Improving the Efficiency of Dynamic Service Provisioning in Transport Networks with Scheduled Services

Transcription

1 Improving he Efficiency of Dynamic Service Provisioning in Transpor Neworks wih Scheduled Services Ralf Hülsermann, Monika Jäger and Andreas Gladisch Technologiezenrum, T-Sysems, Goslarer Ufer 35, D-1585 Berlin, Germany {ralf.huelsermann, monika.jaeger, Absrac This paper describes a new model for dynamic leased-line services in ranspor neworks. In difference o oher Poissonian-like raffic and service models, he allocaion of nework resources is possible wihin ime slos, and no randomly. This resuls in nework reconfiguraions a fixed poins of imes, which can also be simpler for he nework managemen and operaion compared o coninuous nework reconfiguraions. Furhermore, he concep of noificaing connecion requess in advance was analysed wihin his conex. Boh conceps have been evaluaed by simulaion and i could be shown, ha he efficiency of dynamic service provisioning can be improved by scheduling services. 1 Inroducion Dynamic resource allocaion in ranspor neworks is expeced o increase he nework resource uilizaion due o saisical muliplexing effecs. Furhermore dynamic leased line services wih shor minimum holding imes and shor provisioning imes could be aracive for new cusomers who don need a permanen saic connecion. The efficiency of dynamic resource allocaion srongly depends on he number of cliens in he nework. A small number of raffic sources, and hence a small number of available nework resources will resul in a small saisical gain. The connecion use, i.e. he raio of he holding ime and he iner-arrival ime also has o be small enough. Wih hese resricions, he poenial of dynamic services in eiher small neworks or wih a small number of services (e.g. when inroducing a new service) is expeced o be limied [2]. A commonly used model for dynamic raffic sources in connecion oriened s is he Poisson model. This model is characerized by a exponenial disribuion of he iner-arrival imes and assumes a ime independen behaviour of he clien. The mean amoun of raffic generaed by his model is consan in ime. The absracion of he characerisics of dynamic raffic sources wih he Poisson model is an idealizaion. The ime independency of a single source is no realisic. In case of blocked reques he iner-arrival ime will be shorened (re-dialling). Furhermore, he offered raffic will no be consan in ime. In he following secions we describe a proposal for a new service model. The model can help o improve he performance of dynamic neworks by offering a sloed access o he shared nework resources. Furhermore he possibiliy o pre-plan nework resources is given. This mechanism can be used o handle raffic peaks. 2 Model Descripion The Poisson raffic model wih immediae service provisioning afer he arrival of a service reques resuls in random access o he nework resources. This can be compared o he ALOHA concep [1]. The user mus no mee any boundary condiions and can deliver his requess a random imes. If only one uni of nework resource is aken ino accoun, he ime inerval in which conflics in resource access can occur is wice as long as he holding ime [Figure 1]. This is differen in case of sloed resource access. The access o he nework resources is resriced o fixed imes, which resuls in a ime raser, where iner-arrival and holding imes have o fi in. Wih his, he iner-arrival and holding imes are no longer coninuous random numbers bu raher discree. The ime inerval, where blockings of requess can occur, is reduced o he holding ime [Figure 1]. Figure 1: Random vs. sloed access

