DiffServ/MPLS Network Design and Management

1. DiffServ/MPLS Network Design and Management Doctoral Dissertation Tricha Anjali Broadband and Wireless Networking Laboratory Advisor: Dr. Ian F. Akyildiz

2. Contents • Introduction • Network Management • TEAM Structure • LSP/lSP Setup • Traffic Routing • Available Bandwidth Estimation • End-to-end Available Bandwidth Measurement • Inter-domain Management • TEAM Implementation • Conclusions • Future Work

3. Goals • Two-fold which are complementary: • Guarantee Quality of Service for the required applications. • Use the network resources efficiently.

4. MultiProtocol Label Switching • Explicitly routed point-to-point paths called Label Switched Paths (LSPs) • Support for traffic engineering and fast reroute • Simpler switching operations

5. src dest Generalized MPLS • GMPLS is a set of protocols for a common control of packet and wavelength domains • Reserve a wavelength on a path (Lambda Switched Path or lSP) for an aggregation of flows

6. DiffServ + GMPLS • DiffServ • Scalable service differentiation • DiffServ + GMPLS • Class differentiation for QoS provisioning • Traffic Engineering for DiffServ classes for efficient use of resources

7. Network Model MPLS Networks Link: Label Switched Path (LSP) Class Type 0 (BE) Class Type 1 (AF) Class Type 2 (EF) Wavelength Network Link: lambda Switched Path (lSP) Optical Network Link: fiber

8. 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

9. 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

10. A New Network Management Tool • Traffic Engineering Automated Manager (TEAM) • Automated • Monitors the network performance • Implements various algorithms for handling events in MPLS and optical network • Allows efficient use of resources and prompt responses

11. Traffic Engineering Automated Manager Big Picture of TEAM Simulation Tool (ST) Management Plane DiffServ/ GMPLS Domain Traffic Engineering Tool (TET) LSP/lSP Setup/ Dimensioning Resource LSP Preemption Route LSP Routing Measurement/ Performance Evaluation Tool (MPET) Traffic Routing Network Dimensioning and Topology Design TEAM To neighboring TEAM

12. LSP and lSP Setup Problem • “Optimal Policy for LSP Setup in MPLS Networks,” Computer Networks Journal, June 2002 • “LSP and lSP Setup in GMPLS Networks,” Proceedings of IEEE INFOCOM, March 2004 Find an adaptive traffic driven policy for dynamic setup and tear-down of LSPs and SPs. Why not the fully connected topology? Too many LSPs for increasing number of routers N (N2 problem) Why not a fixed topology? Because traffic is unpredictable

13. LSP and lSP Setup Problem • Arrival of bandwidth request • Decision among: • Option 1: no action • Option 2: setup a direct LSP • Option 3: setup a direct lSP and LSP 1 dest 2 src 3

14. LSP and lSP Setup • Optical network virtual topology design algorithms • Chen 1995, Davis 2001, Krishnaswamy 2001: Design the network off-line with a given traffic matrix • Gençata 2003 : On-line virtual topology adaptation approach for optical networks • Does not combine optical and MPLS layers

15. Assumptions • Routing Assumption • Default topologies • Packets are routed either on • the direct LSP(i,j) or • the min-hop path P(i,j) over the default MPLS network • LSPs are routed either on • the direct lSP or • the min-hop path Plij over the default optical network • a new LSP can not be routed on a previously established non-default lSP

16. Model Formulation • Events and Decision Instants • MPLS network • Arrival/Departure of bandwidth requests between (i, j) • Optical network • Arrival of LSP(i, j) capacity increment/decrement requests

17. Model Formulation • State vector (local) • MPLS network s = (A, Bl, Bp) • Available capacity (A) • Bandwidth requests on direct LSP (Bl) or on min-hop path (Bp) • Optical network s = (A, Bl, Bp, k) • Available capacity (A) • Capacity requests on direct lSP (Bl) or on min-hop path (Bp) • Number of lSPs between the node pair (k)

18. Action Variables MPLS network Optical network Model Formulation (Contd.)

19. Cost Model Incremental cost W = Wb + Wsw+ Wsign • Wb(s,a) : Bandwidth cost • Wsw(s,a) : Switching cost • Wsign(s,a) : Signaling cost if LSP/lSP is set-up or re-dimensioned • Wb and Wsw are linear with respect to the bandwidth request and time • Wsign is incurred only if the decision is a = 1

20. Optimal Setup Policy • Based on Markov Decision Process Theory • Minimize expected infinite-horizon discounted total cost • Determine transition probabilities and optimality equations • Solve the optimality equations with value iteration algorithm Optimal policy stationary control-limit

