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DiffServ/MPLS Network Design and Management Doctoral Dissertation Tricha Anjali Broadband and Wireless Networking Laboratory Advisor: Dr. Ian F. Akyildiz 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 March 30, 2004 BWN Lab - Tricha Anjali 2 Goals • Two-fold which are complementary: – Guarantee Quality of Service for the required applications. – Use the network resources efficiently. March 30, 2004 BWN Lab - Tricha Anjali 3 MultiProtocol Label Switching • Explicitly routed point-to-point paths called Label Switched Paths (LSPs) • Support for traffic engineering and fast reroute • Simpler switching operations March 30, 2004 BWN Lab - Tricha Anjali 4 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 src March 30, 2004 dest BWN Lab - Tricha Anjali 5 DiffServ + GMPLS • DiffServ – Scalable service differentiation • DiffServ + GMPLS – Class differentiation for QoS provisioning – Traffic Engineering for DiffServ classes for efficient use of resources March 30, 2004 BWN Lab - Tricha Anjali 6 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 March 30, 2004 BWN Lab - Tricha Anjali 7 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 MPLSbased DiffServ ✘ Does not provide its own management system functionality March 30, 2004 BWN Lab - Tricha Anjali 8 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 March 30, 2004 BWN Lab - Tricha Anjali 9 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 March 30, 2004 BWN Lab - Tricha Anjali 10 Big Picture of TEAM Traffic Engineering Automated Manager Simulation Tool (ST) Management Plane LSP/lSP Setup/ Dimensioning Traffic Engineering Tool (TET) DiffServ/ GMPLS Domain LSP Preemption LSP Routing Traffic Routing Measurement/ Performance Evaluation Tool (MPET) TEAM Network Dimensioning and Topology Design To neighboring TEAM March 30, 2004 BWN Lab - Tricha Anjali 11 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 lSPs. 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 March 30, 2004 BWN Lab - Tricha Anjali 12 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 March 30, 2004 BWN Lab - Tricha Anjali 13 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 March 30, 2004 BWN Lab - Tricha Anjali 14 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 March 30, 2004 BWN Lab - Tricha Anjali 15 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 March 30, 2004 BWN Lab - Tricha Anjali 16 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) March 30, 2004 BWN Lab - Tricha Anjali 17 Model Formulation (Contd.) Action Variables MPLS network 1 a 0 Optical network 1 a 0 March 30, 2004 setup or re-dimension LSP no action on LSP setup or re-dimension lSP no action on lSP BWN Lab - Tricha Anjali 18 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 March 30, 2004 BWN Lab - Tricha Anjali 19 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 March 30, 2004 stationary control-limit BWN Lab - Tricha Anjali 20 Optimization (MPLS network) tm v ( S0 ) E S0 e m 0 tm1 ( t tm ) Wsign (Sm , a) e wb (Sm , a) wsw (Sm , a) dt tm Optimal policy * such that Optimality equations where March 30, 2004 * v ( s) inf v ( s) l v( S ) min r ( S , a) aA l r ( S , a) Wsign ( S , a) q ( j | S , a ) v ( j ) _ jS wb ( S , a) wsw ( S , a) l BWN Lab - Tricha Anjali 21 Optimal Policy (MPLS Network) * {d * , d * , d * , } 0 * a A, BL , BP , 0 * d * a A, BL , BP ,1 a* A, B , B ,1 L P 0 where S A, BL , BP , 0 for A b S A, BL , BP , 0 for A b S A, BL , BP ,1 S A, BL , BP , 2 S A, BL , BP ,3 l * * 1 c h c v A , B , B 2 b ,3 v 0, BL BP b, b,3 s a L P a* A, BL , BP ,0 otherwise 0 1 cs