Measuring the edge-to-edge available bandwidth in a DiffServ domain

Received 7 December 2006Revised 16 April 2007

Copyright © 2007 John Wiley & Sons, Ltd. Accepted 24 April 2007

Measuring the edge-to-edge available bandwidth

in a DiffServ domain

N. Blefari-Melazzi1 and M. Femminella*,†,2

1DIE, University of Rome ‘Tor Vergata’, Italy2DIEI, University of Perugia, Italy

SUMMARY

The new Internet will be deployed with a number of tools for network management and quality of service control. Tothis end, we focus on a single administrative domain based on the Differentiated Services architectural model, and werecognize the need for two main functions for each supported traffic class: an admission control procedure, and a monitoring of the edge-to-edge bandwidth availability. In this work, we specifically focus on the second issue. To pre-serve scalability and thus to be compliant with Differentiated Services architecture, we propose stateless and distrib-uted procedures based on traffic measurements. Our technique tests network resources by means of ‘special’ probingpackets, which have the task of implicitly conveying the network status to its edges. We show by means of simulationsthe effectiveness of our solutions, in spite of a very low overhead. Copyright © 2007 John Wiley & Sons, Ltd.

1. INTRODUCTION

The ever-evolving protocols of the Internet have been enriched by the Differentiated Services (DiffServ)paradigm [1,2]. This aims at providing differentiated levels of quality of service (QoS) by applying dif-ferent per-hop forwarding behaviors (PHB) to different traffic types. A PHB describes the packet han-dling strategy set adopted in each network element of the same domain. This implies introducing areduced number of traffic classes (mapped onto PHBs), where the traffic streams within each traffic classare considered homogeneous and each traffic class is treated separately from the other traffic classes.Traffic is classified at the network border by edge routers (ERs), which mark packets by assigning a valueto the DiffServ Code Point field (DSCP, which is a 6-bit field in the IP header) [3], and perform policingand shaping operations. Having assigned to the ERs a number of tasks, the DiffServ model leaves thecomplexity at the network edges, requiring core routers (CRs) to perform only aggregate classification(based on the DSCP) and to apply the consequent PHB. With such an approach, CRs do not have toimplement complicated tasks such as active packets management (e.g., parsing and remarking opera-tions), and their duty is to forward packets at the highest possible speed [4].

In this framework, domain administrators aiming at providing their customers with enhanced trans-port services have to define service profiles and negotiate them with suitable entities, to reach a Service-Level Agreement (SLA). An SLA characterizes the service performance in deterministic or statistical termsby means of proper descriptors. Each SLA is composed of contractual aspects and one or more Service-Level Specifications (SLSs), which represent the ‘technical’ part of the SLA, defining the service perfor-mance characterization and the offered traffic profile the SLA has to be applied to.

In DiffServ, SLAs and SLSs do not offer any indication on how to provide the guaranteed performance,and the SLSs map into network layer requirements to build up the so-called Per-Domain Behaviors

INTERNATIONAL JOURNAL OF NETWORK MANAGEMENTInt. J. Network Mgmt 2008; 18: 409–426Published online 31 May 2007 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/nem.660

*Correspondence to: M. Femminella DIEI, University of Perugia, Via G. Duranti 93, I06125 Perugia, Italy.†E-mail: [email protected]

(PDBs). A PDB defines quantifiable and measurable parameters that can be adopted to describe the per-ceived performance of the transport service form edge to edge of a single domain. More formally, a PDBis ‘the expected treatment that an identifiable or target group of packets will receive from “edge-to-edge”of a DiffServ domain’ [5]. Thus, we may reformulate the above sentence saying that a PDB specifies thetreatment that a set of packets with a particular (set of) DSCP(s) will receive as they cross a DiffServdomain. Such a treatment derives from [5]: (i) the characteristics of the traffic aggregate that results fromthe classification and traffic conditioning functions at the ingress of the DiffServ domain; (ii) the ingresstraffic loads and domain’s topology; and (iii) the forwarding treatment the packets get inside each entityof the domain (PHB).

Each domain can define and adopt its own PDB set. Thus, if multiple domains are crossed by a givendata flow, the related end-to-end QoS level can be obtained by combining the PDBs of the domainscrossed by the data flow. Figure 1 shows an example of interconnected domains and the scope of theirPDBs. In order to compose the end-to-end QoS signaling, the new protocol suite [6] designed by the IETFNext Steps In Signaling (NSIS) Working Group could be used.

In this paper we present two functions, which, we believe, are important building blocks of a PDB sup-porting QoS-aware services. The first one is a function that allows advertising the domain’s edge-to-edgebandwidth availability to transport a given traffic class to neighboring domains and/or to the entities incharge of coordinating the information transfer over end-to-end paths, whereas the second function isadmission control (AC).

Knowledge concerning resource availability (available bandwidth estimation, ABE) may allow, facili-tate, or improve tasks at the domain level such as:

• definition and deployment of SLA with both users and peer service/network providers, includingthe setup of VPNs with guaranteed bandwidth;

• deployment of adaptive pricing policies for transport services based on the amount of availableresources (demand/offer criterion);

• tuning of the resource partitioning among the different traffic classes on the basis of the observedper-class measurement (e.g., the tuning of the scheduler weights associated to different trafficclasses);

• update of the routing table of overlay networks;• planning of capacity upgrades in critical sections of the domain.

From all the above considerations, it should be clear that this kind of knowledge typically refers to aggre-gate quantities and to large time scales [4]. In other words, this function can be used to communicatemacro-information on all-comprehensive metrics and, in essence, it is unable to follow quick variationsof traffic loads and related performance. Consequently, by itself, this first function (ABE) is not useful toguarantee predefined performance to single flows when they cross the considered domain. To this end,it is necessary to deploy the second function, the AC [7,8], since the basic DiffServ method of operationdoes not intrinsically solve the problem of controlling congestion. In fact, upon overload in a given serviceclass, all flows in that class suffer a potentially harsh degradation of service [4].

The goal of this paper is to perform both these functions (ABE and AC) in a stateless way, by meansof distributed, edge-to-edge measurements through probe packets. It is worth noting that, although theABE function is not necessary to implement the AC one, and vice versa, under the assumption that bothof them are useful, we think it expedient to adopt a common approach able to fulfill the above require-ments (distributed, measurement-based, minimum involvement of CRs, and low overhead) to cope withthem. The proposed measurement framework is general, and can be easily adapted to any kind of trafficfor which the SLS specifies policing and/or shaping at the ingress node, including TCP-based flows.1

410 N. BLEFARI-MELAZZI AND M. FEMMINELLA

Copyright © 2007 John Wiley & Sons, Ltd. Int. J. Network Mgmt 2008; 18: 409–426DOI: 10.1002/nem

1Clearly, the notion of available bandwidth does not apply in the case of greedy, non-regulated traffic since, in that case, independent of thenumber of flows sharing the resources over a path, the available bandwidth will always be next to zero on the bottleneck link, and thus it willbe next to zero over the whole path.

