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Network Sensitivity to Hot-Potato Disruptions. Renata Teixeira ( UC San Diego ) http://www-cse.ucsd.edu/~teixeira with Aman Shaikh (AT&T), Tim Griffin(Intel), and Geoff Voelker (UCSD). SIGCOMM’04 – Portland, OR. UCSD. AT&T . AOL. Verio. Sprint. interdomain routing (BGP).

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network sensitivity to hot potato disruptions

Network Sensitivity to Hot-Potato Disruptions

Renata Teixeira

(UC San Diego)

http://www-cse.ucsd.edu/~teixeira

with

Aman Shaikh (AT&T), Tim Griffin(Intel), and Geoff Voelker (UCSD)

SIGCOMM’04 – Portland, OR

internet routing architecture

UCSD

AT&T

AOL

Verio

Sprint

interdomain routing (BGP)

intradomain routing (OSPF,IS-IS)

Changes in one AS

may impact traffic

and routing in other ASes

End-to-end performance

depends on all ASes

along the path

Internet Routing Architecture

Web

Server

User

hot potato routing

dst

multiple connections

to the same peer

10

9

Hot-potato routing = route to closest egress point when there is more than

one route to destination

  • All traffic from customer to peers
  • All traffic to customer prefixes
  • with multiple connections
Hot-Potato Routing

New York

San Francisco

ISP network

Dallas

hot potato disruption

dst

- failure

- planned maintenance

- traffic engineering

10

9

11

11

Hot-Potato Disruption

New York

San Francisco

ISP network

Routes to thousands

of destinations switch

exit point!!!

Dallas

consequences of hot potato disruptions
Consequences of Hot-Potato Disruptions
  • Transient forwarding instability
    • Up to three minutes convergence delay
    • Normal internal changes take a couple of seconds
  • Traffic shift
    • Responsible for largest traffic matrix variations
  • Interdomain routing changes
    • Around 2 – 5% of a router’s external BGP updates
what to do about it
What to do about it?
  • Engineer network to minimize disruptions
    • Network operator: operational practices to avoid changes
    • Network designer: designs that minimize sensitivity
  • Need a vocabulary and metrics to evaluate impact of internal changes
    • Compare possible network designs
    • Identify critical events
    • Take special care during maintenance or traffic engineering
modeling hot potato routing
Modeling Hot-Potato Routing
  • Model of egress selection in backbone networks
    • Internal topology and link weights
    • Set of egress routers for each destination prefix
  • Apply topology changes
    • Link or router failures
    • Link weight changes
  • Evaluate impact of topology changes
    • For a router what fraction of prefixes shifts
    • Most critical link failure
modeling egress selection

B

A

Region of A

Region of B

Egress set for a destination prefix (dst) =

set of border nodes that learn routes to dst ({A,B})

Region of egress node A = nodes that are closer to A than B

Modeling Egress Selection

dst

B

9

A

4

3

8

D

3

10

4

G

11

E

F

8

6

5

C

modeling topology changes

dst

B

9

A

4

3

8

D

3

10

4

G

11

E

F

8

6

5

C

Region of A

Region of A

Region of B

Region of B

Topology change = edge or node deletion,

link weight change

C shifts from region of A to B

Modeling Topology Changes

dst

B

9

A

4

3

8

D

3

10

4

G

11

E

F

8

6

5

C

generalizing to all prefixes

Routing-shift at C

when CF is deleted

= 10,000/15,000

(i.e. 2/3)

Y (1,000 prefixes)

Generalizing to All Prefixes
  • Fraction of prefixes at a router that change egresses after a single topology change
    • Routing-shift function (HRM)

X (10,000 prefixes)

9

B

B

A

A

4

D

3

6

10

3

4

G

11

E

F

8

6

5

Z (4,000 prefixes)

C

all prefixes routers and topology changes

routing-shift

function

fraction of prefixes

at C that changes

egress after the

failure of link CF: 2/3

C

failure of CF

All Prefixes, Routers, and Topology Changes

routers

topology changes

node routing sensitivity metrics rm
Node Routing Sensitivity Metrics (RM)
  • Node routing sensitivity
    • Expected fraction of route shifts experienced by a node
  • Worst case
    • Maximum route shift experienced by a node

routers

C

topology changes

routing impact of a graph transformation rm
Routing Impact of a Graph Transformation (RM)
  • Impact of graph transformations
    • Average fraction of route shifts across all nodes
  • Worst case
    • Maximum route shift caused by each graph transformation

routers

failure of CF

topology changes

case study a large isp backbone network
Case Study: A Large ISP Backbone Network
  • Obtaining input for the model
    • Topology – intradomain routing messages
    • Egress sets – collection of BGP tables
    • Set of graph transformations
      • Single link failures
      • Single router failures
    • Probability distribution for graph transformations
      • Uniform
which failures are most disruptive

router failures

link failures

Routing Impact of Failures

Most failures cause no

hot-potato disruptions

Operators can focus on

most disruptive failures

fraction of failures

Which failures are most disruptive?

routers

Order failures

according to

average impact

single router failures

which routers are most sensitive

router failures

link failures

Very few hot-potato changes on average,

but there are many failures that cause

no shift

Node Routing Sensitivity

fraction of routers

Which routers are most sensitive?

Order routers

according to

average sensitivity

routers

High variance among routers

single router failures

what is the largest routing shift for each router

Very disruptive failures

for some routers

Worst Case

Node Routing Sensitivity

fraction of routers

What is the largest routing shift for each router?

Order routers

according to

worst case

sensitivity

routers

single router failures

or

single link failures

conclusion
Conclusion
  • Contributions
    • Model of hot-potato disruptions
    • Basis for a sensitivity analysis tool
  • Robustness should be a first-order metric
    • As important as traditional performance metrics
    • Network should have small reactions to small changes
  • Two approaches
    • Engineer the system: our model
    • Redesign routing interaction: on-going work
minimizing disruptions
Minimizing Disruptions

5

5

  • Reconfiguration of routing protocols
  • Link and node redundancy
  • Selection of peering locations

4

10

10

10