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

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

BWN Lab - Tricha AnjaliMarch 30, 20042 Contents Introduction Network Management TEAM Structure LSP/ SP Setup Traffic Routing Available Bandwidth Estimation End-to-end Available Bandwidth Measurement Inter-domain Management TEAM Implementation Conclusions Future Work

BWN Lab - Tricha AnjaliMarch 30, 20043 Goals Two-fold which are complementary: –Guarantee Quality of Service for the required applications. –Use the network resources efficiently.

BWN Lab - Tricha AnjaliMarch 30, 20044 MultiProtocol Label Switching Explicitly routed point-to-point paths called Label Switched Paths (LSPs) Support for traffic engineering and fast reroute Simpler switching operations

BWN Lab - Tricha AnjaliMarch 30, 20045 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 SP) for an aggregation of flows src dest

BWN Lab - Tricha AnjaliMarch 30, 20046 DiffServ + GMPLS DiffServ –Scalable service differentiation DiffServ + GMPLS –Class differentiation for QoS provisioning –Traffic Engineering for DiffServ classes for efficient use of resources

BWN Lab - Tricha AnjaliMarch 30, 20047 Network Model Class Type 0 (BE) Class Type 1 (AF) Class Type 2 (EF) MPLS Networks Link: Label Switched Path (LSP) Optical Network Link: fiber Wavelength Network Link: l ambda Switched Path ( SP)

BWN Lab - Tricha AnjaliMarch 30, 20048 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

BWN Lab - Tricha AnjaliMarch 30, 20049 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

BWN Lab - Tricha AnjaliMarch 30, 200410 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

BWN Lab - Tricha AnjaliMarch 30, 200411 Big Picture of TEAM Traffic Engineering Automated Manager Route Resource LSP Routing Traffic Routing LSP Preemption LSP/ SP Setup/ Dimensioning Management Plane DiffServ/ GMPLS Domain Simulation Tool (ST) Traffic Engineering Tool (TET) Measurement/ Performance Evaluation Tool (MPET) TEAM To neighboring TEAM Network Dimensioning and Topology Design

BWN Lab - Tricha AnjaliMarch 30, 200412 LSP and SP Setup Problem 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 (N 2 problem) Why not a fixed topology? Because traffic is unpredictable - “Optimal Policy for LSP Setup in MPLS Networks,” Computer Networks Journal, June 2002 - “LSP and SP Setup in GMPLS Networks,” Proceedings of IEEE INFOCOM, March 2004

BWN Lab - Tricha AnjaliMarch 30, 200413 LSP and SP Setup Problem Arrival of bandwidth request Decision among: –Option 1: no action –Option 2: setup a direct LSP –Option 3: setup a direct SP and LSP src dest 1 2 3

BWN Lab - Tricha AnjaliMarch 30, 200414 LSP and SP 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

BWN Lab - Tricha AnjaliMarch 30, 200415 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 SP or the min-hop path P ij over the default optical network –a new LSP can not be routed on a previously established non-default SP

BWN Lab - Tricha AnjaliMarch 30, 200416 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

BWN Lab - Tricha AnjaliMarch 30, 200417 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 SP (Bl) or on min-hop path (Bp) Number of SPs between the node pair (k)

BWN Lab - Tricha AnjaliMarch 30, 200418 Model Formulation (Contd.) Action Variables MPLS network Optical network

BWN Lab - Tricha AnjaliMarch 30, 200419 Cost Model Incremental cost W = W b + W sw + W sign –W b (s,a) : Bandwidth cost –W sw (s,a) : Switching cost –W sign (s,a) : Signaling cost if LSP/ SP is set-up or re-dimensioned W b and W sw are linear with respect to the bandwidth request and time W sign is incurred only if the decision is a = 1

BWN Lab - Tricha AnjaliMarch 30, 200420 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

BWN Lab - Tricha AnjaliMarch 30, 200421 Optimization (MPLS network) Optimal policy  * such that Optimality equations where

BWN Lab - Tricha AnjaliMarch 30, 200422 Optimal Policy (MPLS Network) where

BWN Lab - Tricha AnjaliMarch 30, 200423 Optimization (Optical Network) Optimal policy  * such that Optimality equations where

BWN Lab - Tricha AnjaliMarch 30, 200424 Optimal Policy (Optical Network) where

BWN Lab - Tricha AnjaliMarch 30, 200425 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

BWN Lab - Tricha AnjaliMarch 30, 200426 Sub-optimal Policy (MPLS) where

BWN Lab - Tricha AnjaliMarch 30, 200427 Sub-optimal Policy (Optical) where

BWN Lab - Tricha AnjaliMarch 30, 200428 Performance Evaluation Example network: Network has 10 nodes and 17 links C ph = 1000 Mbps Diameter = length of longest shortest path = 3

BWN Lab - Tricha AnjaliMarch 30, 200429 Comparison Discount factor=0.5Discount factor=0.1 Discounted total cost vs. Initial state

