Joint Measurement- and Traffic Descriptor-based Admission Control at Real-Time Traffic Aggregation Points

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1 Joint Measureent- and Traffic Descriptor-based Adission Control at Real-Tie Traffic Aggregation Points Stylianos Georgoulas, Panos Triintzios and George Pavlou Centre for Counication Systes Research, University of Surrey, Guildford, UK e-ail: {S.Georgoulas, P.Triintzios, Abstract The priary role of adission control is to decide on the aount of traffic accepted into the network so that users conforing to their ablished traffic contracts achieve predefined perforance objectives, e.g. bounded packet loss probability, end-to-end delay. We consider Diffserv networks able to support real-tie traffic and we propose a novel fraework for adission control that involves both traffic descriptor and easureent based techniques and we copare its perforance with existing approaches. Our siulation results show that the perforance of our approach is rather insensitive to variations of the traffic sources. Even when the provided traffic descriptors are as siple as a single value denoting the required peak rate, the proposed schee achieves satisfactory perforance. I. INTRODUCTION Real-tie services have stringent delay and loss requireents. Differentiated services (Diffserv) is seen as the eerging technology to support IP Quality of Service (QoS) in a scalable fashion, without the relevant probles of Integrated services (Intserv), which require per icro-flow state inforation in the core network. In Diffserv, per flow state is only kept at edge routers, while in the core network, traffic with siilar QoS requireents is grouped in one of the engineered traffic classes and forwarded in an aggregate fashion. Adission control is the set of actions required at ingress nodes at the service instance ablishent phase to check whether a service requ is to be aditted or rejected. A service instance is aditted when the requed QoS can be satisfied without causing QoS violation to the already ablished service instances. In Diffserv, the violation ers to the QoS perceived by each of the engineered aggregate traffic classes rather than the QoS of individual icro-flows. An additional role of adission control is to optiize the use of network resources in order to achieve realistic network efficiency. The challenge is to design siple control functions that iprove efficiency under any offered traffic conditions. The various adission control approaches differ in the ethods they use to decide if there is enough capacity for the service requ and can be coarsely divided into three categories: (a) Adission control using a priori traffic descriptors, (b) Measureent based adission control (MBAC), and (c) Endpoint adission control (EAC). For the first approach [1], adission control is based on the assuption that it has perfect knowledge of each traffic source type that will be used in every link. It also knows the current nuber of ablished service instances. This inforation will enable adission control to copute the total aount of bandwidth required. Hence, it will only accept a service requ if the iniu aount of bandwidth required by the total nuber of ablished service instances, including the one, is less then the available service rate. It is obvious that this approach is optial if the accurate traffic descriptors are known and used for adission control decisions. As such, the perforance of this approach gives the upper bound for all other adission control approaches, provided of course that sources are conforant. However, since no traffic easureents are taken into consideration, if the provided traffic descriptors do not depict the actual behavior of the sources (for instance that could happen in case of non-conforant non-policed sources) or the appropriate traffic descriptors are not known a priori (for instance, only peak rate ay be known), the perforance of this adission control schee can be very low. The second approach (MBAC) tries to avoid the aforeentioned probles by shifting the task of traffic specification fro the user to the network []. Instead of users explicitly specifying their traffic descriptors, the network attepts to learn the characteristics of existing flows through real-tie easureents. This approach has a nuber of advantages. First, the user-specified traffic descriptors can be very siple, e.g. peak rate, which can be easily policed ([3] suggs that it is hard to police traffic according to a statistical odel). Second, an overly conservative specification does not result in over-allocation of resources for the entire duration of the service session. Third, when traffic fro different flows is ultiplexed, the QoS experienced depends often on their aggregate behavior, the statistics of which are easier to iate than those of an individual flow. However, relying on only quantities for adission control raises a nuber of issues that need to be considered, such as the iation errors [], syste dynaics and eory related issues. In the third approach, the end host/application probes the network by sending t packets along the transission path [4]. Based on soe etric then the host decides if the flow can be aditted. A requireent is for the end-to-end route to

