lization U(t) = M(t)

R(t) is collected. The valuesU(t) aresampled in constant time intervals and are stored as hitsin bins for a time-dependent histogramP (t, U). From thishistogram, the time-dependentpu-percentileUp(t) of theempirical distribution ofU(t) can be derived by

Up(t) = minu

{u : P (t, U ≤ u) ≥ pu}. (2)

Since traffic characteristics change over time, the reserva-tion utilization statistic must forget obsolete data to reflect

the properties of the new traffic conditions. Therefore,we record new samples ofU(t) by incrementing thecorresponding histogram bin by one and devaluate thecontents of all histogram bins in regular devaluationintervals Id by a constant devaluation factorfd. Thedevaluation process determines the memory of EBAC.The reciprocal of the reservation utilization percentile isthe overbooking factor

ϕ(t) =1

Up(t)(3)

which is computed each time a new valueU(t) is recordedin the histogram. To obtain a meaningful value forUp(t),enough statistical data must be collected before Equa-tion (3) yields a reliable overbooking factor.

D. Peak-to-Mean Rate Ratio (PMRR)

The intrinsic idea of EBAC is the exploitation of thepeak-to-mean rate ratio (PMRR) of the traffic aggregateadmitted to the link. With EBAC, the signaled peak raterf

of an admitted flowf is enforced by a traffic shaper. Incontrast to reality, the mean ratecf of a flow is known apriori in our simulations. We define the PMRR of a flowby kf =

rf

cf. Analogously,K(t)= R(t)

C(t) denotes the PMRRof the entire traffic aggregate admitted to the link at timet.K(t) is an upper limit for the achievable overbookingfactor ϕ(t).

E. Memory of EBAC

1) Implementation as Simple Histogram:The his-togramP (t, U), i.e. the collection and the aging of thereservation utilization statistic, implements the memoryofEBAC. This memory correlates successive flow admissiondecisions and consequently influences the adaptation ofthe overbooking factorϕ(t) to changing traffic conditionson the link. The statistic aging process, characterized bythe devaluation intervalId and the devaluation factorfd,makes this memory forget about reservation utilizations inthe past. The parameter pairs(Id, fd) yield typical half-life periods TH after which collected valuesU(t) havelost half of their importance in the histogram. Therefore,we have1

2 =fTH/Id

d and define the EBAC memory basedon its half-life period

TH(Id, fd) = Id ·−ln(2)

ln(fd). (4)

With Equation (4), different combinations of devalua-tion parameters(Id, fd) and (Id′ , fd′) yield equal half-life periods if eitherId′ = ln(fd′)/ln(f

(1/Id)d ) or fd′ =

f(Id′/Id)d holds. However, these equations guarantee only

that the two resulting histograms are identical at timest = LCM(Id, Id′), where LCM denotes the least com-mon multiple. The reservation utilizations obtained in theintervals Id and Id′ are devaluated differently for thetwo parameter sets due to the length of the devaluationinterval and the different devaluation factors. This leadstointermediate deviations between the two histograms andconsequently to different overbooking factors.



2) Implementation as Time Exponentially-WeightedMoving Histogram: To express the performance of theEBAC memory by only its characteristic half-life period,we introduce the method of a time exponentially-weightedmoving histogram (TEWMH) [28] which also improvesthe timeliness of the overbooking factor calculation.This method follows the principle of time exponentially-weighted moving average (TEWMA) [29] used to im-prove the timeliness of rate measurements, and it logicallyextends TEWMA for application to statistical histograms.

Based on the EBAC memory defined in Equation (4),we define the aging ratea = ln(fx)

Ix, x ∈ {d, d′}. Ratea

is constant for two parameter sets(Id, fd) and (Id′ , fd′)if they yield the same half-life periodTH . Instead ofincrementing the histogram bins by one, we weight thereservation utilization hits in the time interval[ti, ti+Ix]exponentially by the weight factor1eat and use the resultas an increment for the bins. Parametert ∈ [0, Ix − 1]thereby denotes the time offset of the sampled reservationutilization in seconds since the last devaluation. This way,newer valuesU(t) experienced in the interval[ti, ti+Ix]become more important than older values and, as aconsequence, all reservation utilizations gathered in thisinterval are evenly devaluated at its end. In addition, thehistograms of both parameter sets are comparable at anytime and always lead to identical overbooking factorsdepending only on the half-life periodTH .

III. EBAC PERFORMANCESIMULATION

In this section, we first present the simulation designof EBAC on a single link and then describe the trafficmodel we used on the flow and packet scale level.

A. Simulation Design

We evaluate the performance of EBAC on a singlelink by discrete event simulation. The simulator is im-plemented in JavaTM and based on a simulation librarycalled JSimLib which has been developed at the Chairof Distributed Systems of the University of Wurzburg inthe past years. The design of the simulation is shownin Figure 1. Different types of trafficsource generatorsproduce flow requests that are admitted or rejected by theadmission controlentity. The flows request reservationsof different bandwidths which leads to different request-dependent blocking probabilities on a heavily loaded link.To avoid this, we apply trunk reservation [30], i.e., aflow is admitted only if a flow request with maximumreservation size could also be accepted. For an admissiondecision, the AC entity takes the overbooking factorϕ(t)into account and admits a flow if Equation (3) holds. Inturn, the AC entity provides information regarding thereservationsR(t) to the EBAC systemand yields flowblocking probabilitiespb(t). For each admitted source, atraffic generatoris instantiated to produce a packet flowthat is shaped to its contractually defined peak rate. Trafficflows leaving thetraffic shapersare then multiplexed onthe bufferedlink l with capacity cl. The link provides

information regarding the measured trafficM(t) to theEBAC system and yields packet delay probabilitiespd(t),packet loss probabilitiespl(t), and the packet waitingtime W (t). In the presence of traffic changes, an impor-tant performance measure for the EBAC mechanism is theoverall response timeTR(t), i.e., the time span requiredby the EBAC system to fully adapt the overbookingfactor ϕ(t) to a new traffic situation.