We point out that the focus of this paper is the ABE function, since the AC mechanism we refer to hasbeen already presented [9] and further elaborated [10]. In fact, the AC here considered is named GRIP,which stands for Gauge and Gate Reservation with Independent Probing. Since it represents the back-ground of this work, and to ease the reader’s effort, we recall the basic concepts of GRIP in order tounderstand how these two functions (ABE and AC) may exploit the same implicit probing mechanism.

The paper is organized as follow. We present the AC procedure together with previous studies aboutbandwidth estimation in Section 2, whereas the ABE procedure is presented in detail in Section 3. InSection 4 we show selected numerical results, and we draw our conclusions in Section 5.

2. RELATED STUDIES

2.1 The admission control function: GRIP

The main idea of GRIP is to convey to the network edges the actual occupancy status of the inner networkby means of stateless and distributed procedures [9]. This is allowed by the intrinsic capabilities of Diff-Serv routers to differentiate the traffic by means of DSCPs, and allow carrying out both ABE and ACphases by reusing the same basic mechanism. Such a procedure, based on traffic measurements, is seman-tically compliant with the standard Assured Forwarding (AF) PHB [11], as shown in Bianchi and Blefari-Melazzi [12]. In fact, an AF PHB implements three service levels mapped onto three values of DSCP. Thedropping policies applied to AF traffic depends on both congestion level (estimated via traffic measure-ments) and the DSCP value.

The GRIP mechanism combines an admission control operation, driven by ERs, with run-time trafficmeasurements performed within CRs to detect congestion. It is possible to distinguish the functionali-ties performed by ERs from those performed by CRs.

GRIP’s edge router operationWhen a user terminal requests a connection with a destination terminal, the relevant ER, acting as a proxyfor it, transmits a probing packet towards the destination. Meanwhile, the ER activates a timer, lastingfor a reasonably short time. If no response returns from the destination ER before the timeout, the sourceER rejects the connection setup attempt. Otherwise, if a feedback packet is received in time, the flow is

EDGE-TO-EDGE AVAILABLE BANDWIDTH IN A DIFFSERV DOMAIN 411


Figure 1. Architecture of the communication infrastructure

accepted and control is given back to the user application, which starts a data phase, consisting in thetransmission of information packets. To maintain full compatibility with the (stateless) DiffServ archi-tecture, probing packets are not meant to carry explicit signaling information, and are labeled with a dif-ferent DSCP with respect to information packets. The different DSCPs assigned to probing andinformation packets allow CRs to apply different forwarding behaviors to them and enforce probe packetdropping (thus blocking the setup attempt) when congestion arises. The role of the destination ER simplyconsists in monitoring the incoming IP packets, intercepting the ones labeled as probing, reading theirsource address, and, for each incoming probing packet, just relaying with the transmission of a feedbackpacket if the destination terminal wants to accept the connections. The specification of a protocol tocontact the ER from a terminal and vice versa is beyond the scope of this paper. For instance, in the caseof a video server, an application protocol could test the server availability.

GRIP’s core router operationPackets incoming to a DiffServ CR output port are dispatched to the relevant queues according to theirDSCPs. Within the router, a GRIP module is in charge of handling both probing and information packets.Within the GRIP module, a measurement module is in charge of measuring the load offered by infor-mation packets, i.e., the overall aggregate accepted traffic. On the basis of these running traffic mea-surements, and according to a suitable decision criterion, the measurement module drives anaccept/reject switch. When the switch is in the ACCEPT state, incoming probing packets are forwardedto the output queue. Conversely, probing packets are dropped when the switch is in the REJECT state.In other words, the router acts as a gate for the probing flow, where the gate is opened or closed on thebasis of the traffic estimates. Note that several GRIP modules, devised to support different traffic classeswith different QoS requirements, may coexist (see Blefari-Melazzi and Femminella [10]).

The operation presented above, in conjunction with the ERs operation described before, provides aper-flow admission control function over a stateless network, via an implicit signaling pipe of which thenetwork remains unaware (it involves only data plane operations). In fact, a call setup is accepted onlyif the probe finds all the CRs along the path in the ACCEPT state.2

GRIP performance and measurement issuesThe performance of GRIP is clearly related to the capability of routers to take decisions locally about theirdegree of congestion. A number of algorithms based on measurements have been proposed in the liter-ature (e.g., see Breslau et al. [13]); however, even if they reach high levels of resources utilization, theymiss controlling overload correctly. Consequently, in order to provide strict QoS guarantees, in Bianchiet al. [9] we defined a novel scheme, able to fulfill this requirement. To this aim, information about QoStraffic flows characteristics is needed. We make the common assumption that standard Dual LeakyBuckets (DLB) regulates traffic sources at ERs. The DLB is a deterministic, credit-based regulator able tocontrol the peak data rate and the average data rate with the relevant tolerance by means of three para-meters: peak rate, PS (in bit/s), sustainable rate, rS (in bit/s), and the token burst size, BTS (in bytes) [14].The localized decision criterion running on each router’s output link is based on the run-time estimationof the number of active sources [9,10]. We considered a threshold on the maximum number of admissi-ble flows, K (evaluated in Elwalid et al. [14]), which acts as a ‘tuning knob’, allowing the domain opera-tor to set target performance levels: GRIP only enforces the number of admitted flows to respect such athreshold value. The estimation of the traffic load in terms of number of active flows at time t on theinterface of router r toward router s has been proposed [9] as Nest, r→s(t), with

(1)N tA t T tr T B

t T ttest,rs TS

New→ ( ) = −( )−( )

+ −( ),,



2Otherwise it gets discarded; hence it does not reach the destination, no feedback packet will be relayed back, and the call will be blocked as soonas the probing phase timer expires.

where A(t − T,t) represents the amount of traffic measured in the last T seconds by the measurementmodule on the considered interface, whereas New(t − T,t) is a term used to protect the system in tran-sient situations (more details can be found in Bianchi et al. [9]).