2 be the sae for probing packets and flows. Setup delays ay be high and, furtherore, siultaneous probing by any sources can lead to a situation known as thrashing [4]. That is that even though the nuber of aditted flows is sall, the cuulative level of probe packets prevents further adissions. In this work we consider adission control for real-tie traffic. We define as real-tie traffic, sources that have a strict, usually sall, delay requireent and a bounded, not necessarily too low, packet loss rate (PLR) requireent. Note that jitter is another QoS paraeter for real-tie traffic but we do not consider it in this work. In a Diffserv doain we assue that such real-tie traffic is aggregated so that traffic fro sources coposing each traffic aggregate will receive the sae treatent over the entire doain. The delay requireent of the traffic aggregate has been taken into account in the provisioning stage, i.e. by appropriately setting sall packet queues and by anipulating the routing process to choose appropriate paths. Packets are expected to be lost only at the first point of aggregation (ingress node), where the serialization of the various traffic sources takes place. We assue that further downstrea; traffic aggregates are treated in a peak rate anner. This is feasible since, as stated in [5], in a coon network configuration, backbone links are overprovisioned. This paper is organized as follows: Section II presents our adission control fraework. In Section III we evaluate the perforance of our fraework against others found in the literature. Finally, in Section IV we conclude, suarizing our findings. II. FRAMEWORK FOR ADMISSION CONTROL In Diffserv networks, custoer traffic contracts are preagreed through SLSes [6] that include traffic descriptors. Our proposed approach is a cobination of Measureent-based and a priori Traffic descriptor Adission Control -we will be erring to it as MTAC. As such, it encopasses the positive features of both adission control schees. It provides a systeatic way to derive the bandwidth requireents of the already ablished flows through easureents and of the candidate for adission flow through its traffic descriptors. We use real-tie easureents of the actual load in order to cope with the fact that the traffic descriptors ay not depict the actual characteristics of individual flows. In this case, we ight end up with either under-utilization, if users overiate their requireents, or with QoS degradation if users underiate their requireents and the network is not able to police the efficiently. For bandwidth anipulation and allocation we adopted the effective bandwidth approach. According to [7], when the effect of statistical ultiplexing is significant, the distribution of the stationary bit rate can be accurately approxiated by a Gaussian distribution. Regarding the validity of this assuption, in [8] it is strongly sugged that the aggregation of even a fairly sall nuber of traffic streas is usually sufficient for the Gaussian characterization of the input process. In that case, the effective bandwidth of the ultiplexed sources is given by: C + a with a = ln( ε) ln( π) (1) where is the ean aggregate bit rate, is the standard deviation of the aggregate bit rate and ε is the upper bound on allowed queue overflow probability. A. Adission control logic We assue that through provisioning and traffic engineering, C total bandwidth is available edge-to-edge for the real-tie traffic aggregate. We use a erence source, with ean and standard deviation (, ) and average duration h, as a odel source for engineering reasons. We define as erence trunks ( T ) the nuber of siultaneously ablished erence sources that can fit in C total for a given target bound on PLR. In our odel, we assue that every tie a source wants to ablish or terinate a service instance, it signals this to the ingress node through a resource reservation protocol; as such, the nuber of active sources at every point in tie is always known. When a requ arrives, easureents are taken for bandwidth iations. The paraeters are the ean rate of the offered load, M, and the variance of the offered load,, at the output queue of the ingress node (first aggregation point). We then calculate the nuber N of the erence sources, whose aggregate ean rate is equal to or greater than M, and the nuber N of the erence sources, whose aggregate variance is equal to or greater than. That is, N represents the nuber of active erence sources that would have produced load with average rate at least equal to M, and N represents the nuber of active erence sources that would have produced load with variance at least equal to. They are the integers that satisfy the following relationships: M N = and N = () Having iated N and N, we copute their ean value, which represents an iate of the nuber of N erence sources that produce load with characteristics siilar to the ones having been. N = ( N + N )/ (3) The source requing adission has declared in the SLS its traffic descriptors (, ). In case the only available traffic descriptor is the source s peak rate p, the above pair becoes (, ) = ( p,0). In all cases, having the easureents and the traffic descriptors of the source, we copute the iated bandwidth C as follows: C = M + + a + PLR (4)