B. Traffic Model

In our simulations, the traffic controlled by EBAC ismodelled on two levels, i.e. the flow scale level and thepacket scale level. While the flow level controls the inter-arrival times of flow requests and the holding times ofadmitted traffic flows, the packet level defines the inter-arrival times and the sizes of packets of individual flows.

1) Flow Level Model: On the flow level, we dis-tinguish different traffic source types, each associatedwith a characteristic peak-to-mean rate ratio (PMRR) andcorresponding to a source generator type in Figure 1.The inter-arrival time of flow requests and the holdingtime of admitted flows both follow a Poisson model [31],i.e., new flows arrive with rateλf and the duration of aflow is controlled by rateµf . The mean of the flow inter-arrival time is given by1/λf and the holding time of aflow is exponentially distributed with a mean of1/µf .Provided that no blocking occurs, the overall offeredload af =

λf

µfis the average number of simultaneously

active flows measured in Erlang. To saturate an EBAC-controlled link with traffic, the load is set toaf ≥ 1.0. Thelatter assumption allows for an evaluation of the EBACperformance under heavy traffic load such that some flowrequests are rejected.

2) Packet Level Model:On the packet level, we ab-stract from the wide diversity of packet characteristicsinduced by the application of different transmission layerprotocols. Since we are interested in the basic under-standing of the behavior of EBAC, we abstain from realtraffic patterns and define a flow of consecutive datapackets simply by a packet size distribution and a packetinter-arrival time distribution. Both contribute to the ratevariability within a flow that is produced by a trafficgenerator in Figure 1. To keep things simple, we assumea fixed packet size per flow and use a Poisson arrivalprocess to model a packet inter-arrival time ditributionwith rateλp. We are aware of the fact that Poisson is nota suitable model to simulate Internet traffic on the packetlevel [32]. We therefore generate Poisson packet streamsand subsequentially police the individual flows with peak-rate traffic shapers (cf. Figure 1). The properties of theflows are significantly influenced by the configuration ofthese shapers. In practice, the peak raterf of a flow fis limited by an application or a network element andthe mean ratecf is often unknown. In our simulations,however, the mean rate is known a priori and, therefore,we can control the rate of flowf by its peak-to-mean rateration (PMRR)kf =

rf

cf.



j

source generator

type 1

source generator

type n

.

.

.

.

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.

.

.

ad

mis

sio

nc

on

tro

l

flowrequests

flow

requ

ests

EBAC system

traffic generator

type x ?{1;n}

traffic generator

type y?{1;n}

admitted

sources

admitted

sources

.

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(t)R(t)

traffic

shaper

traffic

shaper

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packet

flows

packet

flowsbuffer

TR(t)

M(t)

pd(t)

pl(t)

pb(t)

W(t)

link

Figure 1. Simulation design for EBAC performance evaluation.

3) Traffic Variations: Traffic variations may be due tochanges of traffic characteristics on the packet and/or theflow scale level. Variations on the packet level are causedby individual traffic sources that change their sendingbehavior. Variations on the flow level signify a changeof the traffic mix. This article studies the performanceof EBAC for traffic changes on the flow scale level. Inthe corresponding simulations, the traffic mix comprisingflows with different characteristic PMRRskf varies overtime which directly impacts the traffic load on the link.Hence, we investigate the transient behavior of EBACthrough simulation of traffic changes which are caused byvariations of the traffic mix and characterized by eithera decrease or increase of the traffic intensity. In practice,applications know and signal the peak ratesrf of theircorresponding traffic flows. The type of an applicationcan be determined, e.g., by a signaled protocol number.We use only these limited information in our simulations.The mean ratescf of the flows are not known to the EBACmeasurement process, they are just model parameters forthe packet flow generation and thus serve for controllingthe rate of flowf by its PMRRkf =

rf

cf.

IV. EBAC WITH TYPE-SPECIFICOVERBOOKING

In this section, we present type-specific overbooking(TSOB) as a concept extending EBAC. So far, we onlyconsider the traffic characteristics of the entire aggregateof admitted traffic flows and calculate a single factor tooverbook the link capacity. We now include additionalinformation about the characteristics of individual traffictypes and their share in the currently admitted trafficmix to calculate a compound type-specific overbookingfactor. First, we describe the system extension and thenwe show how the compound overbooking factor forEBAC with TSOB can be estimated without type-specifictraffic measurements. Finally, we present some simulationresults showing the advantage of EBAC with TSOB overconventional EBAC.

A. EBAC System Extension

In general, the traffic aggregate on a link is composedof flows of different traffic typesi for which the peak-to-mean rate ratios (PMRRs)Ki remain rather constantover time. Parameteri then denotes a traffic type sub-suming flows of different applications but with similarPMRRs Ki. For admission, each flow is supposed toregister at the AC entity with its peak rate and its traf-fic type. This yields type-specific aggregate reservationsRi(t) for which

∑ni=0 Ri(t)=R(t) holds. On arrival of a

new flowfnewi , Ri(t) is increased by the peak raterfnew

i

of the flow and it is decreased by the same rate whenthe flow terminates. The valueαi(t) = Ri(t)/R(t) thenreflects the share of a traffic typei in the mix and theentire traffic composition consisting ofn different traffictypes is given by vector

α(t) =

(

α1(t)

...αn(t)

)

,

n∑

i=1

αi(t) = 1. (5)

