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TRAFFIC MODELLING IMEC contributions Sofie Verbrugge Koen Casier Sophie De Maesschalck Outline • Traffic trace generator • A pan-European traffic forecast • Multi-operator scenario 2 Outline • Traffic trace generator • A pan-European traffic forecast • Multi-operator scenario 3 Goal • Generate a sequence of requests • According to predefined distribution • Request specify duration of connection and required capacity • Does not cover packet level 4 Overview Topology begin & end-time Traffic Trace Generator Traffic Trace IAT (distribution) HT (distribution) Capacity (distribution) 5 Distributions • Multiple types of distributions available • IAT, HT and capacity specified by means of distributions HT IAT HT HT IAT Connection IAT = time between consecutive connection HT = time between setup and teardown of connection 6 Outline • Traffic trace generator • A pan-European traffic forecast • Multi-operator scenario 7 Basic Reference Topology Oslo Stockholm Glasgow Copenhagen Dublin # nodes 28 # lines 41 Node degree London Warsaw Amsterdam Hamburg Berlin Frankfurt Brussels Paris Strasbourg Munich Zurich Prague Vienna Lyon Bordeaux Milan Madrid Barcelona Node connectivity Budapest Belgrade Zagreb Rome Line connectivity min 2 max 5 avg 2.93 min 2 max 4 avg 2.33 min 2 max 4 avg 2.46 Athens 8 Modified Topologies I Oslo Oslo Stockholm Stockholm Glasgow Glasgow Copenhagen Copenhagen Dublin Dublin London London Berlin Frankfurt Brussels Paris Strasbourg Munich Zurich Lyon Bordeaux Milan Madrid Warsaw Amsterdam Hamburg Barcelona Paris Strasbourg Munich Zurich Budapest Vienna Belgrade Milan Zagreb Madrid Rome 28 nodes , 34 links av. node degree = 2.43 Prague Vienna Budapest Lyon Bordeaux Athens Ring Topology Berlin Frankfurt Brussels Prague Warsaw Amsterdam Hamburg Barcelona Belgrade Zagreb Rome Athens Basic Topology Triangular Topology 28 nodes , 41 links av. node degree = 2.93 28 nodes , 61 links av. node degree = 4.36 9 Traffic Model Traffic* between cities i and j – Voice traffic: Kv * Pi * Pj / Dij2 – Transaction data traffic: Kt * Ei * Ej / Dij – Internet traffic: Ki * Hi * Hj Pi: population region city i E: number of non-production business employees H: number of Internet hosts Dij: distance between city i and city j Kv, Kt and Ki: traffic constants to be determined based on the total volume of respectively voice, transaction data and IP traffic and the parameters Pi, Pj, Dij, Hi and Hj. *Model from Corning (Dwivedi, Wagner, “ Traffic model for USA long distance optical network”, OFC’00) 10 Traffic Demand Forecast Traffic forecast (Tbps) 20 18 16 14 12 10 8 6 4 2 0 Internet traffic Transaction data traffic Voice traffic 2001 2002 2003 2004 2005 2006 Year • Traffic pattern changes over the years due to – different growth rate (IP becomes dominant) – different distance-dependency of the three considered traffic types (voice, transaction data and IP) 11 Modified Topologies II Helsinki Oslo Stockholm Glasgow Copenhagen Dublin London Amsterdam Hamburg London Berlin Frankfurt Brussels Paris Strasbourg Munich Zurich Warsaw Amsterdam Hamburg Berlin Brussels Dusseldorf Frankfurt Prague Prague Paris Strasbourg Munich Zurich Lyon Bordeaux Milan Marseilles Vienna Lyon Milan Birmingham Zagreb Vienna Krakow Budapest Zagreb Belgrade Sofia Rome Lisbon Madrid Barcelona Rome Seville Athens Palermo Core Topology 16 nodes , 23 links av. node degree = 2.88 Basic Topology 28 nodes , 41 links av. node degree = 2.93 Large Topology 37 nodes , 57 links av. node degree = 3.08 12 Outline • Traffic trace generator • A pan-European traffic forecast • Multi-operator scenario 13 Multi-operator topologies Helsinki Oslo Stockholm Glasgow Copenhagen Dublin Birmingham London Warsaw Amsterdam Hamburg Berlin Brussels Dusseldorf Frankfurt Prague Paris Strasbourg Munich Zurich Lyon Bordeaux Milan Marseilles Vienna Krakow Budapest Zagreb Belgrade Sofia Lisbon Madrid Barcelona Rome Seville Athens Palermo Operator 1 Operator 3 Operator 2 14 Multi-operator traffic • Distribution of traffic over different network operators based on e.g. market share Operator 1 Operator 2 Operator 3 Market share X1 % X2 % X3 % Operator 1 X1 % X1 * X 1 % X1 * X 2 % X1 * X 3 % Operator 2 X2 % X2 * X 1 % X2 * X 2 % X2 * X 3 % Operator 3 X3 % X3 * X 1 % X3 * X 2 % X3 * X 3 % X3*X1 % of total traffic goes from network of operator 3 to network of operator 1 X1*X3 % of total traffic goes from network of operator 1 to network of operator 3 X3*X3 % of total traffic stays within network of operator 3 • E.g., X1 = 18%, X2 = 22%, X3 = 15% 15 Multi-operator traffic • Not all operators have a node in all considered cities Market share in served cities will have to be modified 1. Increase market share in served cities, proportionally with overall market share or 2. Modify market share according to set of predefined rules (e.g., certain operator traditionally very strong is certain part of Europe) 16 Multi-operator traffic • According to set of predefined rules Amsterdam Athens Barcelona Belgrade Berlin Birmingham Bordeaux Brussels Budapest Copenhagen Dublin Düsseldorf Frankfurt Glasgow Hamburg Helsinki Krakau Lissabon London Operator 1 (18%) Operator 2 (22%) Operator 3 (15%) 25 29 10 0 0 55 20 20 20 0 13 42 25 25 0 15 40 0 20 15 0 25 29 10 20 13 30 30 25 0 0 from Belgrade2 50 0 Traffic to Budapest3 = 25 30 0 13%20* 30%* of total25traffic from Belgrade 20 0 50 0 20 20 10 50 0 0 30 0 30 0 40 0 20 40 10 to Budapest 17 Conclusions IMEC contributions to traffic modelling • Traffic trace generator – Generate sequence of request – According to distributions • A pan-European traffic forecast – Reference topology – Traffic forecast model – http://www.ibcn.intec.ugent.be/projects/IST-FP5/NRS/index.html • Multi-operator scenario – Divide traffic among operators according to market share 18 Questions??
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