3 where a PLR is coputed as in (1), based on the target PLR bound of the real-tie traffic aggregate. This value C will be used in the adission control criterion. B. The Precaution Factor (PF) Before deriving the adission control criterion, there are two iportant issues that need to be taken into account. The first one is that the adission control decision needs to be ore conservative as the current easureents of the offered load correspond to a nuber of erence sources N that exceeds the nuber of erence trunks T to avoid degradation of perforance due to excessive PLR incurred by the overloading of the syste. The second iportant issue is that the ore stringent the target bound on PLR, the ore conservative the adission control decision should be. To deonstrate why that should hold, one can consider the following exaple: the bandwidth that is required by 100 VoIP sources, with peak rate 64kbps and exponentially distributed ON and OFF periods with average durations 1.004sec and 1.587sec, with a bound on queue overflow probability equal to 0.01, using (1), is Mbps. According to (1), the bandwidth required for 101 sources of that type for queue overflow probability 0.01 is Mbps. That eans that the additional source gives an increase of bandwidth, eff (0.01) = 9.0kbps. For queue overflow probability 0.001, the corresponding increase is eff (0.001) = 30.18kbps. This eans that since for decreasing bound on queue overflow probability (which directly translates to a decreasing bound on PLR) the increase in bandwidth requireents for each aditted source is greater, it should be taken into account in the adission control decision. When we decide whether a source should be aditted or not, the two issues discussed above need to be taken into account. Theore we introduce a Precaution Factor (PF ) before we derive the final expression for our adission control criterion. The ore conservative the adission control decision, the greater the value of the precaution factor. In order to take into account the first issue, PF should be proportional to the quantity ( N / T ). In order to take into account the second issue, we proceed as follows: given (1), for two different levels of PLR, e.g. ε 1 and ε with ε 1 < ε and all other paraeters (nuber and characteristics of sources) the sae, it can be sugged that this relative increase in additional bandwidth required is proportional to the quantity ln( ε1 ) ln( π). ln( ε ) ln( π) Theore, the final expression for the precaution factor that is adopted is: ln( ε) ln( π) PF = ( N / T )* (5) ln( ε ) ln( π) In both expressions, ε is a erence PLR level. Also, when it is N < T then the value PF = 1 is adopted. C. The easureent window We define the easureent windoww, as the tie interval within which the offered load is taken into account for deriving the required easureents. In siilar fashion to [9], we use the following expression for the easureent window: w = ax( DTS, w ), (6) In (6), DTS represents the Doinant Tie Scale. DTS is the ost probable tie scale over which overflow occurs. In [8], a systeatic way to derive DTS using real-tie easureents is provided with the assuption that the input process to the ultiplexing point in the network is Gaussian, which is by definition our assuption when eploying (1), and we use this ethod for iating DTS. w represents the ean inter-departure delay [], and it is defined as follows: w h =, (7) Nactive where N active is the nuber of siultaneously active sources. We select as easureent window the ean interdeparture delay, that is the tie interval within which the syste can be considered stationary -no flow departuresunless this tie interval is not long enough to capture the packet level dynaics of the aggregate traffic strea. In that case we use DTS as the value of the easureent window. D. The Adission Control Criterion Given the allocated bandwidth for the real-tie traffic aggregate fro edge-to-edge is C total, and having coputed the value for, eploying the precaution factor and the C easureent window, the adission control criterion becoes: If ( C PF) Ctotal, adit If ( C PF) > C reject total, III. PERFORMANCE EVALUATION In order to evaluate the perforance of the aforeentioned adission control fraework we used siulations. In our experients we use the dubell topology of Fig. 1. Figure 1. Siulation topology. (8)

