A Performance Study of IP and MPLS Traffic Engineering Techniques under Traffic Variations

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1 A Performance Study of IP and MPLS Traffic Engineering Techniques under Traffic Variations Sukrit Dasgupta Department of ECE Drexel University Philadelphia, PA, USA Jaudelice C. de Oliveira Department of ECE Drexel University Philadelphia, PA, USA Jean-Philippe Vasseur Cisco Systems Boxborough, MA, USA Presented at IEEE-GLOBECOM PMQRS 09 Washington DC, USA 29th November

2 Outline Introduction Traffic Engineering Traffic Engineering with MPLS and IP Performance Comparison Conclusion 2 2

3 Introduction The Internet is more widely used than ever... New applications generating enormous volumes of traffic Applications such as Gaming, Video On Demand, VoIP, etc., have strict QoS requirements With explosive growth and strict QoS requirements comes the need for efficient resource management Service Providers resort to... Network Engineering Manipulate network to suit traffic Buy new equipment (fiber, routers, etc.) to keep up with growth At 60-70% annual traffic growth rate (200%+ in Japan), proves to be very expensive and time consuming Traffic Engineering Manipulate traffic to suit network Move traffic in the network to create more space Commonly deployed using IP, MultiProtocol Label Swticthing (MPLS) and Asynchronous Transfer Mode (ATM) 3 3

4 Traffic Engineering Art of efficiently routing traffic to... Improve efficiency of bandwidth resources Ensure desirable path for most/all traffic Reduce operational costs Challenges... Current mechanisms require the knowledge of Traffic Matrix Mechanisms can be traffic disruptive and unable to cope with rapid changes/multiple failures, etc. Multi-constraint objective functions are needed Several models exist... Centralized: Efficient in solving multi-constraint problems but scale poorly and require multiple cycles of computation and deployment Distributed: Highly scalable, dynamic but may be complex to analyze side affects on network behavior 4 4

5 Traffic Engineering with MPLS and IP Traffic Engineering can be performed using... IP with metric optimization Have to know traffic matrix, effective when conditions do not change MPLS Traffic Engineering Constraints Do not exceed link capacity (or a fraction of link capacity) Additional constraints (such as delay, etc.) When traffic changes: Flash crowd / Failures / Misconfigs With IP: Change link metric (B. Fortz: Reoptimizing OSPF/ISIS Weights) Triggers Shortest Path Tree computation (O.Bonaventure: FIB Ordering) Reroutes traffic on new shortest path. With MPLS: Compute Constrained Shortest Path (CSPF: Prune links + SPF) Setup new reservation and tear down old one ( Make before break ) Forward traffic on new path. 5 5

6 Traffic Engineering with MPLS and IP Node D Next Cost B 9 Router B Router A X Router C 3/DS3 Router D Node D Next Cost Tunnel1 10 Router B Router A X Router C 3/DS3 Router D 1/OC48 Router I 3/DS3 1/OC48 1/OC48 Router I 3/DS3 1/OC48 Router H Router G 1/OC48 Router F Router E Router H Router G 1/OC48 Router F Router E Node Next Cost E G 6 Packet Drops Node Next Cost E G 6 With IP Mbps to E Global impact: Affects every router, SPT computation Micro-Loops: Traffic in transit is sent back and forth Lack of granularity: Specific flows cannot be selected Slow reactive process: Offline optimization problem On linkup: Change the metric to its old value 40 Mbps to D before link failure 40 Mbps to D after link failure DS3: OC3: 45 Mbps 155 Mbps 140 Mbps to E With MPLS... Tunnel is created 40 Mbps to D before link failure 40 Mbps to D after link failure DS3: OC3: 45 Mbps 155 Mbps Path computed takes into account current network state (dynamic path option) Path can also be assigned manually (explicit path option) Reduces chances of congestion On linkup, reoptimization can be enabled and the new path will be computed 6 6

7 Simulation Setup Performance Comparison Realistic Traffic Profile (Daily variation) 4 service provider topologies (OSPF-TE as IGP) All flows on the shortest path (Same starting condition) Link failures (to create a heavy load and traffic shifts) Independently ~ U(0,60) minutes Restored ~ U(0,15) minutes Traffic rerouted on link failure (path computation) Performance Metrics Link Utilization (how good is IP at handling traffic changes/shifts) Path Quality: Ratio of current cost to shortest path cost (how far is MPLS from the shortest path) Bandwidth (Kbps) Traffic Traffic Profile Plot - Data Time (Minutes) 7 7

8 Maximum Link Utilization Link UtilizationMaximum Link Utilization IP+Failures IP Static TE+Failures MPLS Maximum Utilization Maximum Utilization Time Number of Links Time Number of Links With IP... Link utilization crosses 100% several times Signifies congestion and packet drops With MPLS Path computation after pruning links CSPF computes paths that can fit traffic Hotspots with IP 8 8

9 Topological View: Max Link Util. MPLS + Failures IP + Failures 9 9

10 4 3.5 MESH SYM ISP1 ISP2 Path Quality with MPLS Distribution of TE:IGP path cost ratio accross TE-LSPs Primary=2543,NHop=0,NNHop=0 TE-LSPs MESH SYM ISP1 ISP2 Distribution of TE:IGP path cost ratio accross traffic Primary=2543,NHop=0,NNHop=0 Traffic TE:IGP Path cost ratio TE:IGP Path cost ratio Percentage of TE-LSPs Topology dependent Percentage of total traffic ISP1, SYM have almost all TE-LSPs/Traffic on shortest path ISP2 and MESH have 90% TE-LSPs/Traffic close to shortest path Fatter TE-LSPs are on longer paths, need more space Priorities can be used for alignment of traffic on shorter paths 10 10

11 Concluding... Summary Quantified metrics that capture and allow comparison of MPLS and IP performance Showed that MPLS can help to reduce congestion without any metric re-computation Showed that MPLS can keep more traffic and TE-LSPs close to their shortest path Contributions Compared MPLS and IP performance under similar scenarios Quantified metrics to motivate the use of MPLS Time varying distribution of link utilization to capture congestion instances Path quality with MPLS 11 11

12 Thank You Questions? This work is supported by Cisco Systems and in part by the National Science Foundation under Grant No

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