EBAC with TSOB uses information about the PMRRsKi

and the time-dependent traffic compositionα(t) to de-termine type-specific reservation utilizationsUi(t). InSection IV-B, we explain how the valuesUi(t) canbe estimated without type-specific traffic measurements.The valuesUi(t) are stored as hits in bins of separatehistogramsPi(t, U) which yield type-specific reservationutilization percentilesUp,i(t). The EBAC admission de-cision for a new flowfnew

i of type i is then extendedto

rfnewi

· Up,i(t) +∑

f∈F(t)

rf ·Up,type(f)(t)≤cl ·ρmax. (6)

We weight these percentilesUp,i(t) by their correspond-ing sharesαi(t) and finally calculate the compoundoverbooking factor for EBAC with TSOB as

ϕc(t) =1

∑

i αi(t) · Up,i(t). (7)



B. Estimation of Type-Specific Reservation Utilizations

A crucial issue for the performance of EBAC withTSOB is the estimation of the type-specific reservationutilizations Ui(t). Making type-specific measurementsMi(t) would yield exact values forUi(t)=Mi(t)/Ri(t).For a reduced number of traffic classes, type-specificmeasurements seem feasible if we consider new networktechnologies such as differentiated services (DiffServ)[33] for traffic differentiation and multiprotocol labelswitching (MPLS) [34] for the collection of traffic statis-tics. However, current routers mostly do not provide type-specific traffic measurements and, therefore, we have touse the available parametersM(t), R(t), Ri(t), andα(t)to estimate the valuesUi(t). In the following, we developtwo methods to obtain estimates for the type-specificreservation utilizations.

1) Estimation with Linear Equation Systems (LES):The first method calculates the type-specific reservationutilizationsUi(t) as results of a linear program and usesthe equationU(t) =

∑

i αi(t) · Ui(t) setting up a linearequation system (LES, cf. e.g. [35]) of the form

U(tj−n)...

U(tj)

=

α1(tj−n) . . . αn(tj−n)...

...α1(tj) . . . αn(tj)

U1(tj)...

Un(tj)

,

(8)where n is the number of traffic types andj denotesa time index. LetA(tj) denote the matrix of columnvectors(αi(tj−x))1≤i≤n in Equation (8), then we haveU(tj) = A(tj) · Ui(tj). Hence, a unique solution ofthe LES requires probes ofU(t) and α(t) for t ∈[tj−n, tj ] and n linearly independent columns inA(tj),i.e. det(A(tj)) 6= 0. We calculate a new solution of theLES every time the vectorα(t) changes significantly, i.e.,∃k : |ak(ti)−ak(ti−1)|

ak(ti)> ǫ. A problem of estimating type-

specific reservation utilizations with the LES method isthat the linear independence of the matrix columns inA(tj) is not guaranteed at any timetj when the trafficcomposition changes. In this case, a unique solution forthe equation system does not exist and the valuesUi(tj)cannot be included in the histogramPi(t, U). Therefore,we simply insert the utilizationsUi(tj−x) of the lastfeasible LES until a new linearly independent LES isfound.

Algorithm 1 illustrates the computation of matrixA(tj)with linearly independent column vectors. It takes thecurrent traffic composition vectorα(tj), the previousmatrix A(tj−1), and a setL of unusedα-vectors asinput parameters and returns a linearly independent matrixA(tj) with 1 ≤ i ≤ n columns. The first call of thisalgorithm returns the transposed column vectorα(ti) toan n× 1 matrix. For any further call, vectorα(tj) isinitialized with α(tj). If there are recentα-vectors not yetconsidered inA(tj−1), i.e.L 6= ∅, thenα(tj) joinsL andα(tj) is set as an exponentially-weighted moving average(EWMA) [29] of all yet unconsideredα-vectors inL. The

Input: traffic composition vectorα(tj),previous matrixA(tj−1),setL of unused vectorsα(t)

if A(tj−1)=NULL thenA(tj) := α(tj)

T {first call}RETURN(A(tj))

elseα(tj) := α(tj)if L 6= ∅ then

L := L ∪ α(tj)α(tj) := BUILD EWMAVECTOR(L)NORMALIZE(α(tj))

end ifA(tj) := A(tj−1)n := NUMBEROFELEMENTS(α(tj))if rank(A(tj)) = n then

REMOVEFIRSTCOLUMN(A(tj))end ifAPPENDLASTCOLUMN(A(tj), α(tj)

T )if det(A(tj)) 6= 0 then

L := ∅RETURN(A(tj))

elseRETURN(A(tj−1))

end ifend if

Output: matrix A(tj)

Algorithm 1: GENERATESHAREMATRIX : computationof linearly independent matrix column vectors.

EWMA is calculated over vector elementsαi such that

αi(tx) =

{

αi(tmin) if tmin is earliest time index inL

β ·αi(tx−1)+(1−β)·αi(tx) else.(9)

In Equation (9), the devaluation parameterβ ∈ [0, 1]controls the influence of older valuesαi(tx) whose im-pact decays exponentially withβ. The computation ofα(tj)=(α1(tj), . . . , αn(tj)) can lead to

∑ni=1 αi(tj) 6=1

and, therefore, vectorα(tj) must be normalized after theapplication of the EWMA algorithm.

Finally, we construct a new matrixA(tj) from thematrix A(tj−1) by removing the oldestα-vector fromA(tj−1) and appending the transposed vectorα(tj) to it.However, if the input matrixA(tj−1) is not of sizen×n, acolumn is appended and none is removed. The constructedmatrix A(tj) is then tested for linear independency and,if det(A) 6= 0, it is returned by the algorithm which alsoempties the setL of unconsideredα-vectors. Otherwise,the previous matrixA(tj−1) is returned.