4 The real-tie traffic QoS requireents are as follows. To eet the target bound on the delay of the aggregate, we configure the output queue to hold a axiu of 5 packets, thus enforcing that the upper bound on packet delay will be no ore than 5 ties the average packet size to the output link capacity. We use scenarios with the target bound on packet loss rate for the aggregate real-tie traffic being equal to 0.01 and We t varying load conditions ranging fro 0.5 to 5, where the value 1 corresponds to the design load. The latter is the load that the syste is designed to handle for a desired service rejection probability of 0.01, if we eploy a circuit switched approach. The reaining details of the siulation setup are: ε is set equal to 0.01, the source active tie is exponentially distributed with average h equal to 300sec. The source activation rate is a Poisson arrival process with average 1000 source activations/hour, which for a service rejection probability equal to 0.01 corresponds to T equal to 100. For traffic sources we use three different odels of VoIP traffic. All of the are ON/OFF sources with exponentially distributed ON and OFF ties, having a peak rate of 64kbps. The first odel [10], has ean duration for the ON and OFF periods of 1.004sec and 1.587sec respectively. This odel is also used as the erence source odel and thus sources that follow this odel are considered as conforant. The second odel we use is the Brady odel [11], which has ean duration for the ON and OFF periods of 1sec and 1.35sec respectively. Sources that follow this odel have worse characteristics than the erence sources; theore they are characterized as non-conforant. In the third odel [1], the ean durations for the ON and OFF periods are 0.35sec and 0.65sec respectively. Sources that follow this odel have better characteristics than the erence sources. The ter ild sources will er to this last category of sources. In order to copare the perforance of our fraework against other existing proposals, we have ipleented two algoriths fro the literature. The first one is an adission control schee using a priori traffic descriptors. We call this algorith TDAC (Traffic Descriptor-based Adission Control). In this schee, only the declared traffic descriptors are used for bandwidth iations and adission control. The second algorith is an ipleentation of the schee described in [13] as Rate Envelope Multiplexing (REM), with adaptive weight factor (the packet loss easureent approach) and no histogra update. We call this algorith MBAC (Measureent-based Adission Control). In this schee, in addition to easureents, the peak rate of the candidate for adission flow is used for adission control. Regarding the tie scales involved in MBAC, we consider the values 0.05sec, 0.1sec, 0.5sec, 1sec and sec. We also adopt the value 0.05sec as the iniu sapling interval involved in our approach in order to derive easureents for the ean rate, the variance of the offered load and iate the DTS. A. Sources that declare their real (, ) In this case the network has been provisioned and engineered using the erence source VoIP odel. However, the invoked sources ay deviate fro this odel, but they declare their real traffic descriptors. That could correspond to a situation where an ISP has provisioned and diensioned its network using a hypothetical erence source odel and has to serve requs that need to send VoIP traffic with different characteristics. We copare the perforance of MTAC against TDAC (we don t consider MBAC in this case because it cannot take advantage of other traffic descriptors apart fro the peak rate) for all three different types of traffic sources and the siulation results are shown in Fig. and 3, where M, R, W in brackets ean that the relevant curve corresponds to ild, erence and worse (non-conforant) sources. Figure. Achieved PLR for target PLR Figure 3. Utilization for target PLR For target PLR 0.01 and for all traffic sources, MTAC perfors reasonably well copared to TDAC. For ild sources the target PLR is not violated in any case. For erence and worse sources, the target PLR is violated only for loading conditions ore than 3 ties the design load, which, in a real network configuration, would correspond to a situation where the network diensioning and provisioning are inadequate. Additionally, it should be noted that MTAC is prone to easureent errors that affect the entities and the iation of DTS, since the coputed value for DTS depends on the values for ean rate and variance of the offered load. Regarding utilization, MTAC s perforance is also satisfactory; taking into account that TDAC provides the upper bound in perforance with respect to both the achieved PLR and utilization. Siilar conclusions, though not shown due to space liitations, can be drawn for the case of target PLR