hl ca v* A b, BL b, BP b,3 v* 0, BL BP b, b,3 a A, BL , BP ,1 otherwise 0 * March 30, 2004 BWN Lab - Tricha Anjali 22 Optimization (Optical Network) tm v ( S0 ) E S0 e m 0 tm1 ( t tm ) Wsign (Sm , a) e wb (Sm , a) wsw (Sm , a) dt tm Optimal policy * such that Optimality equations where March 30, 2004 * v ( s) inf v ( s) l v( S ) min r ( S , a) aA l q ( j | S , a ) v ( j ) _ jS l l w ( S , a ) w l sw ( S , a) r ( S , a) Wsign ( S , a) b l BWN Lab - Tricha Anjali 23 Optimal Policy (Optical Network) * {d * , d * , d * , } a* 0 0 * d 1 0 1 where S 0, 0, B F , 0, 0 S 0, 0, B F , 0,1 S Al , B l , B F , k , 0 k 0, Al b B F S Al , B l , B F , k , 0 k 0, Al b B F S Al , B l , B F , k ,1 k 0, Al W b B F S Al , B l , B F , k ,1 k 0, Al W b B F F c Wh cap F v* 0, 0, B F 2b, 0,1 v* W B F 2b, B F 2b, 0,1,1 1 cx c y h * ' ' a l 0 otherwise March 30, 2004 BWN Lab - Tricha Anjali 24 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 March 30, 2004 BWN Lab - Tricha Anjali 25 Sub-optimal Policy (MPLS) # {d # , d # , d # , } 0 1 a A, BL , BP , 0 1 d # a A, BL , BP ,1 a1 A, B , B ,1 L P 0 1 BP b BTh a1 A, BL , BP ,0 0 otherwise where March 30, 2004 BTh where S A, BL , BP , 0 for A b S A, BL , BP , 0 for A b S A, BL , BP ,1 S A, BL , BP , 2 S A, BL , BP ,3 1 BP BTh a1 A, BL , BP ,1 0 otherwise cs h ca l h 1 cip cmpls BWN Lab - Tricha Anjali 26 Sub-optimal Policy (Optical) # {d # , d # , d # , } a1 0 0 * d 1 0 1 where S 0, 0, B F , 0, 0 S 0, 0, B F , 0,1 S Al , B l , B F , k , 0 k 0, Al b B F S Al , B l , B F , k , 0 k 0, Al b B F S Al , B l , B F , k ,1 k 0, Al W b B F S Al , B l , B F , k ,1 k 0, Al W b B F (cx c y h F )( l ' ' ) ccapWh F F 1 B b 1 a (h F 1)(cl copt ) otherwise 0 March 30, 2004 BWN Lab - Tricha Anjali 27 Performance Evaluation Example network: • Network has 10 nodes and 17 links • Cph = 1000 Mbps • Diameter = length of longest shortest path = 3 March 30, 2004 BWN Lab - Tricha Anjali 28 Comparison Discounted total cost vs. Initial state 450 1600 Optimal Sub-optimal [2,4,0] [9,2,0] 300 [9,2,0] [8,4,0] Expected Total Cost 350 [3,3,1] 250 [4,2,0] [3,3,1] [4,2,0] 200 150 [2,6,0] [1,5,1] [2,6,0] [1,5,1] 100 [1,1,7] [0,1,2] 50 [3,8,0] [7,3,0] 0 [1,0,0] [1,1,1] [2,5,1] [1,5,1] [1,5,5] [1,5,7] [1,5,10] [1,10,7] Initial State Discount factor=0.5 March 30, 2004 Optimal Sub-optimal [1,5,3] [1,10,6] 1400 Expected Total Cost 400 1200 [4,16,0] 1000 [1,5,7] [9,8,0] [1,5,11] 800 [1,14,0] [8,12,0] [2,13,3] [0,7,4] [8,10,0] 600 [7,13,1] [9,11,0] [4,13,0] [10,10,0] 400 200 [7,13,0] 0 [1,0,0] [1,1,1] [3,5,1] [1,5,1] [1,5,5] [1,5,7] [1,5,10] [1,10,7] Initial State Discount factor=0.1 BWN Lab - Tricha Anjali 29 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 March 30, 2004 BWN Lab - Tricha Anjali 30 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 March 30, 2004 BWN Lab - Tricha Anjali 31 Total Expected Cost Optimal Sub-optimal Heuristic 1 Heuristic 2 Heuristic 3 1200 Expected Total cost 1000 800 600 400 200 0 March 30, 2004 2 4 6 Experiment number BWN Lab - Tricha Anjali 8 10 32 Bandwidth Wastage in MPLS Network Max bandwidth wastage in LSPs (Mbps) 1000 March 30, 2004 Sub-optimal Heuristic 1 Heuristic 2 Heuristic 3 800 600 400 200 0 2 4 6 Experiment number BWN Lab - Tricha Anjali 8 10 33 Big Picture of TEAM Traffic Engineering Automated Manager Simulation Tool (ST) Management Plane LSP/lSP Setup/ Dimensioning Traffic Engineering Tool (TET) DiffServ/ GMPLS Domain LSP Preemption LSP Routing Traffic Routing Measurement/ Performance Evaluation Tool (MPET) TEAM Network Dimensioning and Topology Design To neighboring TEAM March 30, 2004 BWN Lab - Tricha Anjali 34 