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Deriving Traffic Demands for Operational Deriving Traffic Demands for Operational IP Networks: Methodology and ExperienceIP Networks: Methodology and Experience

Anja Feldmann*, Albert Greenberg, Carsten Lund, Nick Reingold, Jennifer Rexford, and Fred True

Internet and Networking Systems Research Lab

AT&T Labs-Research; Florham Park, NJ

*University of Saarbruecken

PowerPoint: view slide show for animation; view notes page for notes

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Traffic Engineering For Operational IP NetworksTraffic Engineering For Operational IP Networks

Improve user performance and network efficiency by tuning router configuration to the prevailing traffic demands.– Why?

– Time Scale? AS 7018 (AT&T)*

*synthetic loads

some customers or peers

some customers or peers

backbone

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Traffic Engineering StackTraffic Engineering Stack

Topology of the ISP backbone– Connectivity and capacity of routers and links

Traffic demands– Expected/offered load between points in the network

Routing configuration– Tunable rules for selecting a path for each flow

Performance objective– Balanced load, low latency, service level agreements …

Optimization procedure– Given the topology and the traffic demands in an IP network,

tune routes to optimize a particular performance objective

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Traffic DemandsTraffic Demands

How to model the traffic demands?– Know where the traffic is coming from and going to

– Support what-if questions about topology and routing changes

– Handle the large fraction of traffic crossing multiple domains

How to populate the demand model?– Typical measurements show only the impact of traffic demands

» Active probing of delay, loss, and throughput between hosts

» Passive monitoring of link utilization and packet loss

– Need network-wide direct measurements of traffic demands

How to characterize the traffic dynamics?– User behavior, time-of-day effects, and new applications

– Topology and routing changes within or outside your network

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OutlineOutline

Sound traffic model for traffic engineering of operational IP networks

Methodology for populating the modelResultsConclusions

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OutlineOutline

Sound traffic model for traffic engineering of operational IP networks– Point to Multipoint Model

Methodology for populating the modelResultsConclusions

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Traffic DemandsTraffic Demands

Big Internet

Web Site User Site

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Traffic DemandsTraffic Demands

Interdomain Traffic

Web Site User Site

AS 1

AS 2

AS 3

AS 4

U

AS 3, U

AS 3, U

AS 3, U

•What path will be taken between AS’s to get to the User site?•Next: What path will be taken within an AS to get to the User site?

AS 4, AS 3, U

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Traffic Demands Traffic Demands

Web SiteUser Site

Zoom in on one AS

200

11010

110

300

25

75

50

300

IN

OUT3

OUT2

OUT1

110

Change in internal routing configuration changes flow exit point!

110

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Point-to-Multipoint Demand ModelPoint-to-Multipoint Demand Model

Definition: V(in, {out}, t)– Entry link (in)

– Set of possible exit links ({out})

– Time period (t)

– Volume of traffic (V(in,{out},t))

Avoids the “coupling” problem with traditional point-to-point (input-link to output-link) models:

Pt to Pt Demand Model

Traffic Engineering

Improved Routing

Pt to Pt Demand Model

Traffic Engineering

Improved Routing

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OutlineOutline

Sound traffic model for traffic engineering of operational IP networks

Methodology for populating the model– Ideal

– Adapted to focus on interdomain traffic and to meet practical constraints in an operational, commercial IP network

ResultsConclusions

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Ideal Measurement MethodologyIdeal Measurement Methodology

Measure traffic where it enters the network– Input link, destination address, # bytes, and time

– Flow-level measurement (Cisco NetFlow)

Determine where traffic can leave the network– Set of egress links associated with each network address

(forwarding tables)

Compute traffic demands– Associate each measurement with a set of egress links

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Adapted Measurement MethodologyAdapted Measurement MethodologyInterdomain FocusInterdomain Focus

A large fraction of the traffic is interdomainInterdomain traffic is easiest to capture

– Large number of diverse access links to customers

– Small number of high speed links to peers

Practical solution– Flow level measurements at peering links (both

directions!)

– Reachability information from all routers

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Inbound and Outbound Flows on Peering LinksInbound and Outbound Flows on Peering Links

Peers Customers

Inbound

Outbound

Note: Ideal methodology applies for inbound flows.

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Most Challenging Part: Most Challenging Part: Inferring Ingress Links for Outbound FlowsInferring Ingress Links for Outbound Flows

Outbound traffic flowmeasured at peering link

Customersdestination

output

Use Routing simulation to trace back to the ingress links!

? input

? input

Example

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ForwardingTables

ConfigurationFiles

NetFlow SNMP

Computing the DemandsComputing the Demands

Data– Large, diverse, lossy

– Collected at slightly different, overlapping time intervals, across the network.