21. Optimization (MPLS network) Optimal policy * such that Optimality equations where

22. Optimal Policy (MPLS Network) where

23. Optimization (Optical Network) Optimal policy * such that Optimality equations where

24. Optimal Policy (Optical Network) where

25. Sub-optimal Policy • Optimal policy is difficult to pre-calculate because of large number of possible system states • Sub-optimal policy that is fast and easy to calculate • Minimizes the cost incurred between two decision instants • Maintains the threshold structure of the optimal policy

26. Sub-optimal Policy (MPLS) where where

27. Sub-optimal Policy (Optical) where

28. Performance Evaluation Example network: • Network has 10nodes and 17links • Cph = 1000 Mbps • Diameter = length of longest shortest path = 3

29. Comparison Discounted total cost vs. Initial state Discount factor=0.5 Discount factor=0.1

30. Experimental Results What happens when we homogeneously increase traffic on selected node pairs • LSPs with larger number of default LSPs in their path are established first • lSPs with larger number of default lSPs that need re-dimensioning in their path are established first

31. Heuristics for Comparison Heuristic 1: Fully connected LSP network Heuristic 2: LSP re-dimensioned exactly Heuristic 3: LSP re-dimensioned with extra capacity In each heuristic, lSP network is fully connected

32. Total Expected Cost

33. Bandwidth Wastage in MPLS Network

34. Traffic Engineering Automated Manager Big Picture of TEAM Simulation Tool (ST) Management Plane DiffServ/ GMPLS Domain Traffic Engineering Tool (TET) LSP/lSP Setup/ Dimensioning Resource LSP Preemption Route LSP Routing Measurement/ Performance Evaluation Tool (MPET) Traffic Routing Network Dimensioning and Topology Design TEAM To neighboring TEAM

35. QoS Routing • “A New Path Selection Algorithm for MPLS Networks Based on Available Bandwidth Estimation,” Proceedings of QoFIS, October 2002 • “Traffic Routing in MPLS Networks Based on QoS Estimation and Forecast,”submitted Find a low cost feasible path for routing traffic flows in MPLS networks adaptively. Why adaptive? Because MPLS network topology is changing Existing routing algorithms • Heuristic solutions of the delay constrained least cost problem • LSP routing algorithms (MIRA, PBR)

36. Routing Algorithm • Notations • puv: path in the MPLS network • puv= (lux, …, lzv) • Alij/dlij: Available capacity/delay on lij • npuv: Number of LSPs in puv

37. Cost Model LSP cost W = Wb + Wsw+ Wsign+WAB+Wd • Wb and Wsw linear with respect to the bandwidth request and duration of request • Wsign is instantaneous • WAB is inversely related to LSP available bandwidth • Wd linear with respect to delay on the LSP Path cost Wp = ∑ LSP costs + (n-1) ( Relay node cost )

38. Routing Problem Find the path such that subject to feasibility constraints

39. Routing Algorithm • Heuristic of the exact problem • Path set size restricted to F • Set populated by paths with increasing length • Feasibility check • Cost comparison

40. Partial Information • Estimation algorithm for accurate state information • Linear prediction • Dynamically change the number of past samples based on prediction performance

41. Performance Evaluation Popular ISP topology with link capacity = 155 c.u.

42. Rejection Ratio

43. Minimum Available Bandwidth

44. Paths with Relay Nodes

45. Traffic Engineering Automated Manager Big Picture of TEAM Simulation Tool (ST) Management Plane DiffServ/ GMPLS Domain Traffic Engineering Tool (TET) LSP/lSP Setup/ Dimensioning Resource LSP Preemption Route LSP Routing Measurement/ Performance Evaluation Tool (MPET) Traffic Routing Network Dimensioning and Topology Design TEAM To neighboring TEAM

46. Available Bandwidth Measurement • “ABEst: An Available Bandwidth Estimator within an Autonomous System,” Proceedings of IEEE Globecom, November 2002 • “MABE: A New Method for Available Bandwidth Estimation in an MPLS Network,”Proceedings of IEEE NETWORKS, August 2002 Measure/estimate the available bandwidth in a link/path to analyze the performance of the network Various existing tools to measure narrow link capacity • Pathchar based (Jacobson 1997) : link-by-link measurement • Packet pair based (Keshav 1991): end-to-end capacity • Nettimer (Lai 2001) : end-to-end 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): end-to-end available bandwidth measurement

47. 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)]

48. 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

49. 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.

50. 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