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) March 30, 2004 BWN Lab - Tricha Anjali 35 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 p l – Auv min Aij lij puv – duv p March 30, 2004 lij puv dijl BWN Lab - Tricha Anjali 36 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 ) March 30, 2004 BWN Lab - Tricha Anjali 37 Routing Problem Find the path such that p* uv * uv p :W min W puv p uv subject to feasibility constraints n k, p* uv d p* uv d max , A Amin . p* uv March 30, 2004 BWN Lab - Tricha Anjali 38 Routing Algorithm • Heuristic of the exact problem • Path set size restricted to F • Set populated by paths with increasing length • Feasibility check • Cost comparison March 30, 2004 BWN Lab - Tricha Anjali 39 Partial Information • Estimation algorithm for accurate state information • Linear prediction • Dynamically change the number of past samples based on prediction performance March 30, 2004 BWN Lab - Tricha Anjali 40 Performance Evaluation Popular ISP topology with link capacity = 155 c.u. March 30, 2004 BWN Lab - Tricha Anjali 41 Rejection Ratio SP Proposed 0.35 Percentage 0.3 0.25 0.2 0.15 0.1 0.05 0 0 March 30, 2004 5 10 15 Experiment BWN Lab - Tricha Anjali 20 25 42 Minimum Available Bandwidth 70 SP Proposed Capacity units 60 50 40 30 20 10 0 0 March 30, 2004 5 10 15 Experiment BWN Lab - Tricha Anjali 20 25 43 Paths with Relay Nodes 80 SP Proposed Number 60 40 20 0 0 March 30, 2004 5 10 15 Experiment BWN Lab - Tricha Anjali 20 25 44 Big Picture of TEAM Traffic Engineering Automated Manager Simulation Tool (ST) Management Plane LSP/lSP Setup/ Dimensioning Traffic Engineering Tool (TET) DiffServ/ GMPLS Domain LSP Preemption LSP Routing Traffic Routing Measurement/ Performance Evaluation Tool (MPET) TEAM Network Dimensioning and Topology Design To neighboring TEAM March 30, 2004 BWN Lab - Tricha Anjali 45 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 March 30, 2004 BWN Lab - Tricha Anjali 46 Available Bandwidth Estimator • Assumptions – SNMP is enabled in the domain – MRTG++ is used to poll the network devices with 10 sec granularity • Notations – – – – – – March 30, 2004 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)] BWN Lab - Tricha Anjali 47 ABEst (Contd.) k-p+1 k k+h • 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 March 30, 2004 BWN Lab - Tricha Anjali 48 ABEst (Contd.) 1. At time instant k, available bandwidth measurement is desired. 2. Find the vectors wa, a[1,h] using covariance method given p and the previous measurements. 3. Find Lˆ [k 1], 4. Predict Ah[k] for [(k+1), (k+h)t]. 5. At time (k+h)t, get , Lˆ [k h] T and L [k p 1], 6. L [k 1], , L [k h] T T Find the error vector e [k 1], , e [k h] 7. Set k = k+h. 8. Obtain new values for p and h. 9. Go to step 1. March 30, 2004 BWN Lab - Tricha Anjali , L [k ] T 49 ABEst (Contd.) • Covariance estimated as rL (n, m) k i k N p L [i n]L [i m] • Covariance normal equations rL (0, 0) rL ( p 1, 0) wa (0) rL (0, a) rL (0, p 1) r (1, a) w (1) a L rL ( p 1, p 1) w ( p 1) r ( p 1, a ) a L • Ah[k] estimated – Either C – max{predicted utilization vector} – Or C – Effective bandwidth from the utilization vector March 30, 2004 BWN Lab - Tricha Anjali 50 ABEst (Contd.) • Algorithm for h and p – If s/ > Th1, decrease h until hmin and increase p till pmax multiplicatively – If Th1 > s/ > Th2, decrease h until hmin and increase p till pmax additively – If s/ < Th2, then: • • • March 30, 2004 If > Th3*M2E, decrease h until hmin and increase p till pmax additively If Th3*M2E > > Th4*M2E, keep h and p constant If < Th4*M2E, increase h and decrease p till pmin additively BWN