Algorithm 1 requires at leastn calls before it canprovide a matrix with linearly independent column vectorsas necessary for a unique solution of Equation (8). Sincelinear independency cannot be guaranteed for each newvectorα(t), the continuous computation of current type-specific reservation utilizations by the LES method is not



guaranteed. Therefore, this method must be consideredas approximation of the type-specific reservation utiliza-tions Ui(t).

2) Estimation with Least Squares Approximation(LSA): The second approach estimates the type-specificreservation utilizationsUi(t) using a least squares ap-proximation (LSA, cf. e.g. [36]). For the ease of un-derstanding, we illustrate this method, without loss ofgenerality, for two different traffic typesi ∈ {1, 2}.The variablesU1(t) andU2(t) denote their type-specificreservation utilizations. The global reservation utilizationis calculated asU(t) = α1(t) · U1(t)+α2(t) · U2(t) andwith α1(t)+α2(t)=1 we get

U(t) = α1(t) · (U1(t) − U2(t)) + U2(t). (10)

We substituteaj =U1(tj)−U2(tj) andbj =U2(tj) andobtain the least squares errorε for parametersU1(t) andU2(t) if we minimize the term

ε = minam,bm

m∑

j=1

[U(tj) − (α1(tj) · am + bm)]2. (11)

The time indexj thereby covers all valuesU(tj) andα(tj) from the first probe (j =1) to the last (j =m) everdetermined by the EBAC system. We find the minimumof ε where the first derivatives of Equation (11) yield zero,i.e., we set∂ε

∂a

!=0 und ∂ε

∂b

!=0 and resolve these equations

to parametersam andbm which yields

am =m·∑

jα1(tj)U(tj)−∑

jα1(tj)·∑

jU(tj)

m·∑

jα1(tj)2−(

∑

jα1(tj))2 (12a)

bm =

∑

jU(tj)·∑

jα1(tj)2−∑

jα1(tj)·∑

jα1(tj)U(tj)

m·∑

jα1(tj)2−(

∑

jα1(tj))2

(12b)

for 1 ≤ j ≤ m. The sums in Equations (12a) and (12b)can be computed iteratively which helps to cope with thelarge set of parameter values observed over all timestj .To limit the effect of short-time fluctuations, we apply theTEWMA algorithm to these sums. LetSm denote any ofthe sums in Equations (12a) and (12b) at timetm, thenthe TEWMA at timetm+1 is

S(tm+1) = S(tm) · e−γ·(tm+1−tm) + x(tm+1). (13)

In Equation (13), the devaluation factorγ ∈ [0, 1]leads to an exponential decay of older valuesx(tj≤m)in the sumS. This incremental implementation of theLSA method is efficient and enables its application tomore than two different traffic types. With the calculatedparametersam andbm, the estimates for the type-specificreservation utilizations are finally obtained asU1(tm) =am+bm andU2(tm)=bm.

3) Comparison of Measured and Estimated Type-Specific Reservation Utilizations:We perform simula-tions with both previously described methods estimatingthe type-specific reservation utilizations. For the sakeof clarity, we simulate with only two different traffic

0.0

0.2

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0.8

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Simulation time t (s)

U (t)M

2

U (t)E

1U (t)

M

1

U (t)E

2

Re

se

rva

tio

n u

tiliz

atio

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(t)

XR

ese

rva

tio

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tiliz

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(a) Estimation with linear equation systems

0.0

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U (t)M

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2

Re

se

rva

tio

n u

tiliz

atio

n U

(t)

XR

ese

rva

tio

n u

tiliz

atio

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i

U (t)M

1U (t)

E

1

(b) Estimation with least squares approximation

Figure 2. Comparison of measured and estimated type-specific reser-vation utilizations.

typesi∈{1, 2}. Type 1 has a mean PMRR ofE[K1]=2and an initial mean share ofE[α1] = 0.2 in the trafficmix. Traffic type 2 is characterized byE[K2] = 8 andE[α2] = 0.8. All simulations use the same seed for therandom number generator to exclude effects of differentstatistical characteristics of the simulated traffic. Thisguarantees a fair comparison of the results.

Figure 2(a) shows a comparison of the measured type-specific reservation utilizationsUM

i (t) and their corre-sponding estimatesULES

i (t) obtained by the LES method.Figure 2(b) compares the valuesUM

i (t) to their approx-imations ULSA

i (t) achieved with the LSA method. Themeasured and the type-specific reservation utilizations aredetermined every second. On the packet scale level ofthe traffic, we have Poisson distributed inter-arrival timeswhich lead to short-time fluctuations for the measuredvaluesUM

i (t). These fluctuations are clearly damped bythe TEWMA algorithm used for the estimated valuesULES

i (t) andULSAi (t). Obvioulsy, the LES method is not

feasible for the approximation of type-specific reservationutilizations since the resulting estimates deviate stronglyfrom the exact measurements. The reason is that, in manycases, the linear equation system in Equation (8) does



not yield a unique solution. In contrast, the LSA methodprovides good estimates for the corresponding measuredutilizations. Hence, this approach enables EBAC withTSOB without type-specific traffic measurements and isused for the following performance comparisons.

V. PERFORMANCECOMPARISON OF

CONVENTIONAL EBAC AND EBAC WITH TSOB

To investigate EBAC with TSOB, we perform a numberof simulations each associated with a different trafficsituation. For all simulations, we use a link capacityof cl = 10 Mbit/s and simulate with two traffic typesi ∈ {1, 2} with characteristic peak-to-mean rate ratios(PMRRs)E[K1] = 2 and E[K2] = 8. A flow fi of anytype i reserves bandwidth with a peak raterfi

= 768Kbit/s and has a mean holding time of1/µf = 90 s.The mean interarrival time of flow requests is set to1/λf =750 ms such that the link is saturated with traffic,i.e., some flow requests are rejected. For conventionalEBAC we use the overbooking factorϕ(t) according toEquation (3) and for EBAC with TSOB, we calculate thefactorϕc(t) according to Equation (7). We first investigateEBAC with TSOB for a rather constant traffic mix andthen study its behavior for a suddenly changing trafficcompositionα(t).