In Blefari-Melazzi and Femminella [10], we have shown that the performance of such a measurementprocess is, on average, affected by a systematic error due to an overestimation factor of the number ofactive sources. To improve the AC efficiency (thus providing ‘statistical’ and not absolute QoS guaran-tees) and to use this estimation also to measure available bandwidth coherently with AC and withoutsuch an error, in this paper we propose to pre-correct the estimation rule by means of such a factor, thusobtaining for the estimation of the number of active calls on a link (Nest,new,r→s(t)) the expression

(2)

where 1/m is the (estimated or a priori known) average call duration. The router remains in the ACCEPTstate until Nest,new < K. The estimation of the average call duration is possible only for specific traffic classes,such as those devised to transport voice traffic or pre-recorded videos.

2.2 Previous studies on available bandwidth estimation

In the literature there are a number of algorithms able to measure the available bandwidth. In this paperwe refer to the very interesting survey [15] and references therein. That study considers for estimatingbandwidth availability two main mechanisms: Self-Loading Periodic Streams (SLoPS) and Trains ofPacket Pairs (TOPP). These schemes make the assumption, used also in this paper, that the availablebandwidth remains nearly constant if averaged over time (i.e., stationary traffic load over the consideredpath [15]), at least for the interval needed for the measurement. However, this does not imply absenceof traffic variability.

SLoPS attempts to find the available bandwidth by using streams of equal-sized probing packets. Thesestreams are sent at different rates employing a binary search method. The value of the available band-width is estimated by investigating variations in the one-way delay of probes.

TOPP uses a packet pair technique: a number of packet pairs are sent at increasing rate in a linearfashion. The available bandwidth is estimated by observing the rate at the receiver side.

The main difference between SLoPS and TOPP is in the statistical processing of measurements. Fromthe analysis of these solutions, we may observe that:

• the quantity they are able to estimate is the overall available bandwidth over the considered path,and it neither takes into account the quantity relevant to each specific traffic class, nor is it associ-ated to a value (the effective bandwidth [14]) able to provide QoS guarantees;

• the overhead introduced is far from being limited, since both SLoPS and TOPP use a large numberof packets to accomplish a single measurement, with instantaneous bandwidth consumption closeto the quantity to estimate;

• the time necessary to carry out a single measurement could be relevant, since a large number of iterations are needed.

In conclusion, these schemes need a large overhead and long times, since a procedure purely based onendpoint measurements suffers from performance drawbacks mostly related to the necessarily limitedmeasurement time spent at the destination. Measurements taken over such a short time, and on an end-to-end basis, cannot capture stationary network states, and thus the estimation of available resources istaken over a snapshot of the network status, which can be quite an unrealistic picture of the networkcongestion level.

Thus, even if distributed and carried out at the network edges, none of the literature approaches seemto satisfy the other main requirements of this paper: limited overhead and the capability to estimatevalues of bandwidth associated to QoS classes. In the following section, we present our proposal, whichis able to fulfill these requirements.

N t N tT

T B rT

t test,r est,rTS s

→ →( ) = ( ) ⋅−( )

+

m2



3. AVAILABLE BANDWIDTH ESTIMATION

In this section we propose a novel technique to measure the bandwidth availability between ERs, com-pliant with the DiffServ paradigm. This implies avoiding the possibility that CR could parse, remark or,more generally, actively manage probing packets (such as write on them the measured value of band-width). To overcome the limitations of approaches described in Section 2.2, we apply the same basic con-cepts of the above probing scheme to convey the domain’s occupancy status at the edges of the domain,in which the estimation of network load is performed over a longer timescale by inner nodes. However,we note that in the framework of AC the information to be conveyed is binary and simply states whethera given flow can be admitted or not (ACCEPT/REJECT). Now, we want to measure and then to adver-tise aggregate, edge-to-edge bandwidth availability to build up a PDB with detailed information aboutthe edge-to-edge service. Thus, in order to monitor the network status, we adopt a repeated probe mech-anism, as briefly sketched in Di Sorte et al. [16] and explained in detail in Section 3.1 below.

It is worth noting that these advanced functions (AC and ABE), even if deployable for each type oftraffic, are mostly suitable for traffic classes requiring specific, quantifiable levels of QoS, such as IP tele-phony or video on demand. In this regard, we point out that the definition of available bandwidth usedin this work is strictly coupled with QoS guarantees. Specifically, consider two edge routers betweenwhich the traffic of a specific class (e.g., IP telephony) is transported with predefined QoS guarantees interms of maximum delay and packet losses. According to the DiffServ paradigm, the links making upthe edge-to-edge path will be (likely statically) provisioned with aggregate resources for that traffic class,through the proper setting of node schedulers. Thus, on the overall considered edge-to-edge path, thereis an aggregate minimum bandwidth to transport the data of a specific traffic class. We define as avail-able bandwidth the minimum spare amount of that capacity able to transport data, from edge to edge,with the QoS guarantees specified for the considered traffic class, at a given time. However, from the def-inition above, the available bandwidth we refer to is the so-called effective bandwidth [1,14], whose valuetypically differs from peak and average bandwidths, which are the network parameters more commonlyused for network management purposes. Even if effective bandwidth is generally relevant to a specifictraffic flow [14], here we treat it as an aggregate value.

To be clearer, let us fix a traffic class and consider the example topology depicted in Figure 2, whichshows six nodes (four edge and two core routers), connected through bidirectional links with capacityequal to the values Clocal,i↔j,3 reported in the table on the lower side of the figure. The table on the upper



Figure 2. Example topology with traffic matrix and available bandwidth, for a specific traffic class

3This value could not represent the link capacity, but only the fraction of the capacity devoted to transport the traffic relevant to the class underconsideration.

side of the figure reports the capacity requirements (in terms of effective bandwidth) of two aggregate,unidirectional traffic flows: one from ER0 to ER3 (black), and one from ER2 to ER1 (grey). The table on theleft summarizes the available capacity from edge to edge (Rava,i→j). The goal of the ABE function is to esti-mate this quantity. It takes into account the minimum amount of provisioned edge-to-edge resources(always equal to 4Mbit/s for the topology in the figure), the amount of resources occupied by the twotraffic flows (in terms of effective bandwidth, as explained above), and flow directions.

3.1 Edge-to-edge estimation algorithm

The proposed approach can be seen as an extension of the AF PHB [11], where the number of DSCPsused for each class is increased from 3 to 2n + 1, where n is the number of bits of the DiffServ field ded-icated to perform ABE. These 2n + 1 possible configurations of the DSCP field are used as follows:

• a first configuration of the DSCP is used to label data traffic;• another configuration is used for probing in the AC procedure;• the remaining (2n − 1) configurations are used to measure the capacity available over a network path

by means of repeated and ever more precise approximations through probes (named DiscoveryProbe Packets, DPPs).

