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WP 2 - Preparation

WP 2 - Preparation. Planned work for Activity 2.2 (TMCM). Partners: ACIT, SAG, TID, DTAG, AGH, UST-IKR, UPC, POLITO. TID. Reference Scenario for Dynamic Metro-Core Networks TID. Motivation and Objectives

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WP 2 - Preparation

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  1. WP 2 - Preparation Planned work for Activity 2.2 (TMCM) Partners: ACIT, SAG, TID, DTAG, AGH, UST-IKR, UPC, POLITO

  2. TID

  3. Reference Scenario for Dynamic Metro-Core NetworksTID • Motivation and Objectives • Fixed and Mobile Convergent (FMC) metro networks present dynamic traffic patterns. However, network reference scenarios provided in NOBEL I were based on static traffic matrices • IP/OTN functionalities addressed in WP2 are specially well adapted to dynamic environments • A dynamic metro reference scenario will be very useful in order to evaluate the performance of IP/OTN functionalities (Traffic Engineering, L1 pass-through, resilience, etc) and architectures (IP/(Static WDM), RPR, IP/(dynamic OCS), IP/OBS, etc) in dynamic FMC environments • Brief description (i.e methodology) and expected results • Provide a metro-core reference scenario for dynamic metro-core FMC networks • Physical Topology: We can use one of the metro-core reference networks (Madrid and London) described in NOBEL-I • Dynamic traffic Matrix: Different traffic matrices will be obtained at several moments along the day (e.g morning, evening and night). These matrices will be based on fixed and mobile end users characterization Contact person (Fernandez-Palacios, Juan – jpfpg@tid.es – +34913373923). Back up person: Jesús F. Lobo –jflp@tid.es-+349133774452)

  4. Generation dynamic traffic matricesMethodology • 1. Fixed and Mobile Services Requirements • NOBEL-I results + potential extensions • 2. End users normalization • Classification (e.g residential, business) • Users characterization. User types are defined by two main parameters: • PSxy: Probability of using service X at moment Y • PLxy: Probability of being connected to node X at moment Y • Inputs for PS and PL values can be obtained from current scenarios and market surveys • These probabilities can also be considered as percentages of users Contact person (Fernandez-Palacios, Juan – jpfpg@tid.es – +34913373923). Back up person: Jesús F. Lobo –jflp@tid.es-+349133774452

  5. Generation dynamic traffic matricesMethodology • 3. Metro Core Normalization • Node classification: Small, Medium, Big. • This classification can be done according to the number and type of access nodes connected to it. • Inputs from current physical topology • The effective bandwidth in the access node can be obtained from services requirements (defined in step 1) and the number and type of users connected at each moment • Traffic distribution in the metro core network based on the following assumptions: • The percentage of external traffic (to/from) the national backbone is different for real time and best effort traffic and similar to current values • Internal traffic is uniformly distributed among the nodes according to their bandwidth requirements Contact person (Fernandez-Palacios, Juan – jpfpg@tid.es – +34913373923). Back up person: Jesús F. Lobo –jflp@tid.es-+349133774452

  6. Requirements of fixed and mobile services (rate, delay,…) Traffic Matrix Metro Core Nodes Characterization Users Characterization Generation dynamic traffic matricesMethodology NOBEL-I results + potential extensions Operators: Inputs from current scenarios and market surveys Number and type of users connected to the node at each moment The Effective Bandwidth can be obtained from the service requirements and the number and type of users connected at each moment. What effective bandwidth model should we use? • This traffic matrix will be mainly used for comparisons so, as simpler the EB model as better. • Two different EB might be used for real time and best effort traffic External traffic percentage similar to current figures Internal traffic distributed uniformly

  7. Potential Collaboration Activities • Collaboration with other activities • This metro-core reference scenario could be used as input for several WP2 studies: • A2.1&A2.3. Performance analysis of TE and resilience mechanisms in dynamic environments • A2.4. Network Architectures Benchmarking in dynamic metro core networks. One of the main objectives of this reference scenario is to be used in the technoeconomic studies of A2.4. • Collaboration within A2.2 • Traffic models developed in A2.2 can be used to find the aggregated traffic characteristics in the metro-core nodes of the reference scenario. For example: • ACIT: Analysis and modeling of traffic traces • Internal WP2 interaction with activity A2.4 on network solution benchmarking: to dimension networks for benchmarking