A. Simulation with Constant Traffic Mix

The first experiment simulates traffic with rather con-stant traffic sharesαi(t), i.e., the composition of the trafficmix remains constant except for statistical fluctuations.The results of a single simulation run are shown inFigure 3(a) for conventional EBAC and in Figure 3(b)for EBAC with TSOB. The mean shares of the traffictypes in the aggregate are set toE[α1] = 0.2 andE[α2] = 0.8. We repeated this experiment 50 times toobtain reliable confidence intervals which proved to bevery small. However, the illustration of a single simu-lation run shows more clearly the advantage of EBACwith TSOB over conventional EBAC. For EBAC withTSOB (cf. Figure 3(b)), the decreases of the PMRRK(t) due to statistical fluctuations ofα(t) lead to asignificant decrease of the overbooking factor (OBF)ϕ(t).The increases ofK(t) due to changingα(t) lead toa significant increase ofϕ(t). For conventional EBAC(cf. Figure 3(a)), these changes happen rather slow and,therefore, the QoS may be at risk or the link capacity maybe underutilized. In contrast, EBAC with TSOB adjusts itscompound overbooking factorϕc(t) very quickly to themodifications ofα(t) and, therefore, it is able to keep themeasured rateM(t) on a clearly more stable level thanconventional EBAC. This, in turn, leads to better QoS andalso improves the resource utilization.

B. Simulation with Changing Traffic Mix

In the following two simulation experiments, we focuson the reaction of EBAC with and without TSOB after adecrease or an increase of the traffic intensity. We consider

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200 400 600 800 1000 1200

0

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8R(t)K(t)

clM(t)


Ba

nd

wid

th (

Mb

it/s

)

OB

F(t), P

MR

R K

(t)j

j(t)

(a) Conventional EBAC

0

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200 400 600 800 1000 1200

0

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8R(t)K(t)

clM(t)


Bandw

idth

(M

bit/s

)

OB

F(t), P

MR

R K

(t)j

jc(t)

(b) EBAC with TSOB

Figure 3. Conventional EBAC vs. EBAC with TSOB for a constanttraffic mix.

sudden changes of the traffic compositionα(t) to haveworst case scenarios and to obtain upper bounds on theEBAC response times.

1) Simulation with Decreasing Traffic Intensity:Weinvestigate the change of the traffic intensity from a highto a low value. Figure 4 shows the average results over50 simulation runs. We use the same two traffic typeswith their characteristic PMRRs as before. However, westart with mean traffic sharesE[α1] = 0.8 and E[α2] =0.2. At simulation time t0 = 1000 s, the mean sharesof both traffic types are swapped toE[α1] = 0.2 andE[α2]=0.8 by changing the type-specific request arrivalrates, i.e., the traffic intensity of the entire aggregatedecreases due to a change in the traffic mixα(t). Thisleads to a sudden increase of the PMRRK(t) whichresults in an immediate decrease of the measured traf-fic M(t) for conventional EBAC (cf. Figure 4(a)). Withobservable delay, the conventional EBAC system adaptsits overbooking factorϕ(t) as a result of the slowlydecreasingpu-percentileUp(t) in the histogramP (t, U).From other simulations [26] we know that this delaystrongly depends on the EBAC memory defined by thehalf-life period TH in Equation (4). In contrast, EBACwith TSOB (cf. Figure 4(b)) increases its overbookingfactorϕc(t) almost at once since thepu-percentiles of thetype-specific histogramsPi(t, U) remain rather constant.



0

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250 1000 1750 2500

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R(t)

cl

M(t)


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th (

Mb

it/s

)

OB

F(t), P

MR

R K

(t)j

j(t)


0

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0

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K(t)

cl

M(t)


Bandw

idth

(M

bit/s

)

OB

F(t), P

MR

R K

(t)j

jc(t)

(b) EBAC with TSOB

Figure 4. Conventional EBAC vs. EBAC with TSOB during a decreaseof the traffic intensity.

Since only the shares of the traffic types in the mixhave changed, the compound overbooking factorϕc(t)is immediately adapted. As a consequence, the fasterreaction of EBAC with TSOB leads to a higher and morestable link utilization.

2) Simulation with Increasing Traffic Intensity:Wenow change the traffic intensity from a low to a high valuewhich leads to a decrease of the PMRRK(t) of the trafficaggregate. The simulation results are shown in Figure 5.Using the same two traffic types as before, we start withmean traffic sharesE[α1]=0.2 andE[α2]=0.8 and swapthem at simulation timet0 = 1000 s to E[α1] = 0.8 andE[α2]=0.2 by changing the type-specific request arrivalrates. This increases the traffic intensity of the aggregatedue to a change in the traffic mixα(t). In this simulationexperiment, the QoS is at risk because flows with lowtraffic intensity are successively replaced by flows withhigh intensity and, therefore, the load on the link isrising. Conventional EBAC (cf. Figure 5(a)) reacts againslower than EBAC with TSOB (cf. Figure 5(b)) althoughthis time, their speed of adapting the overbooking factordiffers less. From other simulations [26] we know thatthe response time of conventional EBAC is independentof the EBAC memory in case of a sudden traffic increase.Our simluation results show that conventional EBACyields a slightly higher link utilization compared to EBAC

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(t)j

jc(t)cl

M(t)

(b) EBAC with TSOB

Figure 5. Conventional EBAC vs. EBAC with TSOB during an increaseof the traffic intensity.

with TSOB. However, this high utilization comes at theexpense of a violation of QoS guarantees as the measuredtraffic M(t) consumes the entire link capacitycl for ashort period of time (cf. Figure 5(a)). As a consequence,the packet delay probabilitypd = P (Packet delay≥50 ms) increases, rising frompd = 0 for EBAC withTSOB to a maximum ofpd ≈ 0.3 for conventional EBACin this experiment. This obviously favours the extensionof EBAC towards type-specific overbooking.

