Flow-Based Admission Control Algorithm in the DiffServ-Aware ATM-Based MPLS Network

Transcription

1 Flow-Based Admsson Control Algorthm n the Dfferv-Aware ATM-Based MPL Network Gyu Myoung Lee, Jun Kyun Cho, Mun Kee Cho, Man eop Lee, and ang-gug Jong Ths paper proposes a flow-based admsson control algorthm through an Asynchronous Transfer Mode (ATM based Mult-Protocol Label wtchng (MPL network for multple servce class envronments of Integrated ervce (Interv and Dfferentated ervce (Dfferv. We propose the Integrated Packet cheduler to accommodate Interv and Best Effort traffc through the Dfferv-aware MPL core network. The numercal results of the proposed algorthm acheve relable delay-bounded Qualty of ervce (Qo performance and reduce the blockng probablty of hgh prorty servce n the Dfferv model. We show the performance behavors of Interv traffc negotated by end users when ther packets are delvered through the Dfferv-aware MPL core network. We also show that ATM shortcut connectons are well tuned wth guaranteed Qo servce. We valdate the proposed method by numercal analyss of ts performance n such areas as throughput, end-to-end delay and path utlzaton. Manuscrpt receved July 3, 2; revsed Oct. 24, 2. The research featured n ths paper s sponsored by Korea cence and Engneerng Foundaton (KOEF, and partally supported by Korean Mnstry of Informaton and Communcaton (MIC. Gyu Myoung Lee (phone: , e-mal: gmlee@cu.ac.kr, Jun Kyun Cho (e-mal: jkcho@cu.ac.kr, Mun Kee Cho (e-mal: mkcho@cu.ac.kr, and Man eop Lee (e-mal: leems@cu.ac.kr are wth the Informaton and Communcatons Unversty, Daejeon, Korea. ang-gug Jong (e-mal: sgjong@kt.co.kr s wth Korea Telecom, Daejeon, Korea. I. INTRODUCTION One of the today s most pressng challenges n desgnng IP networks s to meet users Qualty of ervce (Qo requrements. The need to support Qo-senstve applcatons has led to the development of new archtectures. Internet archtecture that guarantees some Qo can be modeled by Integrated ervces (Interv and the Dfferentated ervces (Dfferv []- [4]. In the Resource Reservaton Protocol (RVP, the Interv model s used for sgnalng Qo requests from applcaton to network. Wth the Dfferv model, user flows are aggregated nto a small set of Class of ervces (Cos. nce Interv and Dfferv models focus, respectvely, on reservaton and scalable servce dfferentaton, t s advantageous to combne both for an overall soluton: a scalable and guaranteed end-to-end Interv servce model and Dfferv core network wth ndvdual Qo for flows. Integratng dfferent types of traffc n a sngle network requres an admsson control mechansm whch operates accordng to a resource reservaton mechansm. We propose an optmal admsson control algorthm whch uses flow-based classfcaton to meet users Qo requrements. In addton, we consder the Asynchronous Transfer Mode (ATM shortcut connecton n an ATM-based Mult-Protocol Label wtchng (MPL network to reduce end-to-end delay [5]-[8]. In ths paper, we present our desgn of an ATM-based MPL core network for accommodaton of the Interv and Dfferv models. These models requre sgnalng support for the assocaton of the desred category. The label and each packet belongng to a stream needs to carry the nformaton of the desred servce category. Our flow-based admsson control algorthm for the Int- ETRI Journal, Volume 24, Number, February 22 Gyu Myoung Lee et al. 43

2 erv/dfferv model on the ATM-based MPL network optmzes the network by usng admsson control accordng to traffc classfcaton, whch s sutable for multple servce class envronments. The proposed flow-based admsson control consders Qo and buffer statstcs. Ths algorthm makes an admsson decson n accordance wth the conventonal measurement-based admsson control. Consequently, ths algorthm can acheve two objectves: relable delay bound Qo and hgh resource utlzaton. Also, ths reduces the blockng probablty of hgh prorty servce class flow n the operatng Dfferv. The proposed approaches guarantee a hard Qo that satsfes all performance parameters (bandwdth, latency, jtter, etc. usng an ATM shortcut connecton for Interv guaranteed servce class. They also ncrease resource utlzaton accordng to per class Qo condtons for other servce classes. In the numercal analyss, we analyze probablty and delay as well as prorty queung behavor. We also examne the relatonshp between blockng probablty and the actual load. If the blockng probablty ncreases, the actual load decreases and network effcency mproves. However, the flow acceptance rate decreases. In addton, we apply the ATM shortcut connecton for the guaranteed servce class. An ATM shortcut has a number of advantages: hgher throughput, shorter end-to-end delay, and reduced router load. Numercal results of the proposed shortcut algorthm can mprove ther performance. In ecton II, the ATM-based MPL network archtecture and flow-based admsson control algorthm are dscussed. In ecton III, numercal analyses for the proposed network model and admsson control algorthm are taken. Fnally, numercal results are presented