  8. Traffic model for control plane dimensioningTID • Motivation and Objectives • Traffic models for control plane dimensioning can be very useful for network planning tasks: • determine the maximum number of nodes per AS in a multidomain networks • Location strategies for edge nodes in AS • An analytical model for signaling traffic dimensioning in GMPLS networks was introduced in NOBEL-I. This model is based on the standard recommendations • Brief description (i.e methodology) and expected results • Extensions of the traffic model introduced in NOBEL-I as well as new performance analysis can be done during NOBEL-II • Parametrization of the traffic models according to the inputs from NOBEL measurement studies • Validation in real measurements • Sensitivity analysis against changes in the packet drop probability. • Analyze to which extent the refresh timer can be increased with no impact in the survivability of RSVP flows. Contact person (Fernandez-Palacios, Juan – jpfpg@tid.esl – +34 91 337 3923. Back up Lobo, Jesus, jflp@tid.es +34 91337 4452.

  9. Potential collaboration activities • Collaboration with WP4 • This traffic model could be used to provide inputs to WP4. • Recommendations for control plane planning • Potential recommendations to standards • Collaboration within A2.2 • T-Systems: Linking Traffic Analysis and Traffic Engineering • Interconnection of traffic analysis and GMPLS based Traffic Engineering • Devise CP-policies and SLA’s • Analysis of control plane protocols -> link to standardization • Investigation of network and service parameters to be exchanged via the CP Contact person (Fernandez-Palacios, Juan – jpfpg@tid.esl – +34 91 337 3923. Back up Lobo, Jesus, jflp@tid.es +34 91337 4452

  10. UPC

  11. TCP connections CharacterizationUPC • Motivation and Objectives • To study the impact of new emerging broadband services on TCP connections statistics • Brief description (i.e methodology) and expected results • Starting from real traffic traces captured by UPC, we will investigate both the IAT (InterArrival Time) and HT (Holding Time) statistics for the TCP connections. Contact person Spadaro, Salvatore- spadaro@tsc.upc.edu . back-up contact person: Sanchez, Sergi – sergio@ac.upc.edu – +34938967767

  12. TCP connections CharacterizationUPC • Identify links with other partners proposals • UST-UKR • Identify interactions among other WPs • Potential link with the WP3 Contact person Spadaro, Salvatore- spadaro@tsc.upc.edu . back-up contact person: Sanchez, Sergi – sergio@ac.upc.edu – +34938967767

  13. Multi-fractality of high-aggregated trafficUPC, Siemens • Motivation and Objectives • Contribution on studies on the impact of the different applications on the multi-fractality behavior • Brief description (i.e methodology) and expected results • During the NOBEL phase I, a joint study between Siemens and UPC concludes that the real traffic traces (high-aggregated traffic) provided by UPC show a multi-fractal behaviour. The aim of this work during the NOBEL Phase II will be to investigate the impact of the different applications on this multi-fractal behavior in order to identify those applications which determine the deviation from the poissonian behavior. Starting from the UPC real traffic traces some well-known tests (as we do in NOBEL phase I) will be applied to obtain the traffic characteristics. Contact person Spadaro, Salvatore- spadaro@tsc.upc.edu . back-up contact person: Sanchez, Sergi – sergio@ac.upc.edu – +34938967767

  14. Multi-fractality of high-aggregated trafficUPC, Siemens • Identify links with other partners proposals • Siemens, ACIT • Identify interactions among other WPs • WP2 and WP3 for simulation studies Contact person (Surname, Name – e-mail – phone number – fax number – mobile). If possible indicate a back-up contact person.