5 B. Peak rate adission control In this case the invoked sources ay deviate fro the erence source odel and, additionally, the only traffic descriptor they declare is their peak rate p. We copare the perforance of MTAC against MBAC. The siulation results regarding PLR are shown in Fig. 4 and 5. We don t consider TDAC in this case because its overall perforance regarding utilization would be extreely low as a result of the stringent adission control criterion pi + p Ctotal. Figure 4. Achieved PLR for target PLR Figure 5. Achieved PLR for target PLR MBAC achieves PLR uch lower than the 0.01 PLR target for all traffic sources and all loading conditions. For ild sources, MTAC achieves the PLR target for all loading conditions. For erence and worse sources, the target PLR is violated only for loading conditions ore than 3 ties the design load. Regarding utilization, however, MTAC outperfors MBAC in all cases. MBAC s lower utilization is the penalty for achieving PLR significantly lower than the target PLR. The perforance of MBAC is very low for a target PLR equal to It violates that target in ost cases even though the no histogra update ethod that we use in our ipleentation of MBAC, is the ore conservative approach aong all the other variations presented in [13]. MTAC achieves siilar perforance to the case we had target PLR MBAC achieves better utilization than MTAC, but this cannot be considered as an advantage, because the increased utilization is coupled with a violation of the target PLR. In all cases exained, for both MTAC and MBAC we observe an increase in the achieved PLR for increasing loading conditions. This is anticipated [] because since they both rely on easureents, every adission requ carries the potential of aking a wrong decision. That eans i that a high source activation rate is expected to have a negative effect on perforance. IV. CONCLUSIONS In this paper we proposed an approach for adission control at the first points of aggregation. This approach is a cobination of traffic descriptor- and easureent-based adission control. We showed through siulation that our approach is relatively insensitive to variations of the traffic sources fro the traffic source odel used for network diensioning and provisioning, and that it works reasonably well even when the only available traffic descriptor is the peak rate. In the future we will be looking to extend our adission control fraework for different types of real-tie sources. Acknowledgent: This work was undertaken in the context of the Inforation Society Technologies Manageent of Endto-end Quality of Service Across the Internet at Large (MESCAL-IST ) research project, which is partially funded by the Coission of the European Union. V. REFERENCES [1] T.Lee and M.Zukeran An Efficiency Study of Different Model-Based and Measureent-Based Connection Adission Control Techniques using Heterogeneous Traffic Sources, Proceedings of IEEE ATM 1999, May [] M.Grossglauser and D.Tse A Fraework for Robust Measureent- Based Adission Control, IEEE/ACM Transactions on Networking, June 1999, Vol. 7, No. 3, pp [3] J.Hui Resource Allocation for Broadband Networks, IEEE Journal on Selected Areas of Counications, Deceber 1988, Vol. 6, No. 9, pp [4] L.Breslau, E.Knightly, S.Shenker, I.Stoica, and Z.Zhang Endpoint Adission Control: Architectural Issues and Perforance, SIGCOMM 000, Stockhol 000. [5] G.Iannaccone, M.May, and C.Diot Aggregate Traffic Perforance with Active Queue Manageent and Drop fro Tail, Coputer Counications Review, July 001. [6] E.Mykoniati, C.Charalapous, P.Georgatsos, T.Dailatis, D.Goderis, P.Triintzios, G.Pavlou, and D.Griffin Adission Control for Providing QoS in Diffserv IP Networks: The TEQUILA Approach, IEEE Counications Magazine, January 003, Vol. 41, No. 1, pp [7] R.Guerin, H.Ahadi, and M.Naghshieh Equivalent Capacity and its Application to Bandwidth Allocation in High-Speed Networks, IEEE Journal on Selected Areas in Counications, Septeber 1991, Vol. 9, No. 7, pp [8] D.Eun and N.Shroff A Measureent-Analytic Approach for Qos Estiation in a Network Based on the Doinant Tie Scale, IEEE/ACM Transactions on Networking, April 003, Vol. 11, No., pp [9] S.Belenki An Enforced Inter-Adission Delay Perforance-Driven Connection Adission Control Algorith, ACM SIGCOMM, April 00, Vol. 3, No., pp [10] C.Chuah, L.Subraarian, and R.Katz Furies: A Scalable Fraework for Traffic Policing and Adission Control, U.C Berkeley Technical Report No. UCB/CSD , May 001. [11] P.Brady A Model for Generating On-Off Speech Patterns in Two-Way Conversation, The Bell Syste Technical Journal, Septeber 1969, pp [1] I.Habib and T.Saadawi Multiedia Traffic Characteristics in Broadband Networks, IEEE Counications Magazine, page 48, July 199 [13] T.Lee and M.Zukeran Adission Control for Bursty Multiedia Traffic, Proceedings of IEEE INFOCOM 001, pp