– Subject to network and operational dynamics. Anomalies explained and fixed via understanding of these dynamics

Algorithms, details and anecdotes in paper!

NETWORK

researcher in data mining gear

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OutlineOutline

Sound traffic model for traffic engineering of operational IP networks

Methodology for populating the modelResults

– Effectiveness of measurement methodology

– Traffic characteristics

Conclusions

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Experience with Populating the ModelExperience with Populating the Model

Largely successful– 98% of all traffic (bytes) associated with a set of egress links

– 95-99% of traffic consistent with an OSPF simulator

Disambiguating outbound traffic– 67% of traffic associated with a single ingress link

– 33% of traffic split across multiple ingress (typically, same city!)

Inbound and transit traffic (uses input measurement)– Results are good

Outbound traffic (uses input disambiguation)– Results are pretty good, for traffic engineering applications, but there are

limitations

– To improve results, may want to measure at selected or sampled customer links; e.g., links to email, hosting or data centers.

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Proportion of Traffic in Top Demands (Log Scale)Proportion of Traffic in Top Demands (Log Scale)

Zipf-like distribution. Relatively small number of heavy demands dominate.

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Time-of-Day Effects (San Francisco)Time-of-Day Effects (San Francisco)

Heavy demands at same site may show different time of day behavior

midnight EST midnight EST

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DiscussionDiscussion

Distribution of traffic volume across demands– Small number of heavy demands (Zipf’s Law!)

– Optimize routing based on the heavy demands

– Measure a small fraction of the traffic (sample)

– Watch out for changes in load and egress links

Time-of-day fluctuations in traffic volumes– U.S. business, U.S. residential, & International traffic

– Depends on the time-of-day for human end-point(s)

– Reoptimize the routes a few times a day (three?)

Stability?– No and Yes

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OutlineOutline

Sound traffic model for traffic engineering of operational IP networks

Methodology for populating the modelResultsConclusions

– Related work

– Future work

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Related WorkRelated Work

Bigger picture– Topology/configuration (technical report)

» “IP network configuration for traffic engineering”

– Routing model (IEEE Network, March/April 2000)

» “Traffic engineering for IP networks”

– Route optimization (INFOCOM’00)

» “Internet traffic engineering by optimizing OSPF weights”

Populating point-to-point demand models– Direct observation of MPLS MIBs (GlobalCenter)

– Inference from per-link statistics (Berkeley/Bell-Labs)

– Direct observation via trajectory sampling (next talk!)

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Future WorkFuture Work

Analysis of stability of the measured demandsOnline collection of topology, reachability, &

traffic dataModeling the selection of the ingress link (e.g., use

of multi-exit descriptors in BGP)Tuning BGP policies to the prevailing traffic

demandsInteractions of Traffic Engineering with other

resource allocation schemes (TCP, overlay networks for content delivery, BGP traffic engineering “games” among ISP’s)

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BackupBackup

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Identifying Where the Traffic Can LeaveIdentifying Where the Traffic Can Leave

Traffic flows– Each flow has a dest IP address (e.g., 12.34.156.5)

– Each address belongs to a prefix (e.g., 12.34.156.0/24)

Forwarding tables– Each router has a table to forward a packet to “next hop”

– Forwarding table maps a prefix to a “next hop” link

Process– Dump the forwarding table from each edge router

– Identify entries where the “next hop” is an egress link

– Identify set all egress links associated with a prefix

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Measuring Only at Peering LinksMeasuring Only at Peering Links

Why measure only at peering links?– Measurement support directly in the interface cards

– Small number of routers (lower management overhead)

– Less frequent changes/additions to the network

– Smaller amount of measurement data

Why is this enough?– Large majority of traffic is interdomain

– Measurement enabled in both directions (in and out)

– Inference of ingress links for traffic from customers

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Full Classification of Traffic Types at Peering LinksFull Classification of Traffic Types at Peering Links

Peers Customers

Internal

Inbound

Outbound

Transit

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Flows Leaving at Peer LinksFlows Leaving at Peer Links

Single-hop transit– Flow enters and leaves the network at the same router

– Keep the single flow record measured at ingress pointMulti-hop transit

– Flow measured twice as it enters and leaves the network

– Avoid double counting by omitting second flow record

– Discard flow record if source does not match a customerOutbound

– Flow measured only as it leaves the network

– Keep flow record if source address matches a customer

– Identify ingress link(s) that could have sent the traffic

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Results: Populating the ModelResults: Populating the Model

Ingress Egress Effectiveness

Inbound Netflow Reachability Good

Transit Netflow Netflow & Reachability

Good

Outbound Packet filters

Netflow & Reachability

Pretty Good

Internal X Reachability X

Data Used