2 The parameer of his model is he size of he imeslos. Shorer ime slos will offer more flexibiliy o he clien, bu will also lead o less blocking probabiliy decrease obained wih he sloed access. Hence, when making he slos shorer and shorer he nework performance will finally coincide wih he performance in case of random resource accesses. Provisioning of ime slos for nework resource occupaion means also a simplificaion in nework operaion. While random access resuls in coninuous nework reconfiguraions, he nework says saic wihin he slos. Enhancing his model wih an noificaion of he connecion requess in advance leads o he concep of scheduled services. For his he swiching of connecions will no occur immediaely afer he arrival of reques. For his, he raffic model is enhanced by an addiional parameer, he in advance noificaion ime [Figure 2]. The ime beween a service reques and he connecion swiching is no resriced o be zero as in he previously described service models. service reques T IAT T Advanced Noificaion service sar service reques Figure 2: : raffic model parameers Wih his concep he nework operaor is able o preplan he nework resource reservaion. The preplanning allows also a move of he realizaion of a specific reques in ime, if a he designaed service sar ime connecion swiching is no possible. Wihou his move hose requess ge blocked. The disadvanage of his mechanism is he delay ime which will be added o he service sar ime [Figure 3]. This delay can be heoreically negaive or posiive, bu in pracice he service realizaion will happen in mos cases afer he designaed sar ime, because he probabiliy o find free resources in laer imeslos is higher. T Service Delay T Service Delay esimaed peak raffic rae. This resuls in overdimensioning of he nework for he res of he ime. If he operaor has he abiliy o shif service requess away from he busy hour, he raffic peaks can be smoohed and he over-dimensioning of he nework can be reduced. The delay of connecion requess premises, ha he clien is willing o accep he delay. For high-prioriy, ime-sensiive services his migh no be he case. Bu on he oher hand his parameer allows a service differeniaion and he price of a service can also be adjused. As he lengh of he in advance noificaion ime has a significan influence on he operaor s abiliy in preplanning he nework configuraion and wih his on he nework performance, his parameer may also be refleced in he price of he service. 3 Evaluaion of he Concep 3.1 Nework Scenario The concep was evaluaed by nework simulaion. For his a model of a nework node was implemened which performs connecion rouing and nework resource reservaion auomaically. Dynamic alernae rouing is applied, which means ha shores available pahs will be compued. The connecion requess are generaed by a raffic source model. I is implemened as a Poisson raffic model, generaing exponenially disribued random numbers for he connecion inerarrival- and holding imes. In case of a sloed access o he nework resources he iner-arrival and holding imes are resriced o discree numbers, which fi ino he raser. The in advance noificaion ime is also exponenially disribued and is mean value is assumed o be equal o he connecion iner-arrival ime. The maximum delay ime in case of he scheduled services is resriced o he mean inerarrival ime. The granulariy of a connecion reques is one channel. The number of channels on he links in he nework fis wih he saic dimensioning. Norden Hamburg Bremen Hannover Berlin service reques service available service sar service available Figure 3: Movemen of service realizaion in ime Essen Düsseldorf Dormund Köln Frankfur Leipzig The disribuion of he amoun of offered raffic in ime will no be consan for real raffic sources. As in elephone neworks a cerain imes raffic peaks are expeced. To avoid an unaccepable high level of blocking probabiliy during he busy hour, he nework should be dimensioned wih regards o he Mannheim Nürnberg Karlsruhe Sugar Ulm München Figure 4: Reference Nework Scenario

3 A hypoheic German Transpor Nework was used for he case sudies [Figure 4] [3]. The nework includes 17 nodes and 26 links and has an average nodal degree of 3.6. Two differen raffic marices have been applied. The firs one includes a oal amoun of 121 demand unis; he second one reduces he oal amoun of raffic o 96 demand unis. In he following hese wo s are called he exension and he inroducion. The nework dimensioning was done following a shores pah rouing for a saic raffic marix. The nework-wide disribuion of dynamic raffic sources was assumed o be proporional o he raffic disribuion in he raffic marices. 3.2 Case Sudy Resuls Nework Performance In his secion he dynamic nework performance when applying scheduled services is compared o he conceps of random and sloed resource access. Figure 5 depics he blocking probabiliy agains he amoun of offered load for he inroducion. In case of random access a level of 3% blocking is observed a 51% offered load. This indicaes low saisical muliplexing effecs due o he small amoun of raffic sources and nework resources in his. The applicaion of he sloed access improves he nework performance slighly. For his, he mean iner-arrival ime is divided ino 1 slos. Now he level of 3% blocking probabiliy corresponds o an offered load of 68%. The gain in he amoun of ranspored raffic is abou 33%. The nework uilizaion also increases from 52% up o 66% [Figure 6]. Blocking Probabiliy [%] 1 1 Sloed access Figure 5: Blocking Probabiliy in he inroducion A significan improvemen of he blocking probabiliy was observed when applying scheduled services. Up o a level of 91% offered load, he blocking probabiliy is below 3%. This leads o a gain in he amoun of ranspored raffic of 78% compared o he wih random resource access. The nework load increases nearly linearly up o a level of 9% offered load [Figure 6]. The break afer he linear phase corresponds o he srong increase of he blocking probabiliy in he range of 85%-95% offered load. Similar rouing insabiliy effecs have been observed in dynamic mulilayer neworks [4]. For he scheduled services he avoidance of blockings can happen in wo ways: Firs by choosing longer pahs and second by moving he connecion esablishmen in ime. Boh sraegies are limied: alernae rouing is resriced o he available nework resources; he delay of connecion requess is limied by he maximum delay ime. Following hese mechanism he overload is firs spread over he nework and afer his he nework load wihin fuure imeslos will increase. Nework Load [%] Sloed access Figure 6: Resuling nework load in he inroducion Figure 7 illusraes his effec relaed o he mean service delay. Up o a level of 8% offered load he mean delay ime is always below 5% of he mean iner-arrival ime. Beween 8% and 9% offered load he delay increases srongly up o 2% of he mean iner-arrival ime. Mean Queuing Delay / Mean Inerarrival Time [%] Inroducion Scenario Exension Scenario Figure 7: Mean service delay applying he scheduled services concep In he exension he saisical muliplexing is much higher han in he inroducion. The level of 3% blocking probabiliy in case of random resource access is observed a 89% offered load [Figure 8]. Wih his, he improvemen of he nework