BWN Lab - Tricha AnjaliMarch 30, 200430 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 – SPs with larger number of default SPs that need re-dimensioning in their path are established first

BWN Lab - Tricha AnjaliMarch 30, 200431 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, SP network is fully connected

BWN Lab - Tricha AnjaliMarch 30, 200432 Total Expected Cost

BWN Lab - Tricha AnjaliMarch 30, 200433 Bandwidth Wastage in MPLS Network

BWN Lab - Tricha AnjaliMarch 30, 200435 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)

BWN Lab - Tricha AnjaliMarch 30, 200436 Routing Algorithm Notations –p uv : path in the MPLS network –p uv = (l ux, …, l zv ) –A l ij /d l ij : Available capacity/delay on l ij –n p uv : Number of LSPs in p uv –

BWN Lab - Tricha AnjaliMarch 30, 200437 Cost Model LSP cost W = W b + W sw + W sign +W AB +W d –W b and W sw linear with respect to the bandwidth request and duration of request –W sign is instantaneous –W AB is inversely related to LSP available bandwidth –W d linear with respect to delay on the LSP Path cost W p = ∑ LSP costs + (n-1) ( Relay node cost )

BWN Lab - Tricha AnjaliMarch 30, 200438 Routing Problem Find the path such that subject to feasibility constraints

BWN Lab - Tricha AnjaliMarch 30, 200439 Routing Algorithm Heuristic of the exact problem Path set size restricted to F Set populated by paths with increasing length Feasibility check Cost comparison

BWN Lab - Tricha AnjaliMarch 30, 200440 Partial Information Estimation algorithm for accurate state information Linear prediction Dynamically change the number of past samples based on prediction performance

BWN Lab - Tricha AnjaliMarch 30, 200441 Performance Evaluation Popular ISP topology with link capacity = 155 c.u.

BWN Lab - Tricha AnjaliMarch 30, 200442 Rejection Ratio

BWN Lab - Tricha AnjaliMarch 30, 200443 Minimum Available Bandwidth

BWN Lab - Tricha AnjaliMarch 30, 200444 Paths with Relay Nodes

BWN Lab - Tricha AnjaliMarch 30, 200446 Available Bandwidth Measurement 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 - “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

BWN Lab - Tricha AnjaliMarch 30, 200447 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 –A h [k] : Available bandwidth estimate for [(k+1) , (k+h)  ]

BWN Lab - Tricha AnjaliMarch 30, 200448 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 kk-p+1k+h

BWN Lab - Tricha AnjaliMarch 30, 200449 ABEst (Contd.) 1.At time instant k, available bandwidth measurement is desired. 2.Find the vectors w a, a  [1,h] using covariance method given p and the previous measurements. 3.Find and 4.Predict A h [k] for [(k+1) , (k+h)t]. 5.At time (k+h)t, get 6.Find the error vector 7.Set k = k+h. 8.Obtain new values for p and h. 9.Go to step 1.

BWN Lab - Tricha AnjaliMarch 30, 200450 ABEst (Contd.) Covariance estimated as Covariance normal equations A h [k] estimated –Either C – max{predicted utilization vector} –Or C – Effective bandwidth from the utilization vector

BWN Lab - Tricha AnjaliMarch 30, 200451 ABEst (Contd.) Algorithm for h and p –If  /  > Th 1, decrease h until h min and increase p till p max multiplicatively –If Th 1 >  /  > Th 2, decrease h until h min and increase p till p max additively –If  /  < Th 2, then: If  > Th 3 *M 2 E, decrease h until h min and increase p till p max additively If Th 3 *M 2 E >  > Th 4 *M 2 E, keep h and p constant If  < Th 4 *M 2 E, increase h and decrease p till p min additively

BWN Lab - Tricha AnjaliMarch 30, 200452 Performance Evaluation h min =10

BWN Lab - Tricha AnjaliMarch 30, 200453 Performance Evaluation (Contd.) h min =20

BWN Lab - Tricha AnjaliMarch 30, 200454 End-to-end AB Measurement Motivation –Combine active and passive approaches –Most tools estimate narrow link capacity –Accuracy –Scalability –Statistical robustness –Not intrusive - “TEMB: Tool for End-to-End Measurement of Available Bandwidth,” Proceedings of IEEE ELMAR, June 2003

BWN Lab - Tricha AnjaliMarch 30, 200455 Tight Link Identification Measurement packets 10 measurement packets sent in a second, to make the tool non- intrusive Version TypeLength Checksum Data Record (optional)

BWN Lab - Tricha AnjaliMarch 30, 200456 Data Record Data record Inserted/modified by the hops of the path Counter information from MIB-II in router IP address Counter Timestamp Speed