Since we employ a binary search procedure, the number of these trials is fixed and equal to n, each attemptbeing able to determine the value of one bit in the considered DSCP sub-field (of size n). In particular,to determine the lth bit of the selected set, we use the lth DPP. We rely on the fact that within each outputport of each router crossed by the path there is a measurement module that selectively forwards or dropspackets, on the basis of the performed run-time measurements and of the DSCP value. In more detail,we map the values indicated by a sub-field of the DSCP (e.g., three of the six available bits) onto prede-fined levels of available capacity on a given output port, for a specific traffic class.

Such a mapping must be the same for each router in the domain, in order to maintain coherence in theinformation conveyed at the edges of the domain: in fact, the DPPs have significance over an entire path,and thus the value that they represent has to be the same, independent of the link currently under test.This task can be easily accomplished, since an administrative domain is under the control of a singleoperator. Thus it is possible to define a reference capacity for a specific traffic class, C: the implicit infor-mation carried by the DPPs represents fractions of C.

The available capacity is measured on each link on the basis of the used capacity for a traffic class,denoted Rmeas,r→s(t) for the link r→s at time t. For each router involved in the edge-to-edge measurementprocess, this measured capacity represents an estimation of the consumed capacity by the consideredtraffic class on the considered output port. The estimated value of available bandwidth on the single linkr→s at time t is equal to

(3)

where Clocal is the amount of aggregate link capacity provisioned for such a class of service. Since thisbandwidth has to be able to ensure performance, we evaluate the amount of occupied resources throughthe (estimated) number of admitted flows with QoS guarantees (Nest,new). In fact, according to the valueNest,new, it is straightforward to obtain the value of the measured capacity, expressed as a fraction of Clocal.Such a fraction represents the ratio between the number of (estimated) admitted flows and the maximumadmissible number K on the capacity of that link (Clocal), previously defined:

(4)

As explained in the comment regarding Figure 2, the edge-to-edge available bandwidth will result as afraction of the minimum of the values of Clocal for all the routers on the path.

The overall idea of the edge-to-edge mechanism is very simple: when ERi wants to establish a mea-surement session at time t, it starts sending a sequence of n DPPs towards the selected destination ERj.If ERj receives a DPP, it replies with a feedback packet. The reception of this latter packet implies that the

R t C N t Kr s r s r s r smeas local est, new, , ,→ → → →( ) = ( )

R t C R tr s r s r sava local meas, , ,max ,→ → →( ) = − ( )( )0



available capacity over path i→j is greater than the value associated to that DPP, whereas the lack of feed-back (signaled by a time-out event) implies that the (estimated) available capacity over that path is lessthan such a value. We assume that lack of feedback occurs only when DPPs are discarded, since feed-back packets should be marked as information packets and thus should not be dropped easily as DPPs.

When the procedure begins, the first bit of the considered sub-field of DSCP is provisionally set to 1,whereas the others are set to 0. For instance, let us assume a value of n = 3. At the beginning of an ABEphase (time t), the involved ERi sends the first DPP with binary code Xi→j(t) = 100|2. Upon this packetbeing sent, a suitable timer is started; if the relevant feedback packet is received from the involved peerERj before the time-out (∆, evaluated on the basis of an estimation of the maximum round trip time, RTT),then the first bit is definitively set to 1, and otherwise 0. The same procedure is then repeated for theother n − 1 bits of the ABE field each ∆ seconds. Each router crossed by a DPP with a given code willforward such a packet only if the estimated value of the local, available capacity for such a class of serviceis greater than the capacity corresponding to the code carried out by the DPP in the DSCP: the code ofthe lth DPP (sent at time t + (l − 1)∆) will be Xi→j(t + (l − 1)∆); thus the implicit capacity information con-veyed will be R(Xi→j(t + (l − 1)∆)). This implies that the condition for a CR p to forward the lth DPP tothe downstream router q at the time it is received (i.e., t + (l − 1)∆ + t, including an additional delay t <∆ needed by the DPP to reach the CR p) is equal to

(5)

A DPP arriving at its destination means that all crossed routers have, for the considered service and forthe involved interfaces, a minimum amount of available capacity equal to R(Xi→j(t + (l − 1)∆)). Thus, thisprocedure generates, after n steps (i.e., n∆ seconds), a binary number, X1

i→j(t) = Xi→j(t + n∆), which repre-sents the minimum amount of available capacity along the considered path and approximates the actualvalue, Rava,i→j(t + n∆). The apex represents the number of ABE measurement phases (equal to 1 in thiscase) executed starting from a reference time t. Thus, the exported measurement information for the pathERi→ERj after the kth ABE phase, denoted as, R̂k

ava,i→j(t) will be equal to

(6)

Note that this measurement holds for the time interval [t + k(n∆),t + (k + 1)(n∆)]. From now on, withoutany loss of generality, we assume t = 0 and avoid explicitly reporting it in the following formulas.

Obviously, the greater the number of bits n, the more accurate is the estimation of the available capac-ity, the lower is the number of possible service classes (the DSCP is 6-bit, even if the other two bits cur-rently left unused can be adopted for performing domain-specific operations), and the greater is the timeneeded to complete the procedure. It is worth noting that, adopting a linear mapping between measuredcapacity and DSCP codes, we get, for instance, 3/4C for a value of Xk equal to 110|2. Figure 3 shows anexample of the coding with n = 3.

Finally, under the assumption of stationary traffic during a number of measurement iterations, it couldbe really convenient to adopt a non-linear coding for the ABE intervals. In fact, in steady-state networkconditions, it is likely that most estimations tend to concentrate in a capacity range lower than C, espe-cially if the network is nearly symmetrically configured and loaded. Thus, it could be convenient to adopta code scheme able to exploit this trend, to improve estimation precision. A possible strategy is outlinedbelow.

