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Multi-Layer Traffic Engineering in IP over Optical Networks

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  1. Multi-Layer Traffic Engineering in IP over Optical Networks October 20, 2004 Hung-Ying Tyan Department of Electrical Engineering National Sun Yat-sen University

  2. Outline • IP Network • Transport Network • Traffic Engineering • Some Observations • Multi-Layer Traffic Engineering • Our ML-TE Framework • Our ML-TE Algorithms • Evaluation

  3. IP Network CompanySchoolEnterprise CompanySchoolEnterprise Internet Service Providers (ISP) Carriers router MAN, WAN LAN

  4. OXC OXC OXC OXC OXC OXC OXC IP Network (Optical) Transport Network

  5. OXC OXC OXC OXC OXC OXC OXC IP Network over OTN Conceptual view Data center Actual IP link = circuit

  6. Transport Network • Evolved from traditional telecommunications networks • Good at long distance transmission of digital signal • Technologies • Synchronous Optical Network (SONET) • Wavelength Division Multiplexing (WDM) • Providing long-term circuits between end points • Separation from application networks • Network on network; “overlay network” • Business tiers: carriers vs ISPs

  7. Traffic Engineering (TE) • Mechanisms to allocate network resources according to traffic demand • ISP: Make better use of resources ($$$) • Static: Network planning/provisioning/optimization • Dynamic:Resources allocation adapts to traffic change

  8. Dynamic TE • Basic idea:Move traffic around to alleviate congestion • Why is it effective? • Data traffic can be bursty • Special events occur more frequently in data networks

  9. Observations • ISPs and carriers want to provide better service with less cost • Over-provisioning because of slow response to adding capacity and large variation in traffic demand • Utilization < 25% • Current dynamic TE is still limited • Only deal with congestion

  10. Large Daily Traffic Variation OC-48 link between Dallas and Washington DC

  11. More Observations • “Information Super Highway”? • Distribution channel of electronic information products • Electronic post office

  12. Technology Advances • Control Plane Technology • A separate network dedicated to resources control • Allows resources to be added or released quickly • Optical devices and equipments • Optical laser, receiver, filter etc • Wavelength conversion • Optical add-drop multiplexer (OADM) • Optical cross connect (OXC)

  13. 2 3 2 4 2 4 3 Peak Hours Off-Peak Hours New TE Paradigm – Multi-Layer TE • For ISP, IP links can be leased or released on demand • IP network topology can be changed on demand • Let IP network topology adapt to actual traffic demand

  14. Value proposition • For ISP • OPEX reduction • Simplified network planning • For Carrier • New applications/customers for Carrier Increased (overall) revenues • Improved resource efficiency More revenue from the same resources

  15. OXC OXC OXC OXC OXC OXC OXC Network Model • Two-layer overlay • IP/MPLS network • Optical network • Assume that Optical TE is already available

  16. Input • -Traffic matrix • Physical topology • etc Initial provisioning Network monitoring congestion under-utilization MPLS-TEHybrid path routing Cost down? no yes Activate new IP links Remove idle IP links Our MLTE Framework

  17. Network Monitoring & MPLS-TE IP/MPLS Network 1 2 3 • Monitor outgoing IP links • Detect congestion (if utilization > TH_high) • Detect underutilization ( if utilization < TH_low) • Select target LSPs and notify ingress nodes • Ingress node attempts to re-route LSPs

  18. Hybrid Path Routing • Augmented topology information from optical layer: candidate links • Hybrid path consisting of • Existing IP links • Candidate links • Special cost functionfor both congestionand under-utilization IP/MPLS Network OXC OXC OXC OXC OXC OXC OXC Optical fiber Optical Network

  19. Hybrid Path Routing • Define Network_Cost = sum( Link_Costi ) Link_Costi = F(Link_Utilizationi) x real_link_costi • Algorithm: • Triggered by congested or under-utilized links • Dijkstra’s shortest path • d(link_costi)= F(expected_link_utilizationi) – F(link_utilizationi) • Granting a new route only if it decreases the real network cost F link_utilization UH

  20. LSP Demand High Low 0 8 16 Time (hr) North America Model OXC Seattle Boston Chicago OXC Detroit OXC OXC Denver OXC NYC OXC Cleveland OXC SF DC OXC OXC OXC KansasCity LA OXC Atlanta OXC OXC Dallas Miami OXC 14 Nodes 24 Links (fiber) 193 LSPs

  21. Experiment Results • Simulation tool: J-Sim (www.j-sim.org) • North America Model • 14 nodes, 24 links, 193 LSPs • Average # of IP links ~ 21 • # of IP links at peak demand = 33 • Cost saving ~ 36% v.s. over-provisioning • Tradeoff between cost and number of LSP reroutes

  22. Visualization Tool

  23. Research Topics • ML-TE framework • TE operations • MPLS-TE procedure and Optical-TE • Topology transformation algorithm • Hybrid path routing algorithm • Suitable for both congestion and under-utilization

  24. Thank you! Question?

  25. Basic workflow