  15. ACIT

  16. Analysis and modelling of traffic tracesAlcatel CIT • Motivation and Objectives • In Nobel phase 1, we already analysed a traffic traces measured by the University of Stuttgart in terms of variations of the Coefficient of Variation (CoV) according to the time scale of observation. This first analysis helped us to better understand the behaviour of traffic at different time scale. This is important in network dimensioning (TE, tech.-eco. studies & architecture comparisons), • Additional analysis on new traces is required to consolidate and enrich the results we obtained. New focus on the aggregation process of different flows will be done. • Brief description (i.e methodology) and expected results • Flow-level analyze of traffic traces measured by some WP2 partners. • Depending on the nature of the data from the traces, we can analyze characteristics at the level of the aggregation of flows, or at each individual flow level (for that point, we need to have at least an indication on the source and destination addresses). • The output of the analysis will be the values of the variations of CoV at different time scale, for the aggregation of flows and/or for each individual flows. With individual flow information, we could derive rules for the aggregation process according to the time scale of observation (independence? spatial correlation?) • Comparison of the obtained results with some existing models with eventually some adaptation to fit with the real traffic traces Ludovic NOIRIE – ludovic.noirie@alcatel.fr – tel.: +33-1-69-63-11-36 – fax : +33-1-69-63-18-65 (other contact: Dominique.Verchere@alcatel.fr )

  17. Analysis and modelling of traffic tracesAlcatel CIT • Identify links with other partners proposals • Link with some partners to get traffic traces (UST-IKR, UCP, ... ?) • Interaction with UST-IKR on the activities "Traffic Flow Modeling" and "FLow/Stream Characterisation" • some synergy between ACIT analysis at individual flow level and UST-IKR analysis of "micro-flow/stream" • Interaction with UPC/Siemens on the "multi-fractality of high-aggregated traffic" • Analysis of the link between this "multi-fractal" concept and the concept based on CoV analysis ? • Identify interactions among other WPs • Internal WP2 interaction with activityA2.4 on network solution benchmarking: to dimension networks for benchmarking • No interaction foreseen with other WPs. • Comments, ideas and suggestions for • Increase the cooperation among NOBEL partners • Audio-conferences between concerned partners on specific topics / activities Ludovic NOIRIE – ludovic.noirie@alcatel.fr – tel.: +33-1-69-63-11-36 – fax : +33-1-69-63-18-65 (other contact: Dominique.Verchere@alcatel.fr )

  18. Siemens

  19. Multifractal Traffic Analysis and ModellingSiemens • Motivation and Objectives • Traffic analysis of the UPC traces found in NOBEL phase 1 shows that highly aggregated Internet traffic, analysed in a timeframe of nanoseconds, cannot be described with a Poisson-like process, but are characterised by multifractal behaviour. • Therefore, it is necessary to provide multifractal traffic generators for a realistic modelling of traffic on metro and core networks. • It is also important to analyse the reason(s) why aggregated Internet traffic shows this multifractal characteristic. • Brief description (i.e methodology) and expected results • Design and definition of a multifractal traffic generator. • Analysis of possible reasons for multifractal behaviour of aggregated Internet traffic on very small timescales. • Definition of requirements on Performance Monitoring for Multilayer Traffic Engineering functionality and the evaluation of multilayer performance measurement concepts. Measurements and Monitoring are used to analyze the recent state of the links and nodes in the network; this state information is used to determine optimized routes and minimal required network resources for the traffic in such a way that the required QoS is respected. The main objective of this activity is to provide this state information to the route and resource management. Robert Pleich, Robert.Pleich@siemens.com, Back-up contact: Miguel de Vega Rodrigo, mdevegar@ulb.ac.be

  20. Multifractal Traffic Analysis and ModellingSiemens • Identify links with other partners proposals • To be filled after the Act. Leaders send back to WP2 list all the proposals with some guidelines • Cooperation with NOBEL partners providing traffic traces (UPC, Uni Stuttgart, AGH) • Identify interactions among other WPs • Traffic models and load generators may be used in WP1 and WP5 as well as for simulations in WP 2 and WP 3. • Comments, ideas and suggestions • … Robert Pleich, Robert.Pleich@siemens.com, Back-up contact: Miguel de Vega Rodrigo, mdevegar@ulb.ac.be