VI. FURTHER WORK ON EBAC

This section briefly presents further work onexperience-based admission control (EBAC). Weillustrate the performance of EBAC in steady state andin the presence of traffic changes on the packet scalelevel. We also present a feasible application of EBAC ina network-wide scope.

A. EBAC in Steady State

The intrinsic idea of EBAC is the exploitation of thepeak-to-mean rate ratioK(t) of the traffic aggregate ad-mitted to the link. In [25], we simulated EBAC on a singlelink with regard to its behavior in steady state, i.e., whenthe properties of the traffic aggregate are rather static.These simulations provided a first proof of concept for



EBAC. We showed for different peak-to-mean rate ratiosthat EBAC achieves a high degree of resource utilizationthrough overbooking while packet loss and packet delayare well limited. Further simulation results allowed usto give recommendations for the EBAC parameters suchas measurement interval length and reservation utilizationpercentile to obtain appropriate overbooking factorsϕ(t).They furthermore showed that the EBAC mechanismis robust against traffic variability in terms of packetsize and inter-arrival time distribution as well as againstcorrelations thereof.

B. EBAC in the Presence of Traffic Changes

In [26], we investigated the transient behavior of con-ventional EBAC after sudden traffic changes on the packetscale level of admitted traffic flows. We analyzed theEBAC response timeTR(t) and the QoS performance interms of packet losspl(t) and packet dealypd(t) (cf. Fig-ure 1) which are potentially compromised in case of trafficincreases. As EBAC partly relies on traffic measurements,it is susceptible to changes of the traffic characteristics ofadmitted flows. There are certain influencing parameterscoupled with this problem. One of them is the lengthof the EBAC memory which has been defined by itshalf-life period TH (cf. Equation (4)). We evaluated theimpact of the EBAC memory on a sudden decrease andincrease of the traffic intensity expressed by changes ofthe peak-to-mean rate ratios of the simulated traffic flows.For a changing traffic intensity, the response timeTR

required to adapt the overbooking factor to the new trafficsituation depends linearly on the half-life periodTH .For decreasing traffic intensity, the QoS of the traffic isnot at risk. For a suddenly increasing traffic intensity,however, it is compromised for a certain time spanTQ

R .The corresponding experiments used an unlimited linkbuffer and illustrate the performance of EBAC underextreme traffic conditions corresponding to a collaborativeand simultaneous QoS attack by all traffic sources.

C. Application of EBAC in a Network Scope

The performance of EBAC has intensively been studiedby simulations on a single link. A simple deploymentof EBAC in a network-wide scope is the link-by-linkapplication of the concept. However, this method requiresa lot of signaling which may lead to scalability problems.Another option is to perform admission control only atthe network border by using separate instances of EBACat all network ingress routers. This approach guaranteesscalability, but requires further investigation of the result-ing distributed network admission control (NAC) system.

A prototype applying EBAC at the network borderexists for the purely IP-based network architecture of theKING (Key components for the Internet of the Next Gen-eration) project [37], [38]. Its implementation requires thecollection, synchronization, and correlation of many dis-tributed network information about, e.g., resource reser-vations of flows admitted at the ingress routers, traffic

measurements on the links, routing and load balancingin the network. As a consequence, the network-wideadmission decisions cannot be made independently ofeach other since they have a correlated impact on thelink loads in the network.

However, if a network architecture fulfills certain re-quirements, the application of EBAC in the scope of a net-work is well feasible. The border-to-border (b2b) budget-based network admission control presented in [6] is afeasible approach which implements admission control atthe border of a network and uses directed b2b tunnelswith pre-determined capacities. For a simple deploymentof EBAC in a network, these tunnel capacities can beoverbooked by the EBAC mechanism like physical linkcapacities, i.e., the tunnels are considered as virtual links.This approach requires separate EBAC instances for allcapacity tunnels and, hence, the complexity of the prob-lem is now reduced to appropriate tunnel dimensioningand to b2b aggregate-specific traffic measurements. If thenetwork is based on, e.g. the (generalized) multiprotocollabel switching ((G)MPLS) architecture [34], [39], tun-nels can be implemented as label switched paths (LSPs)between label edge routers (LERs) and traffic can beeasily measured per tunnel. Traffic matrices determinedwith help of the label distribution protocol (LDP) [40]provide the necessary traffic measurements per LSP.

VII. C ONCLUSION

Experience-based admission control (EBAC) is a newlink admission control paradigm [24] representing a hy-brid solution between parameter-based and measurement-based admission control. In this article, we gave a generaloverview of EBAC and, in particular, presented its ex-tension towards type-specific overbooking (TSOB) whichimproves the original EBAC concept. We briefly reviewedthe EBAC system and explained the simulation design andthe traffic model used for the analyses of the performanceof EBAC with and without TSOB. We simulated EBACunder changing traffic conditions on the flow scale leveland presented the corresponding simulation results as themain contribution of this article. Finally, we summarizedfurther work on EBAC regarding its steady state behav-ior [25], its behavior in the presence of traffic changeson the packet scale level [26], and its application in anetwork-wide scope.