Lab - Tricha Anjali 51 Performance Evaluation hmin=10 40 Actual Peak-bw Est. Eff-bw Est. Bandwidth (MB/s) 35 30 25 20 15 10 200 March 30, 2004 300 400 Sample number 500 BWN Lab - Tricha Anjali 600 52 Performance Evaluation (Contd.) hmin=20 40 Actual Peak-bw Est. Eff-bw Est. Bandwidth (MB/s) 35 30 25 20 15 10 200 March 30, 2004 300 400 Sample number 500 BWN Lab - Tricha Anjali 600 53 End-to-end AB Measurement - “TEMB: Tool for End-to-End Measurement of Available Bandwidth,” Proceedings of IEEE ELMAR, June 2003 • Motivation – Combine active and passive approaches – Most tools estimate narrow link capacity – Accuracy – Scalability – Statistical robustness – Not intrusive March 30, 2004 BWN Lab - Tricha Anjali 54 Tight Link Identification • Measurement packets Version Type Length Checksum Data Record (optional) Data Record (optional) • 10 measurement packets sent in a second, to make the tool nonintrusive March 30, 2004 BWN Lab - Tricha Anjali 55 Data Record • Data record IP address Counter Timestamp Speed • Inserted/modified by the hops of the path • Counter information from MIB-II in router March 30, 2004 BWN Lab - Tricha Anjali 56 Example of Auto-detection 0 0 24 0 0 24 checksum checksum A.1.1.1 A.1.1.1 3245 3272 234563 234568 10000000 10000000 0 0 8 checksum S March 30, 2004 D.1.1.1 B.1.1.1 A.1.1.1 0 0 40 checksum A.1.1.1 3245 234563 10000000 C.1.1.1 23487 54236 10000000 C.1.1.1 BWN Lab - Tricha Anjali 0 0 40 checksum A.1.1.1 3272 234568 10000000 C.1.1.1 23498 54245 10000000 D 57 Example of Non-min-hop Path 0 1 72 checksum B.1.1.1 0 0 0 D.1.1.1 0 0 0 C.1.1.1 0 0 0 S March 30, 2004 0 1 72 checksum B.1.1.1 3245 234563 10000000 D.1.1.1 0 0 0 C.1.1.1 0 0 0 0 1 72 checksum B.1.1.1 3245 234563 10000000 D.1.1.1 23487 54236 100000000 C.1.1.1 0 0 0 0 1 72 checksum B.1.1.1 3245 234563 10000000 D.1.1.1 23487 54236 100000000 C.1.1.1 5324586 43214 10000000 D.1.1.1 B.1.1.1 A.1.1.1 C.1.1.1 BWN Lab - Tricha Anjali D 58 Tight Link Identification • 10 packets in one second • N packets back at source for analysis • Utilization of I-th interface at time tk cIk cI ( k 1) U Ik for k 2,3, , N tk t( k 1) • Available bandwidth AIk SI U Ik • At least agreelink of the estimates should concur about the tight link identity. March 30, 2004 BWN Lab - Tricha Anjali 59 Tight Link Identification (Contd.) • All (N-1) estimates should be within [100, agreeavail]% of the minimum estimate • Otherwise the next batch of 10 packets is sent. • Average available bandwidth of interface I is n ( N 1) 1 AI n( N 1) k 1 AIk where n attempts have been made at measurement March 30, 2004 BWN Lab - Tricha Anjali 60 MRTG-based Measurement • More accurate estimation of tight link available bandwidth • MRTG-based passive approach similar to ABEst • Reliably predicts the utilization of the link for a future interval, that varies in size March 30, 2004 BWN Lab - Tricha Anjali 61 Big Picture of TEAM Traffic Engineering Automated Manager Simulation Tool (ST) Management Plane LSP/lSP Setup/ Dimensioning Traffic Engineering Tool (TET) DiffServ/ GMPLS Domain LSP Preemption LSP Routing Traffic Routing Measurement/ Performance Evaluation Tool (MPET) TEAM Network Dimensioning and Topology Design To neighboring TEAM March 30, 2004 BWN Lab - Tricha Anjali 62 Inter-domain Resource Management - “A New Scheme for Traffic Estimation and Resource Allocation for Bandwidth Brokers,” Computer Networks Journal, April 2003 - “Filtering and Forecasting Problems for Aggregate Traffic in Internet Links,” Performance Evaluation Journal, 2004 • Inter-domain resource reservation agreements • Estimate the traffic on an inter-domain link