Cost-Performance Tradeoffs in MPLS and IP Routing

1. Cost-Performance Tradeoffs in MPLS and IP Routing Selma Yilmaz Ibrahim Matta Boston University

2. Motivation

3. Conventional IP Routing Static shortest-path destination based only routing Plus • Single state per destination in the forwarding table Minus • Leads to unbalancedtraffic distribution The Fish Example R2-R3-R4 may get over-utilized R2-R6-R7-R4 may stay under-utilized Find ways to make better utilization of resources by making use of alternate paths 30-80% of the cases there is an alternate path with significantly superior quality i.e. loss rate, bandwidth, RTT [Savage:Sigcomm99]

4. Available Resources QoS Requirements Traffic Demands Best-effort + QoS Routing + + QoS and Traffic Aware Routing + + + Classification of Routing Solutions • Best-effort solutions Ex: Per-packet dynamic routing • QoS routing solutions Ex: Widest-shortest path (WSP) • QoS and traffic aware solutions • Location of ingress-egress pairs Ex: Minimum Interference Routing (MIRA) [Kar:Infocom00] • Traffic matrix • Both location of ingress-egress pairs and traffic matrix Ex: Profile-based routing (PBR) [Suri:01] How far are these solutions from optimal?

5. Increasing Cost Per-packet Dynamic RoutingWSP MIRA PBR • Stateless • Best effort • Per-flow (MPLS kind) • Guaranteed bandwidth Cost • Time Complexity • Space Complexity Performance Measures • Bandwidth Acceptance Ratio Total bandwidth accepted/Total bandwidth requested • Utility Per-packet: Portion of flow that is accepted Per-flow: 0/1 • Maximum Link Utilization Increasing Performance ?

6. Review of Evaluated Algorithms

7. Per-packet Dynamic Routing Properties • Avoids congested links • Computationally simple • Stateless Difficulties • Link states change at packet level • Impractical to generate link state updates at packet level • Larger link state update periods may cause oscillations

8. Widest-Shortest Path Routing • Choose feasible min-hop path Break ties by picking the widest • limit resource consumption: shortest paths • improves performance under heavy load • balance load: widest paths • increases utilization and long term performance • Per-flow state is maintained • Run time complexity is same as Dijkstra’s shortest path algorithm

9. D1 S1 10 11 S2 D2 7 8 6 9 D3 S3 1 2 3 4 5 Minimum Interference Routing (MIRA) Goal: Increase utilization and long term performance of QoS routing by being aware of location of ingress-egress pairs Idea: Among feasible paths, pick the one that interferes the least with future requests • Link costs are assigned based on criticality • Shortest path routing • Run time complexity is complexity of maxflow computation • Per-flow state

10. Profile-based Routing (PBR) Goal: Increase utilization and long term performance of QoS routing by being aware of location of ingress-egress pairs and traffic matrix Traffic Profile (classID, si, di, Bi): Aggregate expected traffic between ingress si -egress di for a class classID. Idea: • Using offline phase to compute pre-allocated capacities for each traffic class • Routing during online phase within these pre-allocated capacities

11. S2 cost=1 cost=infinity S1 D1 D2 cost=infinity Profile-based Routing (PBR) Off-line (pre-processing) phase • Compute an optimal distribution of profiles by solving multicommodity flow problem xi(e)amount of commodity irouted through edgee • Each profile is a commodity • Excess edges are added to always have feasible solution • Flows are forced to be routed through original edges as much as possible

12. Simulations • Algorithms: Dynamic Per-packet Routing, WSP, MIRA, PBR Dynamic per-packet routing • Multicommodity flow problem is solved at each flow arrival/departure xi(e)amount of commodity irouted through edgee • Each active flow is a commodity and allowed to split • Excess edges are used to always have feasible solution • During its lifetime, an individual flow can • be split • get different bandwidth values • be assigned to different paths

13. Simulation Model and Performance Measures Assumptions • Only one class between an ingress-egress pair • Bandwidth demands of flows that belong to the same class are same • Flow arrivals from a class is according to Poisson process • Hold times are Pareto • Load between different ingress-egress pairs is same Performance Measures • Bandwidth Acceptance Ratio Total bandwidth accepted/ Total bandwidth requested • Utility Per-packet: Portion of flow that is accepted Per-flow: 0/1 • Maximum Link Utilization

14. Results Rainbow Topology Per-packet Dynamic Routing > WSP~MIRA > PBR

15. 11 12 13 14 4 4 4 8 9 10 4 4 1 1 1 1 6 7 1 1 5 1 2 1 1 3 4 1 S1 D1 1 D2 2 2 S2 Results Rainbow Topology Profiles are (class1,S1,D1,2) and (class1,S2,D2,2) Accepted bandwidth with MIRA=WSP=n, PBR=0

16. Results • Per-packet dynamic routing packs load along shortest paths - increases maximum utilization • PBR has lowest maximum utilization - it lets links stay underutilized • WSP should be best at load balancing - not seen since there is no alternate paths

17. Results Regular Topology

18. Conclusion • Dynamic per-packet routing shows best, PBR shows worst performance • Among per-flow routing algorithms, WSP shows good performance at low cost • More information doesn’t mean more gain • Because of pre-allocation, statistical multiplexing is lost

19. Future Work • Using extra information in the form of traffic matrix and ingress-egress pairs should lead to a better performance. Why it didn’t? • Take traffic variability into consideration • Don’t take pre-allocated capacities as hard limits • For cost-performance tradeoffs • Solutions that are at the extreme ends of spectrum, i.e. no state or per-flow state, not practical • Find good operating point where • performance is good enough • cost is not too high Ex: hybrid routing for traffic classes