n ecton IV. II. ATM-BAED MPL NETWORK ARCHITECTURE AND FLOW-BAED ADMIION CONTROL ALGORITHM In ths secton, we dscuss the Dfferv-aware MPL/ATM core network for accommodaton of the conventonal Interv and Dfferv. We also nvestgate ntegrated packet schedulng and suggest a flow-based admsson control algorthm.. The Dfferv-Aware ATM-Based MPL Network Archtecture Fgure llustrates the archtectural model of the ATM-based MPL network. Its overlay archtecture s characterzed by layerng the ATM shortcut network and the MPL core network wth the Dfferv model. The MPL core network s composed of Label Edge Routers (LERs and Label wtchng Routers (LRs. The LER s located at the ATM edge swtch as an MPL-aware ngress/egress router. The LER performs label bndng based on the Label Informaton Base (LIB. The LR performs a label swappng functon and reserves the resources to buld a Label wtched Path (LP usng a Constrant-based Routng Label Dstrbuton Protocol (CR-LDP or RVP. LER ATM wtch LR LR ATM swtch LER : Label Edge Router Dfferv over MPL LR ATM hortcut Connecton LR LR : Label wtch Router LER ATM wtch Fg.. The proposed ATM-based MPL network archtecture. The advantage of the ATM-based MPL core network archtecture s that t combnes both the Interv and Dfferv network models for scalable servce dfferentaton. Fgure 2 shows the combned Interv/Dfferv network model usng the ATMbased MPL network. It supports multple traffc types n a sngle network. The core network s composed of ATM based MPL domans that support the Dfferv paradgm. The Access Network Doman consders three man servce domans: the Interv model, the Dfferv model, and the Best Effort servce model. The ngress LER performs admsson control and ntegrated packet scheduler functons. In the Interv model, multple classes of traffc can be assured of dfferent Qo profles. Accordng to avalablty of resources, the network reserves the resources and sends back a postve or negatve acknowledgement. In the Dfferv model, traffc s classfed nto dfferent behavor aggregates. Packets, whch enter nto ngress LER at the border of the core network, are assgned a sngle Dfferentated ervce Code Pont (DCP. They are forwarded as per hop behavors assocated wth ther codeponts. In the Best Effort servce model, t delvers packets to ther destnaton wthout any bounds on delay, latency, jtter, etc. The Dfferv model n the core network may not acheve the performance that can be obtaned by the Interv model at the edge of ATM network. The Interv model takes care of end-toend behavor n ts ntrnsc defnton, whle the Dfferv model bascally specfes local behavor that must be somehow composed to acheve end-to-end sgnfcance. Our proposed network model consders the ntegrated solutons of the two approaches. Ths network model ncludes a scalable endto-end Interv model wth acceptable servce guarantees n the core network. 44 Gyu Myoung Lee et al. ETRI Journal, Volume 24, Number, February 22

3 Access Network Doman Core Network Doman (Dfferv over MPL Access Network Doman Interv Ingress LER Interv Dfferv Admsson Control MPL/ATM Core Network Egress LER Dfferv Best Effort Integrated Packet cheduler Best Effort Fg. 2. The Dfferv/Interv models on the ATM-based MPL network. Access Label Edge Router (LER MPL/ATM Core RVP Request/Reservatons Qo Translaton Interv Flows Guaranteed Traffc hortcut Connecton (ATM sgnalng Dfferv Packets BestEffort Packets Dfferv Mappng Admsson Control Integrated Packet cheduler EF Traffc Other Traffc Classes Best-Effort Traffc Label wappng (MPL gnalng Fg. 3. The proposed traffc flow model of the MPL network. 2. Traffc Flow Model of the ATM-Based MPL Network The Qo-enabled LERs should functon lke an Interv/ Dfferv and/or Best Effort capable router at the edge node and lke a Dfferv router at the core network. The Interv model makes a bandwdth reservaton along the path and performs polcng on the packets. The Dfferv model smplfes the forwardng functons n the core network and no polcng occurs. The traffc flow model of the LER s proposed n Fg. 3. Incomng IP packets are classfed as Interv flows, Dfferv flows, or Best Effort flows. They are processed by the ntegrated packet scheduler usng admsson control. The ntegrated packet scheduler performs approprate queung dscplnes based on servce classes. By ntroducng flow concept, IP swtchng has been developed as a set of methods to reduce router workload and to offer Qualty or Grade of ervce n the network. As llustrated n Fg. 3, we propose that flows can be handled n the core network by two methods. One uses the ATM shortcut connecton. The ngress LER performs a Qo translaton and maps the RVP traffc parameters nto ATM parameters. The other uses the Label swappng technology. For guaranteed traffc, we propose the ATM shortcut connecton. An ATM shortcut connecton can provde hgher throughput, shorter end-to-end delay, reduced router load, and path utlzaton. In the ATM-based MPL networks, a shortcut connecton s set up usng Vrtual Path Identfer (VPI/Vrtual Channel Identfer (VCI values whch are allocated wth proper admsson con- ETRI Journal, Volume 24, Number, February 22 Gyu Myoung Lee et al. 45

