An Adaptive Policy Management Approach to BGP Convergence

1
An Adaptive Policy Management Approach to BGP
Convergence

• by
• Selma Yilmaz
• PhD Examining Committee
• Prof. Ibrahim Matta, First Reader (Major Advisor)
• Prof. John Byers, Second Reader
• Prof. Assaf Kfoury, Third Reader
• Prof. Azer Bestavros, Committee Chair
• Prof. Richard West

2
Border Gateway Protocol (BGP)

• Is the de facto inter-domain routing protocol of
  todays global Internet
• Is a policy-based routing protocol
• allows ASes to share reachability information
  according to policies

Export policy Always share routes with AS1
Import policy Accept routes from AS2 for
destination A
3
Border Gateway Protocol (BGP)

• BGP does not necessarily solve shortest path
  routing problem
• Best path is the path with the highest local
  preference value
• assigned by locally defined policies

May assign higher preference value to path (AS3
AS2 AS1) than path (AS4 AS1)
4
BGP Routing Process

• Allows to select the routes based on any desired
  criteria
• Makes it easier to realize commercial
  relationships between ASes
• Ex Forward data only for paying customers
• Filter out the paths passing through AS
  x

5
Problems with Policy-based Routing

• Collection of locally well-configured policies
  may cause global conflicts
• It may not be possible to satisfy conflicting
  policies simultaneously
• Causes BGP to diverge
• ASes exchange routing messages indefinitely
• First shown by Varadhan et al. USC Technical
  Report 1996
• Statically checking for BGP convergence property
  is an NP-complete problem Griffin et al. Sigcomm
  1999
• Many solutions proposed to detect and prevent
  policy conflicts

6
Why is this problem important?

• BGP is widely deployed in todays Internet
• Persistent oscillations leads to
• repeated advertising and withdrawing of routes
• higher processing load
• re-running BGP decision process to select the
  best path
• updating routing and forwarding tables
• endangered scalability
• routers may experience severe CPU load, and
  memory problems
• makes traffic engineering through an AS very
  difficult
• Convergence of BGP must be guaranteed independent
  of locally selected policies

7
Thesis Contributions

• A generalized control theoretical framework for
  BGP convergence is developed
• The framework is instantiated for recently
  proposed algorithms
• Deficiencies of previous solutions are exposed
• A new dynamic algorithm called Adaptive Policy
  Management Scheme (APMS) is proposed
• Correctness and convergence analysis of the
  algorithm is presented
• APMS implemented in the SSF network simulator,
  and its performance is compared against other
  solutions

8
Abstract Model of BGP Griffin Infocom 2000

• Stable Paths Problem (SPP)
• represents the static semantics of BGP
• Simple Path Vector Protocol (SPVP)
• represents the dynamic semantics of BGP
• is a distributed algorithm solving SPP
• An SPP is called safe if SPVP always converges

9
Stable Paths Problem (SPP)

• Network is represented as a simple, undirected
  graph
• Nodes represent BGP routers, edges represent BGP
  sessions
• Node 0 represents destination
• For each node v, there is a set of permitted
  paths, Pv
• For each node v, there is a ranking function, ?v
• Empty path, e, is a permitted path at each node,
  and has the lowest rank
• Paths are simple, i.e. no repeated nodes
• Example of an SPP instance

210 20
2
420 430
4
0
Most preferred
130 10
30
Least preferred
3
1
10
A Solution to a Stable Paths Problem (SPP)

• A solution is an assignment of permitted paths to
  each node
• such that
• node us assigned path is either of the following
• e
• max (?u((u w)Pw)) among the advertised path Pw by
  w ? peers(u)
• An SPP instance may have
• multiple solutions
• SPVP may diverge
• no solution
• SPVP diverges
• a unique solution
• does not mean that SPVP converges to that
  solution
• solvability does not imply safety
• What is the sufficient condition that will
  guarantee
• safety of an SPP specification?