BWN Lab - Tricha AnjaliMarch 30, 200457 Example of Auto-detection S A.1.1.1 C.1.1.1 B.1.1.1 D.1.1.1 00 checksum 8 D 00 24 A.1.1.1 3245 234563 10000000 00 checksum 40 A.1.1.1 3245 234563 10000000 C.1.1.1 23487 54236 10000000 00 checksum 8 00 24 A.1.1.1 3272 234568 10000000 00 checksum 40 A.1.1.1 3272 234568 10000000 C.1.1.1 23498 54245 10000000

BWN Lab - Tricha AnjaliMarch 30, 200458 Example of Non-min-hop Path A.1.1.1 C.1.1.1 B.1.1.1 D.1.1.1 S D 01 checksum 72 B.1.1.1 3245 234563 10000000 D.1.1.1 23487 54236 100000000 C.1.1.1 5324586 43214 10000000 01 checksum 72 B.1.1.1 3245 234563 10000000 D.1.1.1 23487 54236 100000000 C.1.1.1 0 0 0 01 checksum 72 B.1.1.1 3245 234563 10000000 D.1.1.1 0 0 0 C.1.1.1 0 0 0 01 checksum 72 B.1.1.1 0 0 0 D.1.1.1 0 0 0 C.1.1.1 0 0 0

BWN Lab - Tricha AnjaliMarch 30, 200459 Tight Link Identification 10 packets in one second N packets back at source for analysis Utilization of I-th interface at time t k Available bandwidth At least agree link of the estimates should concur about the tight link identity.

BWN Lab - Tricha AnjaliMarch 30, 200460 Tight Link Identification (Contd.) All (N-1) estimates should be within [100, agree avail ]% of the minimum estimate Otherwise the next batch of 10 packets is sent. Average available bandwidth of interface I is where n attempts have been made at measurement

BWN Lab - Tricha AnjaliMarch 30, 200461 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

BWN Lab - Tricha AnjaliMarch 30, 200463 Inter-domain Resource Management 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. - “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

BWN Lab - Tricha AnjaliMarch 30, 200464 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

BWN Lab - Tricha AnjaliMarch 30, 200465 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) – p k (t) : probability that number of active flows at time t is k

BWN Lab - Tricha AnjaliMarch 30, 200466 Traffic Estimation Generating function G(z,t), with the initial condition G(z,mT)=z x(m) where

BWN Lab - Tricha AnjaliMarch 30, 200467 Allocation Forecasting x(m) to forecast R(m+1) Define and Q as the transition probability matrix

BWN Lab - Tricha AnjaliMarch 30, 200468 Performance Evaluation N=20, =  =0.005

BWN Lab - Tricha AnjaliMarch 30, 200469 Performance Evaluation (Contd.)

BWN Lab - Tricha AnjaliMarch 30, 200471 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.

BWN Lab - Tricha AnjaliMarch 30, 200472 TEAM Top-level Design User interface server Commands New bandwidth request LSP Setup Configure routers Routers Trigger receiver Configuration Topology updates Update topology Preemption Reroute Route Create/Destroy/Resize LSP Create/Resize LSP Route Label, path, priority, bandwidth Path, priority, bandwidth LSPs to be destroyed LSPs to be re-routed Route Path, priority, bandwidth Topology change New bandwidth request Path, priority, bandwidth Label, path SchedulerMRTG Interface Measurements

BWN Lab - Tricha AnjaliMarch 30, 200473 TEAM Module Hierarchy ABEST COMMAND CONFIG EVENTS GRAPH LSP_DB LSP_SETUP MPET MRTG PREEMPT REQUEST_DB REQUEST REA ROUTING RRDTOOL SCHEDULER SNMP TOPOLOGY UI-SERVER UI-PROTOCOL RE-ROUTE GSL TET NET_SNMP

BWN Lab - Tricha AnjaliMarch 30, 200474 Performance Evaluation Topology with 40 nodes and 64 links of capacity 600 Mbps Comparison with a traditional manager –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 AnjaliMarch 30, 200475 Generalized Medium Traffic Load Rejection Ratio

BWN Lab - Tricha AnjaliMarch 30, 200476 Generalized Medium Traffic Load Minimum ABAverage AB

BWN Lab - Tricha AnjaliMarch 30, 200477 Focused High Traffic Load Priority 0 RejectionPriority 1 Rejection

BWN Lab - Tricha AnjaliMarch 30, 200478 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.

BWN Lab - Tricha AnjaliMarch 30, 200479 Future Work Heterogeneous large network management MPLS in Wireless Networks Network Tomography

BWN Lab - Tricha AnjaliMarch 30, 200480 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

BWN Lab - Tricha AnjaliMarch 30, 200481 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 SP Setup in GMPLS Networks,” Proceedings of IEEE INFOCOM, March 2004 15.“Threshold-Based Policy for LSP and SP 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

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

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

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Presentation on theme: "DiffServ/MPLS Network Design and Management Doctoral Dissertation Tricha Anjali Broadband and Wireless Networking Laboratory Advisor: Dr. Ian F. Akyildiz."— Presentation transcript:

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