4 trol. For the guaranteed flows, the LER sets up an end-to-end shortcut connecton. Guaranteed Flow 3. Dfferv Model for the Integrated Packet cheduler The Dfferv n the MPL network needs to carry packets at the desred servce category. A separated LP s created for each Forwardng Equvalence Class (FEC and schedulng aggregate par. Dfferentaton n treatment of packets from dfferent behavor aggregates has to be mplemented by mappng drop precedence. Thus, when the underlyng technology s ATM, t can only support two levels of drop precedence. However, by markng the use of the EXP feld n the shm header for the top label stack entry, support for all the drop precedence can be provded n MPL clouds. A shm header cannot be used wth ATM because ths would nvolve dong segmentaton and re-assembly at each ATM-LR n order to read the DCP. Hence, the DCP n the IP header s not accessble by the ATM hardware responsble for the forwardng. Therefore, two alternatve solutons may be consdered: ether have some part of the ATM cell header mapped to the DCP, or use an LDP. In the frst approach, the most lkely soluton s to use the VPI and part of the VCI of the ATM cell header as the label, and to use the remanng eght least sgnfcant bts of the VCI to map the DCP. Then, all that s needed s a functonal component n the nteror Dfferv-enabled ATM LRs to perform the approprate traffc management mechansms on the cells by nterpretng the DCP correctly wth respect to the Per-Hop Behavor (PHB. In the second approach, whch s more lkely for future deployment, the DCP s mapped to an LP at the ngress of the MPL doman. Ths means that for each DCP value/phb a separate LP wll be establshed for the same egress LR. Therefore, f there are n classes and m egress LRs, n m LPs and n labels for each of the m FECs need to be set up. The packets belongng to streams wth the same DCP and FEC wll be forwarded on the same LP. In other words, the label s regarded as the behavor aggregate selector. In Fg. 4, the Dfferv model n the proposed MPL router system s shown. Ths s smlar to conventonal Dfferv archtecture but has many addtonal functons lke servce mappng. Whle ncomng packets are composed of Interv flows and the Best Effort flows, the LER s able to convert Interv requests nto Dfferv traffc classes. Table shows the DCP of ncomng Dfferv packets accordng to class. In Fg. 4, the Mult-Feld (MF classfer checks Interv/ Dfferv and Best Effort through IP flow classfcaton. A traffc condtoner s a part of a network node that takes the node s ngress packets as ts nput and places the packets n ts output n an order that best satsfes the forwardng requrements set Flow MF Classfer Traffc Condtoner BA Classfer ervce Mappng/ Meter haper/ Dropper Marker EF Flow AF Flow Best Effort Flow Admsson Control Fg. 4. The Dfferv model n the proposed MPL router system. Table. The Dfferv classes. ervce class Control Codepont Expedted Forwardng (EF Assured Forwardng (AF Controlled Low drop precedence Medum drop precedence Hgh drop precedence,,,,,,,,,, Best Effort (BE t controlled for the packets and uses the network s resources n the best possble way. The Behavor Aggregate (BA classfer classfes packets based on the DCP only. If ncomng packets are n the Best Effort servce class, servce mappng s performed accordng to applcatons. The low qualty servces of each group (.e., multmeda conferencng, Fle Transfer and WWW navgaton can be mapped to a Best Effort servce class. Applcatons that request Qo, such as vdeo telephony, premum multmeda conferencng, on demand retreval, and premum WWW navgaton, are mapped to a guaranteed flow and wll be assgned to one of the Dff- erv classes. Fgure 5 shows the Dfferv mappng algorthm n detal. Incomng packets are mapped nto fve servce classes: Guaranteed, Expedted Forwardng (EF, two types of Assured Forwardng (AF, and Best Effort. In the Integrated Packet cheduler for admsson control, there are usually multple logcal queues destned to each outgong lnk. Fgure 6 shows the queung archtecture of the proposed Integrated cheduler for Guaranteed, EF, AF, AF2, and Best Effort traffc. Each queue may contan packets from one flow or a class of flows. The basc operaton of schedulng s to decde, at a partcular moment, whch packet n all these queues should be transmtted onto the outgong lnk. The decson 46 Gyu Myoung Lee et al. ETRI Journal, Volume 24, Number, February 22