11
Dispute Wheel GW ICNP 99

• A wheel of size k
  
• For each 1 i k
• Ri is a path from ui to ui1 (u1uk1)
• Qi is a permitted
  path ui
• RiQi1 is a
  permitted path at ui (Q1Qk1)
• Qi is less (or
  equally) preferred than RiQi1 at node ui
• Properties of
  dispute wheels assuming S is an SPP
  instance
• If S has no DW, then S is safe and robust
• Lack of DW implies a solution
• 
  Presence of DW does not imply divergence
• 
  Divergence due to lack of solution implies
  DW
• 
  Divergence due to multiple solutions
  implies DW

spokes
Route preferences of these nodes cause dispute
wheel
12
Examples of Stable Paths Problem
No Solution
Multiple Solutions
1
1
0
0
2
3
2
Solutions (10)(210) and (20)(210)
4
Unique Solution, May Not Converge
Safe, Not Robust
1
130 10
210 20
2
1
1
0
4
If the link (40) fails, new SPP has no
solution
310 3120
2
0
2
40 420 430
312
3
4
0
3420 30
312
312
Stabilizes on (130)(30)(20)(40)
13
Generalized Control Theoretical Framework

• Feedback Monitor Update AdjRibIn
• Check path
  for AS loops (path vector property)
• Apply
  import policies to decide permitted paths
• Control Mechanism Apply import policies to
  assign Local Preferences
  Choose best path
• Check for
  an indication of divergence
• If
  YES, change best path
• Update
  locRIB, and export to peers

14
Details of Control Theoretical Framework
Update localRIB and export best(u)
Node u
Control Mechanism
yes
no
Check for an Indication of Divergence
Compute best path, best(u)
Apply import policies to assign Local Preferences
Apply import policies to see if the path P is
permitted at node u
Feedback Monitor
Update AdjRibIn(w)
Check the path P for loops If P contains u
UPDATE message from peer w with path P
15
Related Work and Instantiations in the Control
Theoretical Framework
16
GaoRexford Algorithm Infocom01

• Provides guidelines that guarantees safety of
• BGP
• Use hierarchical structure of the Internet and
  commercial relationships between ASes to specify
  local policies
• Provider-to-customer graph should be acyclic
• Paths are classified as provider/customer/peer
  according to next-hop AS
• Each AS must prefer customer paths more than
  provider/peer paths
• Route Registry database keeps relationships and
  verify that guidelines are followed
• Disadvantages
• Static solution
• Requires Route Registry
• Disallows many paths

17
GaoRexford Algorithm Infocom01

Ex
Assume following provider-to-customer graph
Cycle involving 1,2,3 will be detected by Route
Registry and ASes will be advised to use their
shortest AS paths (10),(20),(30)
18
GriffinWilfong Algorithm Infocom00

• Proposes carrying dynamically computed history of
  path change events with Update messages, history
• Path change event is computed as follows
• If node u changes its current path from Pold
  to Pnew
• Pnew is more preferred than Pold, e(, Pnew)
• Pold is more preferred than Pnew, e(-, Pold)
• History explains the exact sequence of events
  leading to the adoption of the current path
• Cycles in the history corresponds exactly to
  dispute wheels
• The path whose adoption creates a cycle is
  suppressed
• Disadvantages
• History may get very long, may reveal preferences
• Cycle in the history is necessary but not
  sufficient condition
• Cannot distinguish temporary and persistent
  oscillations

19
GriffinWilfong with Control Theoretical
Framework
Update localRIB and export bestB(u)
Re-compute best path, bestB(u), excluding the
paths in bad path set Update history
yes
Node u
Control Mechanism
no
Compute best path, bestB(u), excluding the paths
in bad path set
Apply import policies to assign Local Preferences
Apply import policies to see if the path is
permitted at node u
Feedback Monitor
Update AdjRibIn(w)
Check the path P for loops If the AS path
contains u
UPDATE message from peer w with path P and
history h
20
GriffinWilfong Periodic Reset
Update localRIB and export bestB(u)
Compute Path Change Event, p p(,
bestB(u)) if ?u(bestB(u))gt
?u(current best path) p(-, current best
path) if ?u(bestB(u))lt ?u(current best path) Set
history of bestB(u) to p
Control Mechanism
Node u
Compute best path, bestB(u), excluding the paths
in bad path set
Apply import policies to assign Local Preferences
Update each path stored in AdjRibIn by resetting
history
Feedback Monitor
Purge bad paths set, B(u)

Periodic Reset
21
GriffinWilfong Infocom00
May GriffinWilfong lead to simultaneous path
eliminations?
step node best path path assignment 0
1 (10) (10)
2 (20) (20)
3 (30) (30) 1 1
(130) (130)(30) 2
(210) (210)(10) 3
(320) (320)(20) 2 1
(10) (-130)(320)(20) 2
(20) (-210)(130)(30)
3 (30) (-320)(210)(10) 3
1 (130) (130)(-320)(210)(1
0) 2 (210)
(210)(-130)(320)(20) 3
(320) (320)(-210)(130)(30) 4
1 (10) (-130)(320)(-210)(130)(3
0) 2 (20)
(-210)(130)(-320)(210)(10) 3
(30) (-320) (210)(-130)(320)(20)
5 1 epsilon 2
epsilon 3 epsilon
Stabilizes to unreachable destination for all
nodes
22
CobbMusunuri Globecomm04