  21. AGH

  22. Investigation of nonlinear effects in traffic characterizationAGH/UST-IKR?/Siemens/UPC • Motivation and Objectives • Many of the current works on traffic characterization focus solely on linear methods • Possible non-linear effects are either not recognized or ignored • Nature of non-linearities (if any) is not known so its impact on model behaviour can not be assessed • Brief description (i.e methodology) and expected results • Assessing by statistical and non-linear time series analysis-based methods if we need to go further than linear description. • Considering different classes of non-linear models (threshold, static, dynamic, chaotic, stochastic) • Answering (at least partially) the question about impact of non-linearities on some well-known network parameters (mean queue length etc.) Contact person (Wajda, Krzysztof – wajda@kt.agh.edu.pl – tel.+48126173638 – fax.+48126342372 – +48501413416). Back-up contact:Piotr.Zuraniewski@agh.edu.pl

  23. Investigation of nonlinear effects in traffic characterizationAGH/UST-IKR?/Siemens/UPC • Identify links with other partners proposals • Possible cooperation with UPC and Siemens on multifractal analysis is foreseen • …. • .. • Identify interactions among other WPs • Possible interactions with WP3 (Burst/packet networks) and WP1 (Architectures) • …. • Comments, ideas and suggestions for • Recently some authors claimed multifractal behaviour might be a spurious effect due to imperfectness of statistical tools used. • It is necessary to continue work done within NOBEL, phase 1, towards constructing database (set) of traffic traces, representative for specific network areas (such as LAN, MAN, core) • AOB • … Contact person (Wajda, Krzysztof – wajda@kt.agh.edu.pl – tel.+48126173638 – fax.+48126342372 – +48501413416). Back-up contact:Piotr.Zuraniewski@agh.edu.pl

  24. UST-IKR

  25. Traffic Sampling methodologiesUST-IKR • Motivation and Objectives • Full traffic measurements are not practical/feasible for TE and for operation due to huge data volumes and processing burden/latency • Numerous sampeling methods exist for monitoring traffic at network nodes for calculating status/statistics • Yet, identification and validation of appropriate method still depends heavily on aspects like requirements of ML TE/RM/CP and abstraction level (packet, flow, stream...) • Brief description (i.e methodology) and expected results • Analysis of the requirements and performance of key sampling methods • Comparison and evaluation studies of the methods against different traces • Identification of suitable sampling methods for monitoring network/link state in NOBEL network scenarios • Results will support an appropriate choice of sampling/monitoring methods in network nodes for RM/TE tasks Contact person: Sass, Detlef; sass@ikr.uni-stuttgart.de, +49 711 685 – 7965, back-up: Gunreben, Sebastian; gunreben@ikr.uni-stuttgart.de

  26. Traffic Sampling methodologiesUST-IKR • Identify links with other partners proposals • Currently no partners work on this topic • DTAG (T-Systems): ->Investigation of network and service parameters to be exchanged via the CP • Identify interactions within WP2 • Within WP2 with Activity A2.1 RM: for identification of input requirements for ML TE • Identify interactions among others WPs • With WP4: for identification of requirements for CP Contact person: Sass, Detlef; sass@ikr.uni-stuttgart.de, +49 711 685 – 7965, back-up: Gunreben, Sebastian; gunreben@ikr.uni-stuttgart.de

  27. Traffic Flow ModelingUST-IKR • Motivation and Objectives • For simulation purpose in network planning phase and for RE/RM studies packet level models are seldom well suited. • Traffic models on flow level (from TCP/UDP flows to traffic streams exchanged between n/w nodes) provide adjustable and manageable trade-off between details and abstraction • Flexible, parsimonious and manageable models are desired reflecting only those characteristics relevant for NOBEL network scenarios • Brief description (i.e methodology) and expected results • Ongoing studies of traffic models, implementation of selected traffic models for the simulation environment IKRSimLib • Validation of selected models by parameterization to NOBEL traces (already captured and new) and validation of their characteristics to traces • Selected models can be used for further RM/RE/WP1 studies or building realistic traffic generators (e.g. for WP5) Contact person: Sass, Detlef; sass@ikr.uni-stuttgart.de, +49 711 685 – 7965, back-up: Gunreben, Sebastian; gunreben@ikr.uni-stuttgart.de

  28. Traffic Flow ModelingUST-IKR • Identify links with other partners proposals • ACIT flow modeling (potential model comparison) • Identify interactions within WP2 • A2.1 RM, A2.3 RE: for dynamic simulation studies • Identify interactions among other WPs • Selected models could be used in WP1 or WP5 Contact person: Sass, Detlef; sass@ikr.uni-stuttgart.de, +49 711 685 – 7965, back-up: Gunreben, Sebastian; gunreben@ikr.uni-stuttgart.de