5 Fg. 5. Dfferv mappng algorthm. Guaranteed servce EF FIFO FIFO+ Integrated Packet cheduler ( fnte buffer Guaranteed ervce, we consder per flow queung for each ndvdual Guaranteed flow. There s no buffer sharng between Guaranteed flows and other traffc flows. The separate buffer for the Guaranteed flow offers an excellent balance between traffc solaton and buffer sharng. For the EF and the AF flows, we adopt a Frst In Frst Out (FIFO + queue [9]. The dea behnd FIFO+ s to try to lmt the accumulaton of delay across hops and to brng down the worst-case delay. Each hop group flows nto classes. Each class tracks ts average queung delay for the hop. For each packet, the hop computes the dfference between the queung delay the packet experences and the average queung delay for the packet s class. It then adds (or subtracts ths dfference from an offset feld n the packet s header. Over the hops, ths offset wll record how far ahead or behnd the packet s from ts class s average. Each hop s requred to schedule a packet n ts queues as f t had arrved at the packet s real arrval tme plus the packet s offset. It s mportant to keep n mnd that FIFO+, unlke Weghted Far Queung (WFQ, s a statstcal multplexng technque. Because t does not provde strct solaton, FIFO+ may occasonally volate ts delay requrements. Applcatons that requre strct delay guarantees wll have to be satsfed wth the longer delay bounds of queung schemes lke WFQ. For Best Effort flows, we employ a common FIFO shared queue. There s no Qo commtment to each ndvdual Best Effort flow. We can defne four prortes: EF, AF, AF2, and BE. Incomng traffc s classfed and stored at four separate queues. The advantage of the prorty queue s the absolute preferental treatment that always gves top prorty to msson crtcal traffc n the event of congeston. 4. Flow-Based Admsson Control Algorthm Accept Admsson Controller AF AF2 Best Effort FIFO ( Prorty Queueng Fg. 6. The queueng model of the proposed ntegrated packet scheduler. algorthm drectly affects delay performance and the bufferng strategy drectly affects packet performance. The ntegrated packet scheduler s realzed by applyng a Prorty Queung (PQ algorthm and an admsson control functon. For Admsson control s the process that decdes whether a newly arrvng request for servce from a network element can be granted. It must be performed on any servce that wshes to offer absolute quanttatve bounds on performance. The precse crtera for makng the admsson control decson are specfc to each partcular servce wth some statements of performance. The goal of the admsson control algorthm s to meet users qualty of servce requrements. The admsson control algorthm s ultmately constraned by the servce commtments []-[]. Fgure 7 shows the proposed admsson control algorthm. The proposed flow-based admsson control algorthm decdes on two terms: Qo condton and resource condton. It can be classfed nto the conventonal measurement-based admsson control. The Qo parameters are offered on a per flow bass, correspondng to the number of related perform- ETRI Journal, Volume 24, Number, February 22 Gyu Myoung Lee et al. 47

6 ance objectves. The proposed flow-based admsson control has the followng assumptons. Each servce class such as Guaranteed ervce (G, EF, AF, AF2, Best Effort (BE s dvded at the edge node wth the negotated users requrements of delay,, and bandwdth. Identfy Flow's Class of ervce Guaranteed Dfferv Best Effort Qo Condton Resource Condton Accept EF Qo Condton Admsson Request AF Qo Condton Per class Qo Condton Resource Condton Accept AF2 Qo Condton Resource Condton Accept III. PERFORMANCE ANALYI. M/M//K wth Bulk Arrval ystem Modelng To analyze the proposed flow based traffc admsson control algorthm, we model the queung system wth a fnte buffer and bulk arrval. We nvestgate the queung behavor wth four prortes - EF, AF, AF2, and BE - n terms of blockng probablty, end-to-end throughput, and average message transfer delay. If the packet arrvals that occur n dsjonted tme ntervals are ndependent and dentcally dstrbuted, we assume that the proposed system s modeled as an M/M//K queue wth bulk arrval. Ths means that the aggregated arrval flow s modeled after a Posson process and each packet flow s exponentally dstrbuted. We also assume that message sze s modeled by geometrc dstrbuton as a bulk arrval process snce Internet traffc tends to be volatle. We assume the system can hold at most a total of K customers wth fnte storage. Fgure 8 shows the state transton model of an M/M//K system wth bulk arrvals, where s the mean arrval rate and µ s the average servce rate. g g g 2 2 g g j - 2 j - j j + j + 2 K g µ µ µ µ Fg. 8. The M/M//K state transton model wth bulk arrval. ATM hortcut Connecton (ATM gnalng Map Label per FEC (MPL gnalng To model the bulk arrval process, g s gven by Fg. 7. The proposed flow-based admsson control algorthm. In the G class, the edge node checks Qo and resource. It rejects connecton admsson when t can t meet servce commtment. In the Dfferv servce classes, we use a Qo condton per class. To meet the Qo condton, the edge node decdes whether ( Tˆ overall the estmated overall aggregated traffc flow s delay accordng to system queung behavor s satsfed wth a delay bound ( D bound that users are requested. New connectons are accepted only f they can be guaranteed to meet the requested performance bounds. Here, we defne the estmate d rate (Rˆ wth the users requred rate ( R requred. The measured performance drectly affects the flow-based admsson control and resultng network utlzaton. Ths flow-based admsson control algorthm can acheve two objectves: delay and utlzaton. where g P[ bulk sze s ] ( K g + g g K g. Let us defne the p, as the steady state probablty of the -th state, whch can be obtaned by K j j g + µ p j µ p j+ + pg j j K (2 K g p p µ p Usng the method of z -transforms, we have. (3 µ ( P z p ( P( z p p z K j g + µ ( z (4 + P( z G( z, 48 Gyu Myoung Lee et al. ETRI Journal, Volume 24, Number, February 22