• Assigns integer costs to the nodes
• Monotonically increases the cost whenever the new
  path of a node has lower rank then its previous
  path
• If there is divergence, costs grow
• Costs are included in Update messages
• Whenever a node has option to improve its current
  path by
• choosing a better alternative path P
• Checks first if the cost of the next-hop node
  along P is lower than a threshold
• Otherwise, keeps the current path
• Disadvantages
• Aggregates paths through the same node
• May lead to simultaneous path rejections
• Lowering costs are hard
• Lowering costs are suggested to be done
  periodically without taking any precaution to
  prevent re-introducing the resolved conflicts

23
CobbMusunuri with Control Theoretical Framework
Update localRIB and export best(u) along with
cost of u
Update Cost of Node u if ((?u(current path)gt
?u(best(u)) if nextHop(current
path)!nextHop(best(u))
cost(u)cost(u)1 if nextHop(current
path)nextHop(best(u))
cost(u)cost(nextHop(current path(u)) else
cost(u)cost(nextHop(current path(u))
Node u
Control Mechanism
yes
no
Compute best path, best(u)
Apply import policies to assign Local Preferences
Apply import policies to see if the path is
permitted at node u
Feedback Monitor
Update AdjRibIn(w)
Check the path P for loops If the AS path
contains u
UPDATE message from peer w with path P and
cost c
24
CobbMusunuri Periodic Reset
Update localRIB and export best(u) along with
cost of u
Update Cost of Node u if ((?u(current path)gt
?u(best(u)) if nextHop(current
path)!nextHop(best(u))
cost(u)cost(u)1 if nextHop(current
path)nextHop(best(u))
cost(u)cost(nextHop(current path(u)) else
cost(u)cost(nextHop(current path(u))
Node u
Control Mechanism
Compute best path, best(u)
Apply import policies to assign Local Preferences
Feedback Monitor
cost(u)0
A command received to reset the cost of node u
to 0
25
CobbMusunuri Globecomm04
May lead to unnecessary path eliminations?

• step node count best path
• 0 1 0 (10)
• 2 0 (20)
• 3 0 (30)
• 1 1 0 (130)
• 2 0 (210)
• 3 0 (320)
• 2 1 1 (10)
• 2 1 (20)
• 3 1 (30)
• 3 1 1 (130)
• 2 1 (210)
• 3 1 (320)
• 4 1 2 (10)
• 2 2 (20)
• 3 2 (30)
• 1 wont use (130) since count(3) 2
• 2 wont use (210) since count(2)
  2
• 3 wont use (130) since count(3)
  2

Assume threshold2
All nodes stabilize to their lowest preferred
paths
26
Motivation for APMS

• Detect persistent oscillations dynamically
• For better scalability
• Detect paths involved in a policy conflict using
  only local info
• Resolve conflicts locally
• Each node involved in a conflict observes route
  flaps
• Constantly adopting a path and later abandoning
  it
• Not every advertisement received is changing
• Safe path
• There must be more preferred path(s) than the
  safe path
• Make the safe path highest ranked path to stop
  oscillation
• Each node needs to keep local history to detect
  the flapping paths
• Count is associated with the paths in the local
  history
• increased with every flap of the path
• Distributed algorithm
• There may be synchronous detection and path rank
  change
• Perform rank change probabilistically

27
Adaptive Policy Management Scheme

• max_threshold
• Due to probabilistic adjustment of path
  preferences, the conflict may remain unresolved
• If countgt max_threshold, suppress the path.
• min_threshold
• To distinguish between temporary and persistent
  oscillations
• Each node independently classifies the state of
  the network by comparing count values against
  max_threshold and min_threshold

count
max_threshold
min_threshold
time
Policy conflict free phase
Policy conflict control phase
Policy conflict avoidance phase
28
Adaptive Policy Management Scheme

• State of the system
• Path ordering at each node
• (Path, Count) pairs in localHistory
• Count value denotes how many times a path is
  adopted and later abandoned
• Bad path set keeps suppressed paths
• peerStability value associated with each peer
• How many times the path advertised by a peer has
  changed
• The peers with peerStability1 are stable peers
• The paths advertised by stable peers are safe
• keepAliveCount associated with each peer
• Used as an indication of stability
• If keepAliveCount ka_threshold for each peer
• Node concludes that the system is stabilized
• Probabilistically restore local preference values