We assume that ERi stores the values of M consecutive measurements towards all the egress ERs, R̂kava,i→j,

to estimate the average mean value, mi, evaluated as

(7)

where the term is the time average, over M consecutive measurements starting from a referencetime, of the estimated, average available bandwidth on the path i→j. Since mi represents the overall mean,estimated, available bandwidth on all the paths originating from ERi towards all the other destination

( ˆ ),Rava i j→

mN M

RN

Ri i jk

k

M

j j i

N

i jj j i

N

=−

⋅

=−→

== ≠→

= ≠∑∑ ∑1

11 1

111 1

ˆ ˆ,

,,

,ava ava

ˆ,R t R X t R X t k ni j

ki jk

i java → → →( ) = ( )( ) = + ( )( )( )∆

R t l R X t lp q i java , → →+ −( ) +( ) ≥ + −( )( )( )1 1∆ ∆t



ERs, in order to evaluate mi the value has to be further averaged over all the N − 1 outgoingpaths from ERi, N being the number of ERs. Analogously to mi evaluated in (7), the average standarddeviation for the estimated available bandwidth for the source ERi, si, is evaluated as

(8)

where N is the number of edge nodes.Then, each M ABE phases, all ERs communicate each other these two values (mi and si), and

each ER is able to perform a further average operation over them, obtaining the global valuesand . At this stage, since each ER knows these quantities, it

is possible to perform a more effective mapping. This process is repeated every M disjoint measurementcycles. The proposed solution is to adopt as R(X) a polynomial of degree 3, obtained through Lagrangeinterpolation using the four extreme values of the DSCP subset (e.g., {000, 001, 110, 111} for n = 3) asabscissa samples and the four values {0, mGLOBAL − sGLOBAL, mGLOBAL + sGLOBAL, C} as the respective ordi-nates. In this way, we relax the estimation precision at the borders of the available bandwidth range andincrease it in the interval [mGLOBAL − sGLOBAL, mGLOBAL + sGLOBAL], where the ordinates corresponding to themiddle values of X (e.g., {001, 010, 011, 100, 101, 110} for n = 3) are concentrated. This scheme is clearlymore effective when the value of sGLOBAL decreases, since it is able to concentrate a larger number ofsamples in a restricted range. As an example, Figure 4 shows the case of non-linear coding of R(X) withLagrange interpolation, starting from the input values C = 4Mbit/s, mGLOBAL = 2.6Mbit/s, and sGLOBAL =1Mbit/s. It is evident that the non-linear coding allows concentration in a range of 2Mbit/s six of eightsamples, thus increasing the estimation precision in the interval where it is more likely to find the currentvalue of available bandwidth.

Nevertheless, the non-linear coding scheme has the drawback of requiring additional signaling toexchange measurement values among ERs and to set the new configuration in CRs. We evaluate the effec-tiveness of this implementation option, in terms of both estimation precision and overhead, in the nextsection. A preliminary comment is that, if the traffic is strongly asymmetric (thus resulting in very highvalues of sGLOBAL), the repeated average operations could limit its effectiveness and the base, linear codingprocedure could provide the same (or even better) performance in terms of estimation precision.

s sGLOBAL = ( )=∑ iN

i N1m m NiN

iGLOBAL = ( )=∑ 1

s i i jk

i jk

M

j j i

N

N MR R=

− −−( )→ →

== ≠∑∑1

11

1

2

11

ˆ ˆ, ,

,ava ava

( ˆ ),Rava i j→



Figure 3. Probing mechanism for performing ABE

However, if the value sGLOBAL should be very high (e.g., comparable with C/2), the non-linear codingoption could be automatically turned off by ERs to switch to the base one.

3.2 Additional considerations and implementation options

A number of additional considerations/concerns apply to the above presented scheme, concerning minordetails or implementation options/issues.

The first is that the algorithm presented in Section 3.1 is always conservative. In fact, referring to Figure3, the value of bandwidth advertised externally (R(X)) results in the output of a quantization process andis set equal to the inferior extreme of the identified quantization interval. The resulting effect is an under-estimation of available bandwidth and thus an increase of the estimation error, since all the values in theidentified interval are mapped on its inferior extreme (worst case). We adopted this option aware of thiseffect, keeping in mind the target of QoS guarantees: advertising always a value of bandwidth surelyavailable. However, in different scenarios, such as involving TCP streams,4 the measurement algorithmshould be different (e.g., simply Rmeas = A(t − T, t)/T), and the advertised value should be the center ofthe identified interval.

The approach presented in the previous section deals with homogeneous traffic. It is worth noting thatthis assumption does not imply that all traffic flows have the same traffic descriptors (i.e., DLB parame-ters), but only that all flows require the same QoS guarantees and thus belong to the same traffic class.For more details see Blefari-Melazzi and Femminella [10].

The measurement process results in a delay equal to about n times the RTT. In order to speed up theprocess, we can envision a modified approach. Instead of sending n probes, at intervals of ∆ seconds (i.e.,about one RTT for each one), the source ER could send directly all the possible 2n − 1 probes at the begin-ning (refer to Figure 3 for the case of n = 3: the possible values of DSCP sub-field are {001, 010, 011, 100,101, 110, 111})5 and wait for feedback packets. The value R(X) is calculated based on the received



000 001 010 011 100 101 110 1110

0.5

1

1.5

2

2.5

3

3.5

4

X (binary notation)

R(X

), M

bit/s

mGLOBAL

+σGLOBAL

mGLOBAL

−σGLOBAL

Figure 4. Non-linear coding example

4We assume also that TCP streams are regulated/shaped at the edge nodes, according to the parameters specified in the SLSs, otherwise thegreedy nature of the TCP protocol will invalidate the concept of available bandwidth measurement.5Note that a DPP with DSCP sub-field {000} is never sent.

feedback packet relevant to the DPP with the highest code. Clearly, this scheme increases exponentiallythe overhead (2n − 1 probing/feedback packets in a single RTT instead of n probing/feedback packets inn RTT). However, for low values of n, due to the very low overhead implied by the proposed procedure(see the results presented in the next section), this option is reasonable. Note that the two options couldbe also switched at run time, since only ERs have to agree on them. In addition, it could be possible toinsert an (optional) idle interval of I seconds between two consecutive ABE phases.

It is worth noting that the ABE process is independent from the AC one, since the former is repeatedperiodically, based on the operator policies, whereas the latter is on demand, repeated each time a newcall needs to be admitted. However, when the ABE procedure estimates large values of the availablecapacity between two ERs, the AC function could also be skipped. In this way, the AC procedure couldbe performed only in the case of critical situations, i.e., when the estimated value of available resourcesis very low, and the AC becomes more precise than ABE to admit new flows. Thus, the operator couldsave a large amount of AC signaling and reduce the setup delay to zero.