and forecast its capacity requirement, based on a measurement of the current usage • Efficient resource utilization while keeping the number of reservation modifications to low values. • Two approaches for resource allocation – Off-line : simple and predictable but lead to resource wastage – On-line : “Cushion” scheme (Terzis 2001) wherein extra bandwidth is reserved over the current usage. • large number of re-negotiations to satisfy the QoS. March 30, 2004 BWN Lab - Tricha Anjali 63 Resource Reservation Problem • Assumptions – Estimate traffic for one traffic class – Number of established sessions is N and stays constant during analysis – For each session, flows are defined as active periods – Each flow has a constant rate of b bits per second – Flows are assumed to be Poissonian with exponential inter-arrival times and durations March 30, 2004 BWN Lab - Tricha Anjali 64 Model Formulation • Notations – y(m) : aggregate traffic on link at time m – x(m) : number of active flows on link at time m – y(m) : noisy measure of the aggregate traffic on link at time m – x(m) : estimate of x(m) – pk(t) : probability that number of active flows at time t is k March 30, 2004 BWN Lab - Tricha Anjali 65 Traffic Estimation • Generating function G(z,t), with the initial condition G(z,mT)=zx(m) G( z, t ) C ( z, t ) x ( m) lz l C ( z , t ) N l z ( z 1)et ( l ) where C ( z, t ) l z l ( z 1)et ( l ) xˆ(m) Axˆ(m 1) B k (m) y (m) CAxˆ(m 1) CB where A e T ( l ) Nl 1 eT ( l ) B l C b k (m) is Kalman Filter Gain March 30, 2004 BWN Lab - Tricha Anjali 66 Allocation Forecasting • x(m) to forecast R(m+1) T pN (t ) and • Define P p0 (t ) p1 (t ) Q as the transition probability matrix P QP and Q Y Y 1 t P Ye C where C e mT 1 Y 1PmT ( m 1)T 1 1 t Define P Y e dt C p0 p1 T mT T pN T Define x(m) min x s.t. px x[ xˆ ( m ), N ] Then R(m 1) bx(m) March 30, 2004 BWN Lab - Tricha Anjali 67 Performance Evaluation N=20, l==0.005 25 Actual EPABB Cushion Bandwidth (Mbps) 20 15 10 5 0 0 March 30, 2004 3000 Time (sec) 6000 BWN Lab - Tricha Anjali 9000 68 Performance Evaluation (Contd.) 25 Actual EPABB Gaussian Bandwidth (Mbps) 20 15 10 5 0 0 3000 6000 9000 Time (sec) March 30, 2004 BWN Lab - Tricha Anjali 69 Big Picture of TEAM Traffic Engineering Automated Manager Simulation Tool (ST) Management Plane LSP/lSP Setup/ Dimensioning Traffic Engineering Tool (TET) DiffServ/ GMPLS Domain LSP Preemption LSP Routing Traffic Routing Measurement/ Performance Evaluation Tool (MPET) TEAM Network Dimensioning and Topology Design To neighboring TEAM March 30, 2004 BWN Lab - Tricha Anjali 70 TEAM Implementation • TEAM has been implemented to run on a computer with the Linux OS. • This testbed has been used as the platform to implement and test the operation of TEAM. March 30, 2004 BWN Lab - Tricha Anjali 71 TEAM Top-level Design Scheduler Interface Measurements MRTG Routers Topology updates Configuration Trigger receiver Configure routers Topology change Commands User interface server Update topology Label, path, priority, bandwidth Create/Destroy/Resize LSP New bandwidth request New bandwidth request LSP Setup Label, path Path, priority, bandwidth Create/Resize LSP LSPs to be destroyed Preemption Path, priority, bandwidth Reroute Route Path, priority, bandwidth Route March 30, 2004 BWN Lab - Tricha Anjali LSPs to be re-routed Route 72 TEAM Module Hierarchy GRAPH REA NET_SNMP LSP_DB TOPOLOGY PREEMPT ROUTING SNMP RRDTOOL LSP_SETUP MRTG REQUEST_DB GSL RE-ROUTE ABEST UI-PROTOCOL SCHEDULER MPET REQUEST EVENTS UI-SERVER COMMAND CONFIG TET March 30, 2004 BWN Lab - Tricha Anjali 73 Performance Evaluation • Topology