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

Motivation

Conventional IP Routing Static shortest-path destination based only routing Plus –Single state per destination in the forwarding table Minus –Leads to unbalanced traffic 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]

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? Available Resources QoS Requirements Traffic Demands Best-effort QoS Routing QoS and Traffic Aware Routing

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 Per-flow (MPLS kind) Guaranteed bandwidth Stateless Best effort Increasing Cost Increasing Performance ? Per-packet Dynamic Routing WSP MIRA PBR

Review of Evaluated Algorithms

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

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

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 S1 S2 S3 D1 D2 D3

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, s i, d i, B i ): Aggregate expected traffic between ingress s i -egress d i 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

Profile-based Routing (PBR) Off-line (pre-processing) phase Compute an optimal distribution of profiles by solving multicommodity flow problem x i (e) amount of commodity i routed through edge e 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 D2 cost=infinity S1D1 cost=1 S2

Simulations Algorithms: Dynamic Per-packet Routing, WSP, MIRA, PBR Dynamic per-packet routing –Multicommodity flow problem is solved at each flow arrival/departure x i (e) amount of commodity i routed through edge e –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

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

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

Results Rainbow Topology Profiles are (class1,S1,D1,2) and (class1,S2,D2,2) Accepted bandwidth with MIRA=WSP=n, PBR= S1 S2 D1 D

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

Regular Topology Results

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

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