A possible concern about the proposed scheme is related to the routing. In fact, the treatment of thiswork assumes that, as for the GRIP protocol presented in Bianchi et al. [9], probes and data packets followthe same path, and that, for a single traffic class, there is no traffic splitting through two ore more pathsinside a DiffServ domain. In addition, a route change during an ABE phase could invalidate the currentmeasurement. The first assumption is quite reasonable. As for the possibility to engineer the transport oftraffic through multiple paths towards a single egress router, this would require additional traffic man-agement instruments, such as the implementation of DiffServ over MultiProtocol Label Switching [17,18].In this case, the edge node acting as probe source should send a number of duplicate probes—one foreach path involved in the traffic splitting and tagged with a suitable marker to assure that all duplicatesfollow different paths—and then properly aggregate the obtained results to provide a single, all-inclu-sive edge-to-edge value of bandwidth availability. Finally, as for the third issue related to routing, clearlyabsence of route changes cannot be guaranteed. However, the typical timescale of these events is muchlarger than the period of a single ABE cycle, and thus the rate of measurement phases affected by thisproblem is quite negligible.

Finally, despite all the possible advantages, the continuous monitoring of available network bandwidthimplies a number of drawbacks. First, routers have to be queried continuously and have to collect a largenumber of statistics: this could decrease the performance of high-speed CRs and thus it avoids the mon-itoring process to scale to large networks. Consequently, a requirement for a network monitoring processis to be distributed and driven by ERs, with a minimum involvement of CRs [19]. In addition, since ithas to be carried out continuously, its level of intrusiveness (i.e., overhead) has to be very limited (Prasadet al. [15] suggest an upper limit of 10% of monitored resources). Whereas the first issue is fulfilled bythe proposed architecture (only data plane operations are carried out), the numerical results presentedin the next section confirm that its level of intrusiveness is definitely much less than the threshold of 10%.

4. NUMERICAL RESULTS

In this section, we report simulation results for the ABE procedure presented above, without presentingnumerical results for the AC, since they can be found in other published papers [9,10]. We consideredthe simulation topology depicted in Figure 5, which also includes link capacities, delays and thresholdvalues (K) considered for the GRIP algorithm. It is worth noting that each link has to be considered bidi-rectional, and the capacity values reported in Figure 5 are intended to be in each direction.

We loaded the DLBs with on–off voice sources emitting at 32kbit/s, enhanced with a silence suppres-sion algorithm with an activity coefficient equal to 0.35, in order to simulate an Internet telephony sce-nario. Call holding times have an exponential distribution with mean value equal to 4min. The DLBparameters are PS = 32kbit/s, rS = 13.6kbit/s and BTS = 5.3kbytes. The value of the measurement windowin the routers is set to 30s (for a discussion on optimal settings of T and its influence on performance,refer to Bianchi et al. [9] and Blefari-Melazzi and Femminella [10]).



We considered an overall domain call arrival rate equal to l, which implies a call rate equal to l/20for each edge-to-edge path.6 The value of l is initially set to 1 call/s, then increased each 3000s by 0.5calls/s up to 8 calls/s. Thus, the load of each edge-to-edge path will vary in the interval [12–96] erlangs(see simulation setup in Figure 5).

We set the number of bits used in the estimation procedures, n, equal to 3, 4, and 5. The DPP timeoutis set to ∆ = 2.1s in order to overestimate the worst-case round trip time (equal to 5 hops for the topol-ogy in Figure 5). This value takes into account transmission, propagation and queuing delay (themaximum is set equal to 0.2s). In addition, we considered an idle interval I of 0.2s between two con-secutive ABE phases. The value of C defined in Section 3 (see also Figure 3) is set equal to 4Mbit/s.

Due to the low number of nodes and the small values of link capacities (a few Mbit/s), the proposedsimulation scenario can represent only a scaled version of the network of an Internet Service Provider(ISP). Despite the small scale, it retains all the characteristics needed to perform the desired analysis,since: (i) the topology is typical of a real ISP network (a number of edge nodes connected to a meshedcore section); (ii) each link allows the multiplexing of a large number of traffic flows; (iii) traffic sourceparameters are representative of a real service (i.e., Internet telephony); (iv) all the various explored loadconditions reach steady state, and span from low resource utilization to heavy load conditions; and (v)time parameters (maximum queuing and link delays, timer configurations, arrival/departure flow sta-tistics and simulation duration) are compliant with a realistic setting. Thus, the obtained results, althoughdependent on the underlying simulation scenario, allow extraction of general considerations about theproposed ABE mechanism.

Figure 6 shows the ability of the proposed iterative mechanism in tracking the available capacity vari-ation over the path from router ER0 to router ER4 from time 2000s to time 5000s of the simulation. Theabscissa reports the simulation time, while the ordinate shows the value of the capacity actually avail-able (labeled ‘actual value’, Rava) compared with that estimated by the proposed procedure (R(X)) for dif-ferent values of n. As expected, the higher the value of n, the higher the precision. In addition, the delayneeded to complete the iterative procedure (i.e., the number of steps to carry out, n) seems not to affectperformance too much.

For each path i→j, we define in (9) Esi→j as the normalized estimation error at the time in which the sth

estimation becomes available, i.e., at time sn∆ + (s − 1)I. The normalization is performed with respect tothe reference capacity for the ABE procedure, i.e., C:

(9)E R s n I R Ci js

i j i js

→ → →= +( )( ) −ava ava, ,ˆ∆



Figure 5. Simulation scenario

6There are five ingress nodes (i.e., sources) and, for each of them, four possible egress ones (i.e., destinations).

We recall that Rava,i→j(sn∆ + (s − 1)I)) is the actual, available bandwidth on the path i→j after s iterationsof the measurement process (each one lasting for n steps of length ∆ seconds, see (5), plus an intermedi-ate idle interval of I seconds), whereas R̂s

ava,i→j is the relevant estimated value (see (6) for details). If thenumber of traffic flows was constant, the estimated value would always be smaller than the actual one,that is, R̂s

ava,i→j ≤ Rava,i→j(sn∆ + (s − 1)I)), due to our choice of exporting the lower extreme of the quantiza-tion interval (see again (5) and Figure 3). Since during each estimation period (lasting n∆ seconds) somenew calls can be admitted, the estimated value could occasionally be larger than the actual one; thus itis necessary to use the absolute value for correctly evaluating the error. However, this event is quite rare,as shown also in Figure 6. Equation (10) reports Ei→j, the normalized error for the path i→j, averaged overthe time interval Tl during which the considered offered load (i.e., the arrival rate l) is constant. Sincethis time interval is equal to Tl = 3000s in the simulation setting, the number of samples, S, depends onn and it is easy to show that it is equal to S = (Tl + I)/(n∆ + I). The average, per-path, normalized esti-mation error is

(10)ES

R sn s I R

C SEi j

i j i js

i js

s

S

i

N

→→ →

→==

=+ −( )( ) −

= ∑∑1 1 1

11

ava ava, ,ˆ∆



2000 3000 4000 50002000

2500

3000

3500

Simulation time (s)

