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IP WORLD

IP WORLD. Commonly agreed that IP should be the future multi-service networking technology…  But this is not our “father’s” IP TECHNOLOGY… Maybe we can call it “ IP on STEROIDS” or “ATMized IP”. “FATHER’S” IP. Based on Datagram Data Traffic

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IP WORLD

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  1. IP WORLD Commonly agreed that IP should be the future multi-service networking technology…  But this is not our “father’s” IP TECHNOLOGY… Maybe we can call it “IP on STEROIDS” or “ATMized IP”

  2. “FATHER’S” IP • Based on Datagram • Data Traffic • “Best Effort” Traffic Paradigm • QoS is NON-EXISTENT

  3. “FATHER’s” IP Question: How can F-IP take care of new multi-media applications that demand QoS?

  4. Key Applications • Web + Mail • Large File Transfers • Interactive Access • Streaming Audio/Video • Games • Many More…

  5. Key Question Shall we MANAGE BANDWIDTH or NOT for QoS?

  6. Just Keep Adding Bandwidth? (No BW Management) Bandwidth management is not needed unless (until) congestion occurs. • Looks like adding bandwidth gives premium service with minimal complexity and management burden.

  7. VISION OF SOME IP FOLKS NO BW MANAGEMENT, i.e.,  OVER-PROVISION/OVER-DIMENSION!!! i.e., whenever and how much BW is needed just keep adding/assigning new BW.

  8. NO BW MANAGEMENT Maybe this was viable in the beginning!!! * The Internet boom was peaking, with many Service Providers and Enterprises were flushed with FUNDS.. * The additional bandwidth was perceived as cheaper than QoS deployment. However,  Not economically efficient!!

  9. Managed Bandwidth Advantages • Constant resources and No BW management  No QoS can be guaranteed !!! • WAN bandwidth is very expensive compared to the LAN (and WAN service pricing is not necessarily driven by open market factors). • IT managers and service providers need to squeeze as much functionality out of WAN BW as possible.

  10. Managed Bandwidth Advantages • Traffic growth models fall apart when new “killer” applications appear • Mission critical applications must get required network resources consistently, even when competing with such applications.

  11. Result FOR THE SAKE of QoS in IP BANDWIDTH MUST BE MANAGED!!!

  12. QoS EveryWhere • QoS in Wireless LANs • QoS in Sensor Networks • QoS in Wireless Ad-Hoc Networks • QoS in P2P Networks • QoS in Access Networks • QoS in Optical Networks • QoS in VoIP • QoS in Wireless Mesh Networks • QoS in 2.5 G (GPRS) Systems • QoS in 3G (UMTS/IMT2000) Systems

  13. Plethora of QoS Conferences • IWQoS (94: Aachen, 95: Brisbane, 96: Paris, 97: NY, 98: Napa/CA, 99: London, 00: Pittsburgh, 01: Karlsruhe, 02: Miami, 03: Montreal, 04: Monterrey) • QofIS (01: Coimbra; 02 Berlin; 03: Stockholm; 04: Barcelona) • QoSIP (01: Roma; 03: Milano; 05: Catania)

  14. Providing QoS in IP Networks Back to USSR !! (ATM)  “IP on Steroids” or “ATMized IP” The ATM Framework for QoS under new nomen-clatura is being RECREATED!!!

  15. Examples for RECREATION??Service Level Parameters • Traffic Parameters: (Peak Rate, Peak Bucket Size, Sustainable Rate, Sustainable Bucket Size, Max Allowed Packet Size) • QoS Classes: (Guaranteed Service, CL Service, BE Service) • QoS Requirements: (Loss Ratio, Transfer Delay, Delay Variation)

  16. IP QoS Overview • Best Effort (No State) (Original IP Service) • IntServ/RSVP (Per Flow State) First efforts at IP QoS • DiffServ (Aggregated State) (Seeking Simplicity and Scalability) • IntServ + DiffServ + Traffic Engineering (BW Optimization; E2E SLAs) • Static DiffServ + MPLS

  17. IntServ + DiffServ Complimentary: • IntServ provides a guaranteed traffic delivery • DiffServ enables better QoS scalability Together, they form a robust QoS deployment. Diff Serv functionalities are Class Based WeightedFair Queueing (CBWFQ) and the Committed Access Rate (CAR)

  18. IntServ + DiffServ+ Traffic Engineering • End-to-end IntServ does not scale well because it requires per-flow state on every router • DiffServ does not provide enough granularity for classifying applications • Solution: • Use DiffServ at the network core • Use IntServ at the edges • Apply traffic engineering principles and admission controls to ensure end-to-end quality.