with 40 nodes and 64 links of capacity 600 Mbps • Comparison with a traditional manager – – – – – March 30, 2004 Shortest path routing for LSPs Shortest path routing for traffic LSP setup based on service level agreements No LSP preemption No on-line network measurements BWN Lab - Tricha Anjali 74 Generalized Medium Traffic Load Rejection Ratio TM TEAM 0.35 0.3 Ratio 0.25 0.2 0.15 0.1 0.05 0 0 March 30, 2004 5 10 15 Experiment BWN Lab - Tricha Anjali 20 25 75 Generalized Medium Traffic Load Minimum AB Average AB 600 160 TM TEAM 500 400 140 Capacity units Capacity Units 150 300 130 200 120 100 110 100 0 March 30, 2004 TM TEAM 5 10 15 Experiment 20 25 0 0 BWN Lab - Tricha Anjali 5 10 15 Experiment 20 25 76 Focused High Traffic Load Priority 0 Rejection 0.5 0.25 TM TEAM 0.4 0.3 0.15 0.2 0.1 0 0 March 30, 2004 TM TEAM 0.2 Ratio Ratio Priority 1 Rejection 0.1 0.05 5 10 15 Experiment 20 25 0 0 BWN Lab - Tricha Anjali 5 10 15 Experiment 20 25 77 Conclusions Development of TEAM, an automated manager for MPLS networks, that performs network design and adaptive network management including LSP and traffic routing, LSP setup and capacity allocation, etc. based on network measurements. March 30, 2004 BWN Lab - Tricha Anjali 78 Future Work • Heterogeneous large network management • MPLS in Wireless Networks • Network Tomography March 30, 2004 BWN Lab - Tricha Anjali 79 Publications 1. “Building an IP Differentiated Services Testbed,” Proceedings of IEEE ICT, June 2001 2. “A New Threshold-Based Policy for Label Switched Path Setup in MPLS Networks,” Proceedings of 17th ITC, September 2001 3. “Optimal Policy for LSP Setup in MPLS Networks,” Computer Networks Journal, June 2002 4. “Design and Management Tools for an MPLS Domain QoS Manager,” Proceedings of SPIE ITCOM, July 2002 5. “MABE: A New Method for Available Bandwidth Estimation in an MPLS Network,” Proceedings of IEEE NETWORKS, August 2002 6. “A New Path Selection Algorithm for MPLS Networks Based on Available Bandwidth Estimation,” Proceedings of QoFIS, October 2002 7. “ABEst: An Available Bandwidth Estimator within an Autonomous System,” Proceedings of IEEE GLOBECOM, November 2002 8. “A New Traffic Engineering Manager for DiffServ/MPLS Networks: Design and Implementation on an IP QoS Testbed,” Computer Communications Journal, March 2003 9. “A New Scheme for Traffic Estimation and Resource Allocation for Bandwidth Brokers,” Computer Networks Journal, April 2003 10. “Adding QoS Protection in Order to Enhance MPLS QoS Routing,” Proceedings of IEEE ICC, May 2003 March 30, 2004 BWN Lab - Tricha Anjali 80 Publications (Contd.) 11. “TEMB: Tool for End-to-End Measurement of Available Bandwidth,” Proceedings of IEEE ELMAR, June 2003 12. “QoS On-line Routing and MPLS Multilevel Protection: A Survey,” IEEE Communications Magazine, October 2003 13. “Optimal Filtering in Traffic Estimation for Bandwidth Brokers,” Proceedings of IEEE GLOBECOM, December 2003 14. “LSP and lSP Setup in GMPLS Networks,” Proceedings of IEEE INFOCOM, March 2004 15. “Threshold-Based Policy for LSP and lSP Setup in GMPLS Networks,” Proceedings of IEEE ICC, June 2004 16. “New MPLS Network Management Techniques Based on Adaptive Learning,” IEEE Transactions on Neural Networks, 2004 17. “Filtering and Forecasting Problems for Aggregate Traffic in Internet Links,” Performance Evaluation Journal, 2004 18. “Traffic Routing in MPLS Networks Based on QoS Estimation and Forecast,” submitted for publication 19. “TEAM: A Traffic Engineering Automated Manager for DiffServ-based MPLS Networks,” submitted for publication March 30, 2004 BWN Lab - Tricha Anjali 81