The extension of EBAC towards TSOB [27] yields acompound overbooking factor which considers differenttraffic types subsuming flows with similar peak-to-meanrate ratios. A major challenge regarding this extension isthe determination of type-specific reservation utilizationsrequired to calculate the compound overbooking factor. Ingeneral, the traffic cannot be measured for type-specificaggregates and, as a consquence, the type-specific reser-vation utilizations cannot be obtained directly. Therefore,we proposed two different methods to estimate them.Only one method, based on a least squares approximationof the type-specific reservation utilizations, proved to besufficiently accurate and was thus used for the further



performance investigations. For the comparison betweenconventional EBAC and EBAC with TSOB, we simulatedsudden changes on the flow scale level of the traffic mix tohave worst case scenarios and to obtain upper bounds onthe EBAC response times. To that aim, the share of flowswith highly utilized reservations, i.e. the traffic intensity,was either suddenly decreased or increased. As a result,we found that if the traffic intensity decreases, EBACwith TSOB adapts faster than conventional EBAC andleads to a significantly better resource utilization duringthe adaptation phase. If the traffic intensity increases,the advantage of EBAC with TSOB over conventionalEBAC becomes even more obvious. While EBAC withTSOB can prevent overload situations due to even extremechanges in the traffic mix, conventional EBAC has noappropriate means to avoid them.

This article provided a proof of concept for EBACwith TSOB but many technical details must be clarifiedbefore it can be deployed in practice. E.g., a reliablenetwork-wide measurement system needs to be installed,an appropriate number of different traffic types for TSOBmust be found, and applications with similar peak-to-mean rate ratios have to be identified and classified. Theseissues must certainly be solved. However, we alreadyhad a prototype of the conventional EBAC running ina real network testbed which showed the feasability toimplement the concept.

ACKNOWLEDGMENT

We thank Prof. Tran-Gia for the stimulating environ-ment which was a prerequisite for this work. We alsothank the unknown reviewers for their detailed comments.

REFERENCES

[1] R. Martin, M. Menth, and J. Charzinski, “Comparison ofLink-by-Link Admission Control and Capacity Overpro-visioning,” in Proc. of International Teletraffic Congress(ITC), August 2005.

[2] R. Martin, M. Menth, and J. Charzinski, “Comparisonof Border-to-Border Budget Based Network AdmissionControl and Capacity Overprovisioning,” inProc. of IFIP-TC6 Networking Conference (NETWORKING), May 2005,pp. 1056–1068.

[3] R. Braden et al., “RFC 2205: Resource ReSerVationProtocol (RSVP) – Version 1 Functional Specification,”September 1997.

[4] R. Yavatkar, D. Pendarakis, and R. Guerin, “RFC 2753: AFramework for Policy-based Admission Control,” January2000.

[5] R. Yavatkar, D. Hoffman, Y. Bernet, F. Baker, andM. Speer, “RFC 2814: SBM (Subnet Bandwidth Manager):A Protocol for RSVP-based Admission Control over IEEE802-style networks,” May 2000.

[6] M. Menth, “Efficient Admission Control and Routing forResilient Communication Networks,” PhD thesis, Univer-sity of Wurzburg, July 2004.

[7] S. Shenker, C. Partridge, and R. Guerin, “RFC 2212: Spec-ification of Guaranteed Quality of Service,” September1997.

[8] J. L. Boudec, “Application of Network Calculus to Guaran-teed Service Networks,”IEEE Network Magazine, vol. 44,no. 3, May 1998.

[9] M. Fidler and V. Sander, “A Parameter Based AdmissionControl for Differentiated Services Networks,”ComputerNetworks, vol. 44, no. 4, pp. 463–479, 2004.

[10] J. Roberts, U. Mocci, and J. Virtamo,Broadband NetworkTeletraffic - Final Report of Action COST 242. Springer,1996.

[11] J. Wroclawski, “RFC 2211: Specification of theControlled-Load Network Element Service,” September1997.

[12] M. Grossglauser and D. Tse, “A Time-Scale Decomposi-tion Approach to Measurement-Based Admission Control,”IEEE/ACM Transactions on Networking, vol. 11, no. 4, pp.550–563, August 2003.

[13] S. Jamin, S. Shenker, and P. Danzig, “Comparison ofMeasurement-Based Call Admission Control Algorithmsfor Controlled-Load Service,” inProc. of IEEE Conferenceon Computer Communications (INFOCOM), March 1997,pp. 973–980.

[14] Z. Turanyi, A. Veres, and A. Olah, “A Family ofMeasurement-Based Admission Control Algorithms,” inProc. of IFIP Conference on Performance of Informationand Communication Systems, May 1998.

[15] L. Breslau, S. Jamin, and S. Shenker, “Comments on thePerformance of Measurement-Based Admission ControlAlgorithms,” in Proc. of IEEE Conference on ComputerCommunications (INFOCOM), March 2000, pp. 1233–1242.

[16] K. Shiomoto, N. Yamanaka, and T. Takahashi, “Overviewof Measurement-Based Connection Admission ControlMethods in ATM Networks,”IEEE Communications Sur-veys & Tutorials, vol. 2, no. 1, pp. 2–13, January 1999.

[17] H. van den Berg and M. Mandjes, “Admission Controlin Integrated Networks: Overview and Evaluation,” inProc. of International Conference on TelecommunicationSystems, Modeling and Analysis (ICTSM), 2000, pp. 132–151.

[18] J. Qiu and E. Knightly, “Measurement-Based AdmissionControl with Aggregate Traffic Envelopes,”IEEE/ACMTransactions on Networking, vol. 9, no. 2, pp. 199–210,April 2001.