  19. Static DiffServ + MPLS Flows are grouped into Class-Types and fast forwarding using label switching • DiffServ-aware Traffic Engineering • Traffic belonging to the same class is aggregated and routed on a dedicated virtual tunnel (LSP)  Difficult to dimension the LSPs efficiently

  20. Our Approach:Measurement based Adaptive DiffServ MPLS NetworkingNSF, NASA, Swayles (2000-2004) • Capacity allocation is adapted using measurements, not based on traffic descriptors. • Statistical Multiplexing is achieved among flows belonging to the same class. • Efficient use of resources and provisioning of QoS monitored on the need

  21. Traffic Engineering Automated Manager (TEAM) C. Scoglio, T. Anjali, J. Cavalcante, I. F. Akyildiz, G. Uhl, “TEAM: A Traffic Engineering Automated Manager for DiffServ based MPLS Networks“, IEEE Communications, Oct’04. Measurement/ Performance Evaluation Tool (MPET) DiffServ/MPLS Domain Management Plane LSP Setup Traffic Engineering Tool (TET) Resource LSP Dimensioning LSP Preemption Route LSP Routing Traffic Routing Simulation Tool (ST) Network Dimensioning and Topology Design To neighboring TEAM

  22. Simulation Tool (ST) Management Plane LSP Setup/ Dimensioning Resource Traffic Engineering Tool (TET) LSP Capacity Allocation LSP Preemption Route Gigabit Ethernet LSP Routing Measurement/ Performance Evaluation Tool (MPET) 7204 VXR (rtr2) ATM 622 Mbps Fast Ethernet 7505 (rtr3) TEAM Catalyst 6506 Lightstream 1010 Catalyst 4000 ATM 155 Mbps External Lightstream MPLS NetworkDimensioning 7204 VXR (rtr1) Traffic Engineering Automated Manager (TEAM)

  23. TESTBED NETWORK TOPOLOGY NASA Goddard Abilene BWN-Lab

  24. MPLS Network Management • Existing MPLS Network Management Tools: • RATES (Bell Labs, 2000): • Sets up bandwidth guaranteed LSPs • Does not support DiffServ • No performance measurement and analysis • DISCMAN (EURESCOM, 2000): • Provides test and analysis results of DiffServ and MPLS-based DiffServ • Does not provide its own management system functionality

  25. MPLS Network Management • Other existing MPLS Network Management Tools: • MATE (Bell Labs, Univ. Michigan, Caltech, Fujitsu, 2001): • The goal is to distribute the traffic across several LSPs established between a given ingress and egress node pair • Not for traffic that requires bandwidth reservation • TEQUILA (European Union Project, 2002): • Global and integrated approach to network design and management • No network management methods developed and implemented • No evaluation of performances

  26. TEAM Components • Traffic Engineering Tool • LSP Setup • LSP Dimensioning • LSP Preemption • LSP Routing • Measurement Tool • Available Bandwidth Measurement

  27. LSP Setup/Dimensioning LSP Setup Problem: A new LSP setup request arrives • When to setup a new direct LSP? • When to re-dimension an existing LSP? • When to route the traffic on the hop-by-hop IP route?

  28. LSP Setup/Dimensioning Related work: Very little attention was given to this problem • S. Uhlig and O. Bonaventure, “On the cost of using MPLS for interdomain traffic,” in Proc. of QoFIS’00. • An LSP is established whenever the number of bytes forwarded within one minute exceeds a threshold • Very high signaling costs and high control efforts for variable and bursty traffic

  29. LSP Setup/Dimensioning C. Scoglio, T. Anjali, J. de Oliveira, I. Akyildiz, and G. Uhl, “A New Threshold-Based Policy for Label Switched Path Setup in MPLS Networks,” Proc. of ITC 2001, Salvador, Brazil, Dec. 2001.Also in Computer Networks Journal (Elsevier), 2002. Find an Adaptive Traffic DrivenPolicy for Dynamic Setup, Teardown and Dimensioning of LSPs • Based on Markov Decision Process theory • Objective Function: • Minimize the expected infinite-horizon discounted total cost • To determine the optimal policy,  the transition probabilities and the optimality equations • The optimality equations are solved using the Value Iteration Algorithm.

  30. LSP Setup/Dimensioning Novelty in our approach: • Online threshold-based traffic driven policy which takes into account bandwidth, switching, and signaling costs

  31. TEAM Components • Traffic Engineering Tool • LSP Setup • LSP Dimensioning • LSP Preemption • LSP Routing • Measurement Tool • Available Bandwidth Measurement

  32. LSP Preemption Problem: • An LSP with higher priority can preempt an LSP with lower priority if there is a competition for resources. • The preempted LSP may be rerouted. Which LSP(s) to preempt?

  33. LSP Preemption Related Work: • M. Peyravian and A. Kshemkalyani, “Decentralized Network Connection Preemption Algorithms,” Computer Networks, June 1998 • Optimizes preemption criteria in a given order of importance • F. Le Faucheur et al., “Requirements for Support of DiffServ-Aware MPLS Traffic Engineering,” IETF Internet Draft, March 2003 • The need for preemption is stressed but no policy is defined • BWN-Lab Testbed preemption experiments: • Commercial policy purely based on priority and tunnel age

  34. LSP PreemptionJ. de Oliveira, C. Scoglio, I. Akyildiz, and G. Uhl,“A New Preemption Policy for DiffServ-Aware Traffic Engineering to Minimize Rerouting,”Proc. of IEEE INFOCOM 2002.Also in IEEE/ACM Transactions on Networking, August 2004 • An LSP with higher priority can preemptan LSP with lower priority if there is a competition for resources • Versatile preemption policy complemented with an adaptive scheme which can reduce the need for LSP rerouting