4 performance when applying sloed access or scheduled services will be much smaller. The gain of ranspored raffic a a level of 3% blocking probabiliy in case of sloed access is 9% compared o he random access. For he scheduled services his gain is 14%. The increase of he nework load is nearly linear up o % for he scheduled services [Figure 9]. Due o his srong increase of he nework load he rouing insabiliy effecs are more significan han in he inroducion. In case of he scheduled services he service delay ime is negligible up o 9% offered load. If he offered load reaches a level beween 9% and 95% he delay increases firmly up o 2% of he mean iner-arrival ime [Figure 7]. Blocking Probabiliy [%] 1 1 Sloed access Figure 8: Blocking Probabiliy in he exension Nework Load [%] Sloed access Figure 9: Resuling nework load in he exension Smoohing of raffic peaks As described in he firs secion of he paper he concep of scheduled services can be used o handle raffic peaks. Figure 1 illusraes he bursy raffic paern which was applied o he inroducion. The basic raffic rae was se o 3% offered load, he maximum peak raffic rae o 221% offered load. The inerarrival ime beween he raffic burss is consan, he raffic wihin a burs follows he sandard normal disribuion wih a variance of.3. In case of random access wihou scheduling of connecion requess he blocking probabiliy is unaccepably high for he peak load. To fulfill he requiremens of he cusomers regarding he qualiy of he service, he nework has o be over-dimensioned. If he concep of scheduled services is applied, nearly no blockings will occur. The nework load is spread over ime and all service requess wihin he peak can be served. Nework Load [%] Blocking Probabiliy [%] Off. Load [%] Scheduled Services Random Access Scheduled Services Random Access Time [ime-unis] Figure 1: Applicaion of bursy raffic paern: Blocking probabiliy and nework uilizaion 4 Conclusions A new model for dynamic services has been presened in his paper. The concep was evaluaed by differen case sudies and i could be shown, ha he efficiency of dynamic service provisioning in s wih a small number of cliens and limied nework resources can be significanly improved. Those s are seen as poenial migraion and inroducion s. Pre-planning of nework resource reservaion can help o reduce he nework overdimensioning if raffic peaks are expeced. Furher invesigaions will focus on he analysis of price models for scheduled services and he possibiliy o differeniae he service qualiy.

5 5 Acknowledgemen The work repored in his paper has been suppored by he European Commission wihin he IST-NOBEL projec. The auhors are responsible for he conen of he paper. 6 References [1] A. S. Tannenbaum: Compuernezwerke, Pearson Sudium, 2. [2] R. Huelsermann, M. Jaeger: Dynamic Service Provisioning in Auomaically Swiched Opical Neworks, ONDM 24, Ghen, 24. [3] R. Hülsermann, S. Bodamer, M. Barry, A. Beker, C. Gauger, M. Jäger, M. Köhn, J. Späh: A Se of Typical Transpor Nework Scenarios for Nework Modelling, 5 h ITG-Workshop on Phoonic Neworks, Leipzig 24. [4] S. Bodamer, J. Späh, C. Glingener: Avoiding Rouing Insabiliies in ASTN-Based Muli- Layer Transpor Neworks, ECOC 24, Sockholm, 24.