7 where P( z j p z j j K j p z j j prorty queung system E[ Wp ], the average watng tme of messages of prorty class p, s gven by [2], and G( z g z g z. The system utlzaton factor ρ can be gven n terms of the bulk arrval rate as In the case of a fxed bulk sze, K G ( z z ρ µ g g. s gven by r r. Then, the system can be modeled and analyzed by an M/E r / queung system wthr stages of servce tme [2]. In the specal case of r, p, the steady state probablty of the -th state s gven by P k µ + k K k µ µ K otherwse. w, the probablty can be calculated n p K value snce the system has a fnte buffer of K. The average number of customers n the system N s gven by N (5 (6 (7 kp k. (8 K k And T, the average system transfer delay, can be obtaned as follows: 2. Prorty Queung Analyss N K T kp k. k We analyze the prorty queung model for Dfferv. We assume P classes (where p, 2,..., P wth non-preemptve prorty. We also assume that messages wth prorty class p arrve at the Posson process wth rate p. Then, the nterarrval tme dstrbuton of messages wth prorty class p s gven by e p t ( t t. Wth the non-preemptve A p (9 P E [ W ] E[ T ] + E[ T ] + E[ T ], ( P p P p p where T s a resdual lfe tme to complete the current servce, Tp s the message servce tme of prorty p, and T p s the message servce tme of hgher prorty durng the watng nterval of messages wth prorty class p. Here, E[T ] s just the weghted sum over all prorty classes, whch s gven by 2 E[ T ] E( τ 2 2 P p 2 p pe( τ p, where the aggregate arrval rate s gven by τ p s the message servce tme wth prorty p. 3. Blockng Probablty and Throughput Analyss P p (, and We analyze the blockng probabltes n the proposed flow admsson control algorthm. The blockng probablty depends on the probablty, P delay, such that a delay tme n the system s less than the negotated threshold value and the probablty, P, such that the n the system s less than the threshold value, and the probablty, P resource, such that the buffer resource n the system s less than the threshold value, whch can be represented by P P P, (2 blockng Qo resource where P Qo s the probablty that s satsfed wth the Qo condton. It can be modeled by P Qo P P P[ Tˆ D ] P[ Rˆ R ] (3 delay overall bound requred where Tˆoverall s the estmated overall delay and Rˆ s the estmated rato, D bound s the negotated delay bound and R requred s the negotated packet rate. In (2, s the probablty that s satsfed wth the P resource resource condton. Let be the nstantaneous estmated arrval rate of flow ˆ,τ at tme s are ndependent, dentcally dstrbuted. Let be the nstantaneous estmated arrval rate of τ. Assume that n ˆ, τ p flows at the specfc lnk. Whle the packet arrval rate s specfed as a functon of the parameters, {peak rate, average rate, Maxmum Burst ze (MB}, we calculate P resource n approxmately as ˆ,τ ETRI Journal, Volume 24, Number, February 22 Gyu Myoung Lee et al. 49

8 P n ˆ P, τ C, (4 resource µ where µ C s the actual avalable bandwdth,.e., the packets servced per second snce µ s the actual servce rate of a packet and C s the actual channel capacty n bt per second. Wth the relatonshp of the blockng probablty, the actual throughput ρ can be obtaned by s ( Pblockng, ρ (5 where s s also the actual arrval rate,.e., the actual throughput s decreased by the blockng probablty. It can be extended to calculate the performance of the queung model for the ntegrated packet scheduler (Fg. 6. There are fve queung systems: Guaranteed ervce (G, Expedted Forwardng (EF, Assured Forwardng - level (AF, Assured Forwardng - level 2 (AF2 and Best Effort (BE. G G G blockng ρ C C G G G G G ( P ( P P P delay resource EF D EF EF EF D ( Pblockng Pblockng ( PdelayP Presource C ρ ρ ρ C 3 ρ C 4 C 2 C 2 AF D ( P P EF EF EF AF AF AF D ( ( P P + ( P P P C 3 C 3 C 2 delay blockng blockng AF 2 D ( Pblockng Pblockng delay resource EF EF EF AF AF AF AF 2 ( ( Pdelay P + ( Pdelay P + AF 2 AF 2 D ( Pdelay P Presource C 4 C EF EF C 2 AF AF C3 AF 2 AF 2 {( ( PdelayP + ( PdelayP + ( Pdelay P D C 4 BE ( Presource + }( Presource (6 (7 (8 (9 (2 The superscrpt denotes G: Guaranteed servce, EF: Expedted servce, AF: Assured Forwardng level, AF2: Assured Forwardng level 2, BE: Best Effort, D: Dfferv, The superscrpt C, C2, C3, and C4 mean the actual throughput of EF class. AF class, AF2 class, and BE class, respectvely. where the actual arrval rate of each class can be gven as shown n Fg. 9. C C 2 C3 C 4 EF EF EF Preject + AF (2 (22 EF EF AF AF AF 2 ( Preject + Preject + (23 EF EF AF AF AF 2 AF 2 BE ( Preject + Preject + Preject +. (24 Class (EF EF Class 2 (AF EF EF AF + Preject Class 3 (AF2 ( EF EF Preject+ AF AF Preject+ Class 4 (BE EF EF AF AF AF ( + P P + P reject reject AF2 2 AF BE reject 2 + Fg. 9. Calculaton of arrval rate of the proposed ntegrated packet scheduler. 