  35. LSP Preemption • Non-real time applications may afford to have their transmission rate reduced • By reducing the rate in a fair fashion: • These LSPs would not be torn down, • There would be no service disruption, extra setup and tear down signaling • THERE WOULD BE NO REROUTING DECISIONS

  36. LSP Preemption • Combines the three main preemption criteria: • Priority of preempted LSPs • Number of preempted LSPs • Bandwidth of preempted LSPs • Optimization formulation and heuristic

  37. TEAM Components • Traffic Engineering Tool • LSP Setup • LSP Dimensioning • LSP Preemption • LSP Routing • Measurement Tool • Available Bandwidth Measurement

  38. LSP Routing Algorithm • LSP Routing Problem: • Setting up bandwidth guaranteed LSPs, where LSP setup requests arrive individually, and future requests are not known a-priori

  39. LSP Routing Algorithm • Related work: • K. Kar, M. Kodialam, and T. Lakshman, “Minimum Interference Routing of Bandwidth Guaranteed Tunnels with MPLS Traffic Engineering Application,” IEEE INFOCOM 2000 and IEEE JSAC,Dec.2000 • MIRA – Tries to minimize interference between different routes in a network for specific set of ingress-egress nodes • Shortcomings: Computation burden (maxflow), uses much longer paths, cannot estimate interference for a cluster of nodes • Not very likely to be implemented by vendors due to complexity

  40. LSP Routing Algorithm J. de Oliveira, F. Martinelli, and C. Scoglio,“SPeCRA: A Stochastic Performance Comparison Routing Algorithm for LSP Setup in MPLS Networks,” IEEE Globecom, Taiwan, Nov. 2002. • Simple algorithm: • uses simple known routing schemes; • no assumptions about incoming traffic; • non-stationary traffic • At each interval, the algorithm evaluates the best routing algorithm for the measured current traffic load

  41. TEAM Components • Traffic Engineering Tool • LSP Setup • LSP Dimensioning • LSP Preemption • LSP Routing • Measurement Tool • Available Bandwidth Measurement

  42. Bandwidth Measurement Measure/estimate the available bandwidth in a link/path to analyze the performance of the network Existing tools to measure link capacity: • Pathchar based (Jacobson 1997):Link-by-Link Measurement • Packet Pair based (Keshav 1991):E2E Capacity • Nettimer (Lai 2001):E2E Capacity • AMP (NLANR 2002):Active link-by-link Measurement • OCXmon (NLANR 2002):Passive link-by-link Measurement • MRTG (Oetiker 2000):5 min averages of link utilization • Pathload (Jain 2002):E2E available BW Measurement

  43. Available Bandwidth Estimator (ABEst) Anjali, C. Scoglio, L. Chen, I. Akyildiz, and G. Uhl,“ABEst: An Available Bandwidth Estimator within an Autonomous System,” Proc. of IEEE Globecom, November 2002 • MRTG • Monitor the traffic load on network links • Highly portable SNMP based tool • Provides only 5 min averages of link utilization • MRTG++ • Our modification to MRTG to reduce the averaging time to 10 sec • MRTG++ is used to poll the network devices with 10 sec granularity T.

  44. Available Bandwidth Estimator • Assumptions • SNMP is enabled in the domain • MRTG++ is used to poll the network devices with 10 sec granularity • Notations • L(t) : Traffic load at time t •  :Length of averaging interval of MRTG++ • L[k] :Average load in [(k-1), k] • p : Number of past measurements in prediction • h : Number of future samples reliably predicted • Ah[k] : Available bandwidth estimate for [(k+1), (k+h)]

  45. k-p+1 k k+h ABEst (Contd.) • We use the past p samples to predict the utilization for the next h samples • Utilize the covariance method for prediction • Values of p and h varied according to the estimation error

  46. ABEst (Contd.) • At time instant k, available bandwidth measurement is desired. • Find the vectors wa, a[1,h] using covariance method given p and the previous measurements. • Find and • Predict Ah[k] for [(k+1), (k+h)t]. • At time (k+h)t, get • Find the error vector • Set k = k+h. • Obtain new values for p and h. • Go to step 1.

  47. ABEst (Contd.) • Covariance estimated as • Covariance normal equations • Ah[k] estimated • Either C – max{predicted utilization vector} • Or C – Effective bandwidth from the utilization vector

  48. ABEst (Contd.) • Algorithm for h and p • If s/m > Th1, decrease h until hmin and increase p till pmax multiplicatively • If Th1 > s/m > Th2, decrease h until hmin and increase p till pmax additively • If s/m < Th2, then: • If m> Th3*M2E, decrease h until hmin and increase p till pmax additively • If Th3*M2E > m > Th4*M2E, keep h and p constant • If m < Th4*M2E, increase h and decrease p till pmin additively

  49. Performance Evaluation hmin=10

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