Ava

ilabl

e ca

paci

ty (

kbit/

s)

actual valueestimated, n = 3estimated, n = 4estimated, n = 5

2000 3000 4000 50002000

2500

3000

3500

Simulation time (s)

Ava

ilabl

e ca

paci

ty (

kbit/

s)

actual valueestimated, n = 3

2000 3000 4000 50002000

2500

3000

3500

Simulation time (s)

Ava

ilabl

e ca

paci

ty (

kbit/

s)


2000 3000 4000 50002000

2500

3000

3500

Simulation time (s)

Ava

ilabl

e ca

paci

ty (

kbit/

s)


Figure 6. Transient behavior for different values of n over the path ER0→ER4

Finally, (11) reports Enorm, representing the value of the mean, per-path, normalized estimation error,further averaged over the N(N − 1) paths connecting all the ERs to each other, for a specific value of thecall arrival rate l:

(11)

Figure 7 reports the value of the overall, average, normalized error, Enorm, as a function of the value ofthe call arrival rate, l. It appears immediately that the higher the value of n, the higher the precision, asexpected. However, the error rapidly increases and stabilizes around a value of 20% in the case of heavyload.7 This phenomenon is due to a number of reasons. First, the higher the value of l, the quicker thesystem fluctuations have to be tracked within the same sampling period. In addition, the estimation erroris increased by the choice concerning conservative estimation discussed in Section 3.2. To overcome thislimitation in the case of heavy load, we implemented also the non-linear coding described in Section 3,with n = 3 and a value of M = 40 iterations. This implies to reconfigure the network with a period ofabout 4min. It appears evident from Figure 7 that this option outperforms the linear coding scheme ineach load condition, even if the network exhibits some asymmetries in both topology and capacity con-figuration.

Finally, Figure 8 reports the value of the ABE overhead (in terms of consumed bandwidths) as a func-tion of the overall call arrival rate. The interesting feature of the proposed procedure is that, thanks tothe information embedded in the two possible events {feedback/timeout}, the ABE procedure is able to

EN N S

R sn s I R

C N NE

i j i js

s

S

j j i

N

i jj j i

N

i

N

i

N

normava ava=

−+ −( )( ) −

=−

→ →

== ≠→

= ≠==∑∑ ∑∑∑1 1

11 1 1 1

111 111

, ,

, ,

ˆ∆



1 2 3 4 5 6 7 80.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

Arrival rate, λ (calls/s)

Ove

rall

norm

aliz

ed e

rror

n = 3n = 4n = 5n = 3, non lin

Figure 7. Normalized error as function of the overall arrival rate l

7Note that normal operational conditions (i.e., an offered load equal to about 0.8 × K erlangs) corresponds to values for the global λ in the range[3.5–4] calls/s, which is the most interesting interval to analyze. Beyond these values, the rate of rejected flows starts increasing quite rapidly,and many flows get discarded by the AC procedure.

auto-regulate the capacity consumption. Specifically, when the network congestion increases, the numberof DPPs is rapidly decreasing (since most of them get dropped), even if they continue to provide theedge-to-edge resource estimation (very important for a network operator). However, this behavior is notalways true: the difference occurs for a mean value of available capacity over the paths slightly less thanC/2. In fact, values immediately above that value are codified with X = 100|2 (i.e., only the first DPPreaches the destination and the relevant feedback is transmitted, whereas the other two are, on average,dropped within intermediate routers), whereas values immediately below that value are codified with X = 011|2.

It is worth noting that the price to pay for having a higher resolution (i.e., higher values of n) is anincreased resource consumption in the case of high network load (this comment applies also to non-linearcoding, where there is also the overhead due to additional message exchanges). However, the overheadis equal, in every case, to very few kbit/s for the overall domain, whereas the overall simulated networkcapacity is equal to 66Mbit/s. In our evaluation, we considered as bandwidth consumption each packettransmission (if a packet crosses three links, it is counted three times), divided by the overall observa-tion period. We used DPP with size equal to 40 bytes (RTP, UDP and IP headers). In any case, the theo-retical maximum peak load for the smallest link (2Mbit/s) is equal to 6kbit/s,8 i.e., 0.3% of the linkcapacity (really far below the 10% indicated as the maximum tolerable value in Prasad et al. [15]). Sincethe link capacities in the analyzed scenario are well below real speeds whereas the monitoring timing isrealistic, the obtained percentage overhead can be considered a strong overestimation of possible realvalues.



1 2 3 4 5 6 7 80

1

2

3

4

5

6

7

8

9

10

Arrival rate, λ (calls/s)

Ove

rhea

d (k

bit/s

)

n = 3n = 4n = 5n = 3, non lin

Figure 8. Procedure overhead (in kbit/s) as a function of the overall arrival rate l

8Evaluated counting both DPPs and relevant feedbacks, and assuming that such a path is always under-loaded (thus no DPPs are discarded)and that all the paths cross that link.

5. CONCLUSION AND FUTURE WORK

In this paper we addressed the definition of what, in our opinion, will be two building blocks of a PDBsupporting QoS-aware service, namely a measuring procedure (ABE) that allows advertising the actualoccupancy status of a domain to neighboring domains and an AC function. We concentrated on the firstfunction, and evaluated the precision of the measuring procedure, which seems to be sufficient for itsobjective, i.e., coarse, aggregate measurements of available capacity, to be coupled with a punctual ACfunction. The overhead relevant to the ABE is really limited and routers do not have to actively managepackets, but, according to the DiffServ paradigm, their duty is limited to forwarding packets.

Obviously, the proposed procedure can be refined and adapted to different scenarios. However, in thispaper the focus is not only on the precision estimation of the proposed measurement algorithm (whichis very simple), but also on the overall system architecture. We think that our proposal, which is modular,stateless and distributed, can be useful in defining enhanced PDBs.

A final, concluding remark is that the described mechanism seems to be easy to implement on realDiffServ routers [12] and thus it is appealing in an open market scenario.

As regards future work, we stress that ‘network monitoring and measurement is increasingly regardedas an essential function for developing and supporting high-quality network services, building andimproving innovative networking technologies, analyzing infrastructure trends and user behavior andimproving the security of our cyber-infrastructure’ [20]. As a matter of fact, several projects funded bythe European Union are currently working on this problem [20,21]. The main goals of these projects are:(i) to understand the traffic dynamics and to devise traffic models; (ii) to continuously monitor the macro-scopic status of the network and, by leveraging on network tomography techniques, of individualdomains (reporting timely information about the actual reachability of nodes and synthetic QoS indica-tors); (iii) to deploy network-wide platforms for detecting macroscopically relevant events like outages,attacks, world-scale infections, and large anomalies. Really, continuous monitoring is nowadays vital toproactively control and manage fixed and mobile networks (thus improving the customer service level),and to detect and react to both small-scale abuses as well as large-scale attacks and threats (thus dra-matically improving the security of the networking environment).