4. End-to-End Performance Analyss w, we analyze the end-to-end performance of the throughput and transfer delay. In our analyss, the Markovan queung network models are assumed for end-to-end data flows. All traffc flows are assumed to have behavor that s smlar to Posson arrval and exponental dstrbuton. Frst, we analyze the end-to-end throughput accordng to the flow blockng probablty. We consder the fxed routng path γ between the source and destnaton node for data transfer. w, the flow utlzaton ρ γ for the routes γ s gven by ( B γ γ ργ (25 where γ s the total number of packets per second that arrve at the orgnal node on the route γ, γ s the number of packets per second that are actually delvered to the destnaton node, B s the probablty that the flow s blocked on route γ, / µ s the average packet length, and C s the capacty n number of bts per second. w, the flow blockng probablty B s gven by 5 Gyu Myoung Lee et al. ETRI Journal, Volume 24, Number, February 22

9 h ( L ( L, Π γ B Π (26 where L s the probablty that the -th lnk s blocked on the route γ and h s the hop dstance of route γ. The lnk blockng probablty depends on the probabltes that the lnk s overflowed and out of servce. Here, the lnk blockng probabltes are recursvely calculated by L L ( β, ρ, C β + β + ( β Pover ( ρ, CU ( β Er( ρ, C snce the -th lnk utlzaton s gven by ρ (27 ( β s the probablty that lnk s out of servce (e.g., lnk or channel errors, routng falure, etc., and P over ( s the probablty that the gven vrtual crcuts are overflowed. It could be approxmately calculated by the Erlang formula denoted by Er( [2]. w, we analyze the connecton behavor of route γ on the saturated condton. Here, we assume that all the cascadng crcuts consstng of the route γ have a same bandwdth. As the flow traffc ncreases, the lnk utlzaton on route γ converges to overall end-to-end flow utlzaton. In a saturated condton, the -th lnk utlzaton s ρ could be calculated by ρ ( L + ( L γ γ ργ ργ + ρ γ for, K, h L. (28 where ρ γ γ, γ s the number of packets per second whch arrve at the -th lnk on the route γ, and γ s the number of packets per second whch arrves at the -th lnk except from the gven route γ. From (28, t results n a saturated condton that γ ργ for, K, h. (29 L For the transfer delay analyss, we utlze the Markovan network model of a cascaded M/M/ queung crcut. The average flow transfer delay could be calculated by T T γ ( T ( B γ B γ h ( L T, (3 where T ( ( L. The overall average end-to- end delay can be calculated by summaton of the average system delay and average flow transfer delay, that s T T + T. (3 overall w, we calculate the number of lost messages on the route γ snce they may be caused by lnk blockng or out of resources. It could be measured by the average number of messages that are not correctly delvered to the destnaton, whch s denoted by N, and gven by N T C ( T B + 2T ( B B (32 γ T where B D s the probablty that a destnaton user s blocked snce there are no avalable resources. IV. NUMERICAL REULT We present numercal results for our proposed system ncludng the ntegrated packet scheduler. To get the numercal results, we consder the followng assumptons. For the fve queues model of the G queue and prorty queues (EF queue, AF queue, AF2 queue, BE queue, each queue has the same buffer sze and the traffc actvtes of each class are statstcally the same. We assume that the lnk bandwdth s 55Mbps and the mean servce tme s 2.73 µ s. The basc unt of the packet sze s 53octets for the bulk arrval model. We frst show the probablty n M/M//K wth the bulk arrval system. Next, the average tme delay and the average watng tme n the prorty queung system s represented. We also present the blockng probablty and actual load consderng our flow-based admsson control algorthm and end-to-end performance result.. Loss Probablty The probablty strongly depends on the buffer sze K. The behavors due to buffer overflow can occur n the G queue and BE queue. Fgure shows the behavor of probablty versus buffer sze. Loss probablty decreases as the buffer sze K ncreases. Fgure (a shows that the probabltes are senstve to bulk sze. We observe that the probabltes are about 6 for r 5, 9 for r, and 5 for r2 n the same buffer sze (K2. In Fg. (b, we observe the behavor of probablty as the offered utlzaton ncreases. Ths fgure shows that the probablty s senstve to the utlzaton u. We observe that the probabltes are about 3 for u.9, 5 for u.8, 9 for u.6, and 5 for u.4 n the same buffer sze (K2. T D ETRI Journal, Volume 24, Number, February 22 Gyu Myoung Lee et al. 5