  29. Flow/Stream CharacterizationUST-IKR • Motivation and Objectives • Understanding the traffic characteristics is crucial for network planning and also for design of ML TE function and RM mechanisms • Flow (micro-flow) or stream characteristics can provide insight at the appropriate abstraction level and working level (granularity) for network planning and design of TE functions • Brief description (i.e methodology) and expected results • Characterisation of measurement data regarding different abstraction levels: • Flow level, i.e. micro-flow defined by a 5-tupel, e.g. TPC, UDP flows • Stream level, e.g traffic exchanged between two host/(sub)networks/nodes • Studies will be performed to elaborate characteristics of single flows/streams, i.e. characteristics within a flow/stream and entire flow/stream characteristics • Traces from NOBEL1 and newly captured data as well as data from other locations will be investigated • Characterisation results of flows/streams can be used for modelling and for RM & RE studies Contact person: Sass, Detlef; sass@ikr.uni-stuttgart.de, +49 711 685 – 7965, back-up: Gunreben, Sebastian; gunreben@ikr.uni-stuttgart.de

  30. Flow/Stream Characterization UST-IKR • Identify links with other partners proposals • ACIT, UPC also on the topic flow/(stream) characetrisation • Identify interactions within other WP2 • A2.1 RM, A2.3 RE: for dynamic simulation studies • A2.4: network benchmarking (?) Contact person: Sass, Detlef; sass@ikr.uni-stuttgart.de, +49 711 685 – 7965, back-up: Gunreben, Sebastian; gunreben@ikr.uni-stuttgart.de

  31. T-Systems (DTAG)

  32. Linking Traffic Analysis and Traffic Engineering T-Systems • Motivation and Objectives • Provisioning of an interface between traffic analysis (WP2) and traffic engineering in multi-layer networks (WP4) with GMPLS • Devise policies for the operation of the NMP and CP based on TE protocols (RSVP-TE etc.) • Brief description (i.e methodology) and expected results • Identification of points / Network Elements where Traffic Engineering is most suitable • Interconnection of traffic analysis and GMPLS based Traffic Engineering • Devise CP-policies and SLA’s • Analysis of control plane protocols -> link to standardization • Investigation of network and service parameters to be exchanged via the CP • Continuation of traffic analysis and characterization of future services Contact person (Dueser, Michael, michael.dueser@t-systems.com)

  33. Linking Traffic Analysis and Traffic EngineeringT-Systems • Identify links with other partners proposals • IKR, Acreo, AGH in traffic characterization and traffic modelling • Identify interactions among other WPs • WP4: Linking traffic characterization and service requirements with CP policies • …. Contact person (Dueser, Michael, michael.dueser@t-systems.com)

  34. Politecnico di Torino (POLITO)

  35. Passive traffic characterizationPolitecnico di Torino - POLITO • Motivation and Objectives • Analysis of real traffic traces allows to better understand problems and expectations user have of future networks • Passive analysis is not intrusive • Not only packet level, but also transport (TCP) and upper layers are important • Multimedia traffic will be important • Brief description (i.e methodology) and expected results • At Polito we developed a tool to perform traffic analysis, called TSTAT http://tstat.tlc.polito.it • It allows to passively monitor IP network, deriving measurement indexes at both network (IP) layer, and transport (TCP/UDP) layer. • Persistent monitoring to track changes in traffic pattern • We are currently extending TSTAT t support MULTIMEDIA traffic characterization • We did in the past traffic characterization based on TSTAT, see http://tstat.tlc.polito.it/publications.php Contact person: Marco Mellia: mellia@tlc.polito.it, http://www.tlc.polito.it/mellia

  36. Passive traffic characterizationPolitecnico di Torino - POLITO • Identify links with other partners proposals • This activity fits really well in the Traffic Measurement Characterization and Modelling (Detlef, sass@ikr.uni-stuttgart.de) • We look forward to extend the use of TSTAT among partners, as well as integrating measurement indexes that partners are interested into Contact person (Surname, Name – e-mail – phone number – fax number – mobile). If possible indicate a back-up contact person.

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