However, a global monitoring infrastructure, capable of tracking and profiling even individual networkflows, poses serious privacy concerns. Thus, a key and open research challenge is to devise techniquesto acquire, process, transfer and export network data in a way that respects the privacy of network customers.

It is true that technical solutions addressing privacy issues, which minimize and control the amountof personalized information made available to third parties, are already being studied in projects [22];nevertheless, what is lacking today is the application of such technologies and mechanisms, nativelydevised for specific environments (such as protection of user identities and their personalized informa-tion) to the general large-scale process of monitoring and managing networks.

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2. Blake S, Black D, Carlson M, Davies E, Wang Z, Weiss W. An architecture for Differentiated Services. IETF RFC2475, December 1998.

3.Nichols K, Blake S, Baker F, Black D. Definition of the Differentiated Services Field (DS Field) in the IPv4 and IPv6header. IETF RFC 2474, December 1998.

4. Carpenter B, Nichols K. Differentiated Services in the Internet. Proceedings of the IEEE 2002; 90(9): 1479–1494.5. Nichols K, Carpenter B. Definition of Differentiated Services per domain behaviors and rules for their specifica-

tion. IETF RFC 3086, April 2001.6. Xiaoming Fu, Schulzrinne H, Bader A, Hogrefe D, Kappler C, Karagiannis G, Tschofenig H, Van den Bosch S.

NSIS: a new extensible IP signaling protocol suite. IEEE Communications Magazine 43(10): 133–141.



7. Bernet Y, Ford P, Yavatkar R, Baker F, Zhang L, Speer M, Braden R, Davie B, Wroclawski J, Felstaine E. A frame-work for Integrated Services operation over DiffServ networks. IETF RFC 2998, November 2000.

8. Huston G. Next steps for the IP QoS architecture. IETF RFC 2990, November 2000.9. Bianchi G, Blefari-Melazzi N, Femminella M. Per-flow QoS support over a stateless DiffServ domain. Computer

Networks 2002; 40(1): 73–87.10. Blefari-Melazzi N, Femminella M. A comparison of the utilization efficiency between a stateful and a stateless

admission control in IP networks in a heterogeneous traffic case. ACM/Kluwer Telecommunication Systems Journal2004; 23(3–4): 231–258.

11. Heinanen J, Baker F, Weiss W, Wroclavski J. Assured forwarding PHB group. IETF RFC 2597, June 1999.12. Bianchi G, Blefari-Melazzi N. Admission control over Assured Forwarding PHBs: a way to provide service accu-

racy in a DiffServ framework. In IEEE GLOBECOM 2001, San Antonio, CA, 25–29 November 2001.13.Breslau L, Jamin S, Schenker S. Comments on the performance of measurement-based admission control algo-

rithms. In IEEE INFOCOM 2000, Tel-Aviv, Israel, March 2000.14. Elwalid A, Mitra D, Wentworth RH. A new approach for allocating buffers and bandwidth to heterogeneous,

regulated traffic in an ATM node. IEEE JSAC 1995; 13(6): 1115–1127.15. Prasad R, Dovrolis C, Murray M, Claffy KC. Bandwidth estimation: metrics, measurement techniques, and tools.

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works. IEEE JSAC 2002; 20(9): 1745–1753.17. Rosen E, Viswanathan A, Callon R, Multiprotocol label switching architecture. IETF RFC 3031, January 2001.18. Le Faucheur F, Wu L, Davie B, Davari S, Vaananen P, Krishnan R, Cheval P, Heinanen J. Multi-Protocol Label

Switching (MPLS) support of differentiated services. IETF RFC 3270, May 2002.19. Habib A, Khan M, Bhargava B. Edge-to-edge measurement-based distributed network monitoring. Computer Net-

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AUTHORS’ BIOGRAPHIES

Nicola Blefari-Melazzi received his Laurea degree magna cum laude, in ElectricalEngineering in 1989, and earned his “Dottore di Ricerca” (Ph.D.) in Information andCommunication Engineering in 1994, both at the Università di Roma, La Sapienza,Italy. In 1993 he joined the Università di Roma “Tor Vergata”, as an Assistant Pro-fessor. From 1998 to 2002 he was an Associate Professor at the Università di Perugia.In 2002 he returned to Università di Roma “Tor Vergata”, as a Full Professor ofTelecommunications, teaching courses in Telecommunications Networks and Foun-dations of Internet. He is the co-ordinator of the PhD Program in “Telecommunica-tions and Microelectronic Engineering”.

Dr. Blefari-Melazzi has been involved in consulting activities and research pro-jects, including standardization and performance evaluation work. His research pro-jects have been funded by the Italian Ministry of Education, University and

Research, by the Italian National Research Council, by industries, by the European Union and by the European Space Agency. He co-ordinated a number of such projects. He also reviewed several research proposals and researchprojects.

Dr. Blefari-Melazzi served as reviewer, TPC member, session chair and guest-editor to IEEE conferences and jour-nals. He organized workshops on topics such as Quality of Service in the Internet, UMTS networks and UWB systems.He is author/co-author of about 130 papers, in international journals and conference proceedings.

His research interests include the performance evaluation, design and control of broadband integrated networks,wireless LANs and satellite networks. He is also conducting research on multimedia traffic modelling, mobile andpersonal communications, quality of service in the Internet, ubiquitous computing, reconfigurable systems and net-works, service personalization, autonomic computing.



Mauro Femminella received his Laurea degree in Electronic Engineering in 1999, magna cumlaude with publication of his thesis, and earned the PhD degree in Electronic Engineering in 2003, both at the University of Perugia, Italy. He has been a Consulting Engineer for theUniversity of Perugia, and for the Italian research consortia CoRiTel, CNIT and RadioLabs.He currently holds the position of Assistant Professor at the Department of Information and Electronic Engineering of the University of Perugia. He was involved in a number ofresearch projects co-funded by the European Union under the programs ACTS and IST, by the Italian Ministry for Education, Higher Education and Research (MIUR), and by theEuropean Space Agency (ESA). His research interests focus on design and performance evaluation of satellite networks, content delivery networks, IP quality of service and IPmobility. He is the co-author of a number of papers in international conferences and journals.



Measuring the edge-to-edge available bandwidth in a DiffServ domain

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