10 Buffer ze (K Loss Probablty (Log (log scale Loss Probablty (Log (log scale x -4 Utlzaton.6 x -7 x - r2 r r5 r x -3 (a as bulk sze r ncreases x Buffer ze (K Bulk ze x -6 u.9 x -9 u.6 u.8 x -2 u.2 u.4 Average Tme Delay (msec (ms Average Tme Delay (msec (ms 8 Buffer ze r5 6 4 r 2 r2 r Offered Load (a as the bulk sze r ncreases 5 Bulk ze 4 3 K2 2 K3 K K5 (b as the offered utlzaton u ncreases Fg.. Behavors of probablty versus buffer sze Offered Load (b as the buffer sze K ncreases 2. Average Tme Delay Next, we calculate the average tme delay n M/M//K wth the bulk arrval system. Fgure shows the behavor of the average transfer delay versus the offered load. Ths performance behavor s appled to all traffc classes. The overall delay behavor s very smlar to the conventonal queung behavor. Fgure (a shows the average tme delay versus the offered load as the bulk sze ncreases. The average tme delay sharply ncreases for a hgh loadng condton as the bulk sze ncreases. Ths ndcates that the bulk arrval has a great nfluence on system performance. Fgure (b gves the average tme delay dependng on the offered load for the buffer sze dfference when the bulk sze s fxed. Ths fgure demonstrates that buffer sze change has only a lttle nfluence on the average tme delay. The average tme delay scarcely ncreases untl the offered load reaches.8. Fg.. The behavors of average tme delay versus offered load. 3. Prorty Queung ystem Performance In the numercal results, the followng servce classes are defned as EF, AF, AF2, and BE. As shown n Fg. 9, each servce class queung model s represented by the relatonshp of servce classes and blockng probabltes accordng to our flow-based admsson control algorthm. We assume that the C C 2 C3 C 4 arrval rates are the same, that s,. Fgure 2 shows the behavor of the average watng tme versus the offered load for the dfferent prorty classes. As we can see, class traffc results n the shortest average watng tme. 4. rmalzed End-to-End Throughput and Transfer Delay Fgure 3 depcts the behavor of normalzed throughput versus the lnk blockng probablty. The capacty of data flows are 52 Gyu Myoung Lee et al. ETRI Journal, Volume 24, Number, February 22

11 2. Average Watng Tme (msec (ms 5 5 class 4 class 3 class 2 h5 h h5 h rmalzed Maxmum Flow Throughput class Offered Load Fg. 2. Average watng tme versus offered load for the prorty queueng model Flow Blockng Probablty (log (Log scale Fg. 4. rmalzed maxmum flow throughput versus flow blockng probablty (h hop count.. 5 h h5 h5 h rmalzed Maxmum Flow Throughput rmalzed Message Transfer Delay h5 h h5 h Lnk Blockng Probablty (log (Log scale Offered Load Fg. 3. rmalzed maxmum flow throughput versus lnk blockng probablty (h hop count. Fg. 5. The behavors of normalzed message transfer delay versus offered load. sgnfcantly reduced whle both the hop dstance and the lnk blockng probablty ncrease. Fgure 4 shows the behavor of normalzed throughput versus the flow blockng probablty. It notes that the flow blockng probablty can be gven n the flow admsson control algorthm shown n Fg. 7. The normalzed throughput of data flows s reduced accordng to both the hop dstance and the flow blockng probablty, but the flow blockng probablty affects the throughput behavor more. Fgure 5 shows the behavor of normalzed mean data transfer delay versus the offered load when the flow blockng probablty s. Ths fgure also shows the behavor of the data 3 channel as the hop dstance ncreases. The normalzed mean data transfer delay has an nfluence on the hop count and of- fered load. It shows that message transfer delay s reduced for the short hop dstance. V. CONCLUION In ths paper, we proposed a flow-based admsson control algorthm to ntegrate Dfferv and Interv models on ATM based MPL networks. We demonstrated that the proposed algorthm s one of the best solutons [3], [4] for Interv/ Dfferv provsonng as well as guaranteed Qo provsonng. The proposed network supports the vrtual shortcut connecton or MPL label swappng usng an underlyng ATM capablty. Ths has a number of advantages on throughput, end-to-end delay, and flow utlzaton. ETRI Journal, Volume 24, Number, February 22 Gyu Myoung Lee et al. 53

12 The proposed Dfferv mappng and ntegrated packet scheduler are the essental part of the flow-based admsson control algorthm. Accordngly, we also analyzed the behavor of normalzed throughput versus flow blockng probablty. The proposed approaches guarantee a hard Qo usng an ATM shortcut connecton for the Interv guaranteed servce class and ncrease resource utlzaton accordng to the per class Qo condton. We analyzed probablty and average tme delay wth the proposed prorty queung model. The results show that probablty s closely related to buffer sze and bulk sze. The mean transfer delay s related to prorty and hop count. As the flow blockng probablty ncreases, the behavor of the normalzed throughput s obtaned. However, we dd not analyze the detaled blockng probablty accordng to traffc characterstcs. Therefore, t s necessary to conduct further performance analyss. The numercal results show good performance behavor on normalzed throughput and delay performance. In the proposed prorty queung system of the Interv/Dfferv model, the average delay of class 4 traffc ncreases by about three tmes that of class traffc. When