Modeling Dynamic Requests for Performance Evaluation in Multilayer Networks

1. University of Stuttgart, UST-IKRC. Gauger, D. Sass, M. Köhn, G. Hu, S. Gunreben{gauger,sass,koehn}@ikr.uni-stuttgart.de Modeling of Dynamic Requests for Peformance Evaluation in Multilayer/Multiservice Transport Networks NOBEL WP2 Meeting Berlin

2. Principle Modeling Tasks • Network elements/”structure” • Functionality • Dimensioning • Network behavior/”strategy” • Control • Management • Algorithms everywhere ... • Traffic • Demand • Bandwidth requirements • Total network demand or traffic matrix, e.g. population/growth models • Individual requests NOBEL WP2 Meeting Berlin

3. MotivationDynamic Multilayer/Multiservice Networks • “Multiservice” means potentially very inhomogenous requests • Bandwidth granularity • Transport constraints, e.g. QoS, QoP, … • Multipoint requests: VPNs • “Multilayer” means • More parameters • Mapping at layer boundaries  large number of combinations • Traffic characteristics and dynamics • Well-studied in the packet/telephony world • Few work on new dynamic transport networks and their request attributes NOBEL WP2 Meeting Berlin

4. Traffic Mix 2 Traffic Mix 1 MotivationDynamic Multilayer/Multiservice Networks Just one example: • Impact of composition oftraffic mix regarding STM granularities • Prefer Optical Layer (solid) • Prefer SDH Layer (dashed) • Mix 1: smaller granularities • Mix 2: coarser granularities Significantly different behavior of the ML routing algorithms depending on traffic mix. NOBEL WP2 Meeting Berlin

5. Definitions • Request • Connection • Discrete entity • End-to-end notion on L1, L2, L3 • Characterized by service attributes • Examples • Wavelength path • STM-n connection • MPLS path • VPN • Modeling options • Detailed/realistic models • Try to be as precise/close to real world as possible • Synthetic models • Reasonable and generic • Analysis of their validity and of sensitivity of results NOBEL WP2 Meeting Berlin

6. Objectives • Objectives of this contribution are • Collection and classification of attributes for modeling of individual requests • Systematization for their generation/application in performance evaluation • Objectives of this contribution are NOT • Proposal of new models • Assessment of existing models • List of request attributes is not meant to be complete but to be representative/illustrative wrt/ classification/systematization NOBEL WP2 Meeting Berlin

7. Classification of Modeling Tasksfor Individual Requests • Timing attributes • Arrival instants, holding times • „Physical“ attributes • I.e. they can be "externally" observed, not PHY • Required even for basic studies • End-points , bandwidth (granularity),... • Logical attributes • Additional service constraints beyond pure bit transport • Required only for specific studies • QoS/QoP classes or constraints, client layer requirements... NOBEL WP2 Meeting Berlin

8. Timing Attributes • Arrival instant • Deterministic sequence of arrivals • Stochastic process, e.g. Poisson, renewal/non-renewal, SRD/LRD, ... • Holding time • Infinite • Stochastic process NOBEL WP2 Meeting Berlin

9. „Physical“ attributes • End points (Point2point, Point2multipoint, Multipoint2multipoint) • Deterministic patterns • Two or more stochastic processes (independent/coupled) • Random topologies • Pattern topologies • Bandwidth/granularity and their composition • Fixed • Defined by distributions • Discrete distribution, e.g. share of STM1 | STM-16 | STM64 requests • Continuous distribution, e.g. MPLS path b/w uniform within [a,b] • Description • Absolute, e.g. in Mbps • Relative, e.g. regarding bandwidth of lambda NOBEL WP2 Meeting Berlin

10. Logical attributes • Quality of Service/Quality of Protection • Affiliation to a certain class • Detailed constraints on different layers • Maximum transfer latency • Acceptable reliability of route • Client layer/service attributes NOBEL WP2 Meeting Berlin

11. SystematizationContext of the Attribute • Global • Overall request process defined by one stochastic process • Individual request is chosen from overall process following a splitting process • E.g. single arrival/holding time process for the entire networkmodels intra-day behavior common to all requests • All different classes of requests are coupled by this process • Per class • Several stochastic process for the different classes of requests • Overall request process is superposition of individual processes • E.g. individual arrival/holding time processes for each source/destination pairmodel individual behavior of this class • Different classes of requests are not coupled • Special case: Poisson process/Markov arrivals • Superposition and random splitting again yield a Poisson process • Global == per class NOBEL WP2 Meeting Berlin

12. SystematizationProperties of Stochastic Processes • Stationarity • Stationary invariance wrt/ time shift, models busy hour or long term behavior • Instationary absolute process time is relevant, models intra day variations or growth models • Memory • Memoryless future behavior only depends on current state • With Memory future behavior also depends on past, different degrees of correlation: SRD, LRD • Dependence of different processes/attributes may be relevant • Time e.g. business/residential wrt/ QoP constraints • Location e.g. requests at core nodes have higher b/w granularity • Distance, e.g. in demand profile • Network state e.g. discouraged arrivals, adaptability NOBEL WP2 Meeting Berlin

13. SystematizationRequest Generation • Timing attributes • Depend on client layer traffic variations and TE strategies • Subject to detailed modeling by stochastic processes • Correlation • Instationarity • Global process with splitting or superposition of individual processes • „Physical“ and logical attributes • Working assumption: low dependence on time and state • Modeling by independent and identically distributed (iid) random variables NOBEL WP2 Meeting Berlin

14. GenerationExample Possible generation of requests for an L2 VPN • Global arrival process models according to demand variations • Holding times for an VPN are independentand identically distributed • VPN end points for random hub-and-spokes scenario • Hub is randomly chosen from set of candidate nodes • Number of spokes Ns follows discrete distribution • End points of spokes are chosen randomly from remaining nodes • Link capacity identical on all spokes, chosen from discrete distribution f2(t) f1(t) f3(t) NOBEL WP2 Meeting Berlin

15. ConclusionOutlook • Multilayer/multiservice requests require increased modeling effort • Significant impact of request attributes on ML TE • Classification of request attributes • Systematization of request generation • Models for arrival and holding time processes needed • Long term variation: intra-day/week and growth models • Short term variation: busy period models • Systematic evaluaton of impact on ML TE • Granularity • Multipoint requests NOBEL WP2 Meeting Berlin

16. References • Multilayer routing algorithm proposed by UST-IKR used for evaluations within NOBEL (download: www.ikr.uni-stuttgart.de/en) • Weighted Integrated Routing (WIR) • Necker, Gauger, Bodamer: A New Effcient Integrated Routing Scheme for SDH/SONET-WDM Multilayer Networks, OFC 2003. • Necker: Improving Performance of SDH/SONET-WDM Multilayer Networks using Weighted Integrated Routing, KiVS 2003. • Performance and dimensioning of multilayer networks • Köhn, Gauger: Dimensioning of SDH/WDM Multilayer Networks, 4thITG Workshop on Photonic Networks, Leipzig 2003. • Hülsermann, Bodamer, Barry, Betker, Gauger, Jäger, Köhn, Späth: Network Modelling for a Set of Typical Transport Network Scenarios,5th ITG Workshop on Photonic Networks, Leipzig 2004. NOBEL WP2 Meeting Berlin