[19] S. Georgoulas, P. Trimintzios, and G. Pavlou, “JointMeasurement- and Traffic Descriptor-based AdmissionControl at Real-Time Traffic Aggregation Points,” inProc.of IEEE International Conference on Communications(ICC), June 2004.

[20] S. Jamin, P. Danzig, S. J. Shenker, and L. Zhang,“Measurement-Based Admission Control Algorithms forControlled-Load Services Packet Networks,” inProc. ofACM SIGCOMM Symposium on Communications Archi-tectures & Protocols, 1995.

[21] R. Gibbens and F. Kelly, “Measurement-Based ConnectionAdmission Control,” inProc. of International TeletrafficCongress (ITC), June 1997.

[22] T. Lee, M. Zukerman, and R. Addie, “Admission ControlSchemes for Bursty Multimedia Traffic,” inProc. of IEEEConference on Computer Communications (INFOCOM),April 2001, pp. 478–487.

[23] M. Dabrowski and F. Strohmeier, “Measurement-BasedAdmission Control in AQUILA Network and Improve-ments by Passive Measurements,” inProc. of Architecturesfor Quality of Service in the Internet (Art-QoS), March2003.

[24] J. Milbrandt, M. Menth, and S. Oechsner, “EBAC - ASimple Admission Control Mechanism,” inProc. of IEEEInternational Conference on Network Protocols (ICNP),October 2004.

[25] M. Menth, J. Milbrandt, and S. Oechsner, “ExperienceBased Admission Control (EBAC),” inProc. of IEEESymposium on Computers and Communications (ISCC),June 2004.



[26] J. Milbrandt, M. Menth, and J. Junker, “Performanceof Experience-Based Admission Control in the Presenceof Traffic Changes,” inProc. of IFIP-TC6 NetworkingConference (NETWORKING), May 2006.

[27] J. Milbrandt, M. Menth, and J. Junker, “Experience-BasedAdmission Control with Type-Specific Overbooking,” inProc. of IEEE International Workshop on IP Operationsand Management (IPOM), October 2006.

[28] M. Menth, J. Milbrandt, and J. Junker, “Time-Exponentially Weighted Moving Histograms (TEWMH)for Self-Adaptive Systems,” inProc. of IEEE GlobalTelecommunications Conference (GLOBECOM), Novem-ber 2006.

[29] R. Martin and M. Menth, “Improving the Timelinessof Rate Measurements,” inProc. of GI/ITG Conferenceon Measuring, Modelling and Evaluation of Computerand Communication Systems (MMB) together with Polish-German Teletraffic Symposium (PGTS), September 2004.

[30] F. Hubner and P. Tran-Gia, “An Analysis of Multi-ServiceSystems with Trunk Reservation Mechanisms,” Universityof Wurzburg, Institute of Computer Science, TechnicalReport No. 40, April 1992.

[31] A. M. Law and W. D. Kelton,Simulation Modeling andAnalysis. McGraw-Hill, 2000.

[32] V. Paxson and S. Floyd, “Wide-Area Traffic: The Failureof Poisson Modeling,”IEEE/ACM Transactions on Net-working, vol. 3, no. 3, pp. 226–244, June 1995.

[33] S. Blakeet al., “RFC 2475: An Architecture for Differen-tiated Services,” December 1998.

[34] E. Rosen, A. Viswanathan, and R. Callon, “RFC 3031:Multiprotocol Label Switching Architecture,” January2001.

[35] I. N. Bronsteinet al., Taschenbuch der Mathematik. Ver-lag Harri Deutsch, September 2000, pp. 281–288.

[36] A. Bjorck, Numerical Methods for Least Squares Prob-lems. Society for Industrial & Applied Mathematics(SIAM), April 1996.

[37] N. Heldt, “Re-Engineering the Internet Gently,”I&CWorld, vol. 12, December 2002.

[38] C. Hoogendoorn, K. Schrodi, M. Huber, C. Winkler, andJ. Charzinski, “Towards Carrier-Grade Next GenerationNetworks,” in Proc. of International Conference on Com-munication Technology (ICCT), April 2003.

[39] E. Mannie, “RFC 3945: Generalized Multi-Protocol LabelSwitching (GMPLS) Architecture,” October 2004.

[40] S. Schnitter and M. Horneffer, “Traffic Matrices for MPLSNetworks with LDP Traffic Statistics,” inProc. of Interna-tional Telecommunication Network Strategy and PlanningSymposium (Networks), June 2004, pp. 231–236.

Jens Milbrandt studied computer science and economics inWurzburg / Germany and received his diploma degree in 2001.He finished his Ph.D. on performance evaluation and resourcemanagement in next generation networks at the University ofWurzburg (Institute of Computer Science, Chair of DistributedSystems) in 2006.

He now works as a scientist and development engineer intelecommunications for Alacatel-Lucent in Stuttgart/Germanyand is concerned with network architecture and control planeissues in future transport networks.

Michael Menth studied computer science and mathematics atthe Universities of Wurzburg / Germany and Austin / Texas. Hehas been working as a researcher at the Universities of Ulm /Germany and Wurzburg where he is currently assistant professorand heading the research group on next generation networks.His special interests are performance analysis, optimization ofcommunication networks, resource management, and resilienceissues. Dr. Menth holds about 25 patent applications and hasreceived various scientific awards for innovative work.

Jan Junker studied computer science at the University ofWurzburg (Institute of Computer Science) in Germany. In hisdiploma thesis, he dealt with performance evaluation and qualityof service of IP networks.

After having received his diploma in computer science,he now works as consultant in the department for researchand development of the PASS Consulting Group. There hefocuses on model-driven software development, service-orientedarchitecture and software engineering automation based on codegeneration.



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