the offered load s less than.6, the blockng probablty of guaranteed servce s less than %. As the lnk blockng probabltes ncrease about %, the flow blockng probabltes for data flows ncrease about 2 for hop count h 2 and about for hop count h 5. REFERENCE [] G. Echler, H. Hussmann, G. Mamals, C. Prehofer, and. alsano, Implementng Integrated and Dfferentated ervces for the Internet wth ATM Networks: a Practcal Approach, IEEE Comm. Magazne, vol. 38, Jan. 2, pp [2] Paul P. Whte, RVP and Integrated ervces n the Internet: a Tutoral, IEEE Comm. Magazne, vol. 35, May 997, pp [3] K. Nchols,. Blake, F. Baker, and D. Black, Defnton of the Dfferentated ervces Feld (D Feld n the IPv4 and IPv6 headers, RFC 2474, IETF, Dec [4] P. Gacomazz and L. Musumec, Transport of IP Controlled- Load ervce over ATM Networks, IEEE Network, vol. 3, Jan- Feb. 999, pp [5] Arun Wswanthan, Nancy Feldman, Zheng Wang, and Ross Callon, Evoluton of Mult-Protocol Label wtchng, IEEE Comm. Magazne, vol. 36, May 998, pp [6] Anoop Ghanwan, Blel Jamouss, Don Fedyk, Peter Ashwood- mth, L L, and Nancy Feldman, Traffc Engneerng tandards n IP Networks Usng MPL, IEEE Comm. Magazne, vol. 37, Dec. 999, pp [7] B. Dave, J. Lawrence, K. McCloghre, Y. Resen, G. wallow, Y. Rekhter, and P. Doolan, MPL Usng LDP and ATM VC wtchng, RFC335, IETF, Jan. 2. [8] J.K. Cho,.H. Km, N.Km,.W.hon, and M.K. Cho, Performance Benefts of Vrtual Path Tunnelng for Control and Management Flows n the Broadband ATM Networks, ETRI J., vol. 2, no. 4, Dec. 999, pp [9] D.D. Clark,. henker, and L. Zhang, upportng Real-Tme Applcatons n an Integrated ervce Packet Network: Archtecture and Mechansm, Proc. ACM IGCOMM 92, Aug. 992, pp [] ugh Jamn, cott J.henker, and Peter B.Danzg, Comparson of Measurement-Based Admsson Control Algorthms for Controlled-Load ervce, IEEE INFOCOM 97, vol. 3, Apr. 997, pp [] ugh Jamn, Peter B. Danzg, cott J. henker, and Lxa Zhang, A Measurement-Based Admsson Control Algorthm for Integrated ervce Packet Networks, IEEE/ACM Transactons on Networkng, vol 5, no, Feb. 997, pp [2] L. Klenrock, Queung ystems Volume : Computer Applcatons, John Wley & ons, 974. [3] Ilas Andrkopoulos, George Pavlou, upportng Dfferentated ervces n MPL Networks, IEEE, IWQo 99, 999, pp [4] T.V. Do, G. Echler, H. Hussmann, B. Mamas, C. Prehofer,. alsano, J.J. Tchouto, C. Tttel, P. Todorova, and I.. Veners, ELIA: European Lnkage between Internet Integrated and Dfferentated ervces over ATM, IEEE Workshop, Qo upport for Real Tme Internet Apps, June 2-4, 999. Gyu Myoung Lee was born n Chnju, Korea, n 972. He receved hs B degree n electronc and electrcal engneerng from Hong Ik Unversty, eoul, Korea, n 999 and hs M degree n the chool of Engneerng from ICU (Informaton and Communcatons Unversty, Daejeon, Korea n 2. He s currently a PhD student n the network group, ICU. Hs research nterests nclude sgnalng and control archtecture for IP-based optcal networks and Qobased traffc control technology. Jun Kyun Cho receved hs B degree n electroncs from eoul Natonal Unversty n 982 and M and PhD degrees from KAIT n 985 and 988. He worked for ETRI durng He s currently workng as an Assocate Professor n Informaton and Communcatons Unversty, Daejeon, Korea. Hs research nterests nclude hgh-speed network archtecture and protocol. 54 Gyu Myoung Lee et al. ETRI Journal, Volume 24, Number, February 22

13 Mun Kee Cho receved hs B degree n appled mathematcs from eoul Natonal Unversty n 974 and M degree n ndustral engneerng from KAIT n 978. He obtaned hs PhD degree n operatons research from rth Carolna tate Unversty, n 989. He worked for ETRI ystem Technology Dvson from 978 to 999. He s currently workng as a Professor n the chool of Management, ICU. Hs current research nterests nclude hgh speed network archtectures, busness on networks and GRID technology. Man eop Lee receved hs B and M degrees from Busan Natonal Unversty n 976 and 978 and hs PhD degree from KAIT n 99, all n electrcal engneerng. Durng the perod of 979 to 998, he worked n the area of optcal transmsson technology and served as Drector of Transmsson Technology Department n ETRI. He has been an Assocate Professor n Informaton and Communcatons Unversty (ICU snce 998 and now serves as Drector of Venture Busness Incubaton Center of ICU. Hs research nterests nclude 4Gb/s optcal transmsson technology, PON technology n access area and optcal wreless LAN technology. He s also Charman of the ubcommttee of Transmsson Technology Group n KIC. ang-gug Jong was born n eoul, Korea, 956. He receved hs B and PhD degrees n electroncs engneerng from Kyung Hee Unversty, eoul, Korea, n 98 and 994 and a DE degree n Electroncs Engneerng from Pars 6 Unversty, Pars, France, n 985. From 987 to 2 he worked as the Drector of the Tranng Center of Korea Telecom. nce 2 he has been wth the Telecommuncaton Network Laboratory n KT as Drector. Hs current research nterests nclude hgh speed nternet access, traffc engneerng, and Moble communcatons. ETRI Journal, Volume 24, Number, February 22 Gyu Myoung Lee et al. 55