29
APMS Feedback Monitor (When an Update is Received)
CONTROL MECHANISM
Apply import policies to see if the path is
permitted at node u
Node u
Update AdjRibIn(w)
Check the path for loops If the AS path contains
u
peerStability(w) keepAliveCount0
UPDATE message from peer w
30
APMS Control Mechanism (When an Update is
Received)
Update localRIB and export bestB(u)
yes
no
count(bestB(u))gtmin_threshold
Node u
yes
no
count(bestB(u))gtmax_threshold
If bestB(u) is different from the current best
path, count(bestB(u))
Compute best path, bestB(u), excluding the paths
in bad path set
Apply import policies to assign Local Preferences
31
APMS Path Rank Restoration

• When the system stabilizes, there may be some
  path rank changes that are not contributing to
  the current state of stability
• Policies are placed for a purpose such as traffic
  engineering, cost, security
• Must keep them untouched unless they are
  conflicting
• Must adapt to every state of the network
• conflict free as well as potentially conflicting
• When the system stabilizes, peers exchange only
  keepAlive messages
• Nodes may use this as an indication of
  convergence
• Probe the state for improvement, i.e.
  restoration, in their current policies
• Probabilistically restore local preference values
• May introduce instability back to system
• Use smaller probability, 1/4

32
APMS Path Rank Restoration (When a KeepAlive is
Received)
Update localRIB and export bestB(u)
Compute best path, bestB(u), excluding the paths
in bad path set
for each peer v of node u for path P in
AdjRibIn(v) with probability 1/4
if P was suppressed, remove it from bad
paths set if Ps preference has
been changed, reset its original local preference
reset some states count(P) for
path P in localHistory
peerStability(v)

keepAliveCount(v)
Control Mechanism
Node u
yes
Stability Check keepAliveCount(v) ka_threshold
for each peer v of u
Feedback Monitor
keepAliveCount(w)
KeepAlive message from peer w
33
Handling Transient Oscillations due to Topology
Changes

• If there is a topology change such as link/node
• failure/recovery
• Resulting flaps may interfere with diagnosing
  conflicts
• May lead to false positives
• The system before and after the change have
  different policy dynamics
• New state may be conflict free, local states must
  be reset
• Suggest that the node next to the topology change
  includes extra-information in the resulting
  Update message
• topologyChange helps to
• reset local state
• temporarily turn policy conflict detection
  process off
• originator is a list of nodes who adapted to the
  new topology
• helps to turn policy conflict detection process on

34
Convergence Analysis of APMS

• Different path orderings at the nodes specify
  different states of the network and define
  different policies
• Goal
• Show that starting with an arbitrary state of
  the system, the APMS converges to a stable state
  within a finite number of steps.
• Use substability property of chosen paths

35
Definitions

• Conflict free node is a node whose policies are
  not conflicting with any other node
• Nonflapping (stable) path P(v,..,destination) is
  the best path of a conflict free node, which does
  not change over time
• Safe path (u,v)P is a permitted path at node u,
  where v is a peer of u, and v is a conflict free
  node and advertising nonflapping path P
• Conflicting safe alternative node is involved in
  a policy conflict and has a safe path
• Conflicting node is involved in a policy
  conflict, and does not have a safe path

36
Convergence Analysis of APMS

• Example
• Conflicting safe-alternative nodes can stabilize
  by
• holding onto their safe paths
• realize through rank change

Node 1 is a conflicting safe-alternative node
with safe path (150) Node 2 is a conflicting
safe-alternative node with safe path (250) Node 3
is a conflicting safe-alternative node with safe
path (350) Node 4 is a conflicting node Node 5 is
a conflict free node with stable path (50)
If node 2 changes its path preference to prefer
(250) more than (2150) node 2 becomes conflict
free node path (250) becomes stable
path path (4250) becomes safe path at
node 4 ..
37
Convergence Analysis of APMS

• Observable Safe Path
• Path P(u,v,..,0) is an observable safe path at a
  conflicting
• safe alternative node u if none of the nodes
  along this path
• observes route flaps due to other conflicts.
• Innermost Conflict

38
Convergence Analysis of APMS

• 3 conflicts with intervening safe paths

560 520
5
2370 20
120 140
1
2
Conflict I
4
4520 40
0
6
640 60
780 70
2
7
Conflict II
Conflict III
3
3140 370
(60) is observable safe path at node 6 (370) is
not an observable safe path Innermost conflict
along (370) is Conflict II
8
9
970 90
890 80
39
Convergence Analysis of APMS

• Theorem
• During the execution of the APMS, the size of the
  set of
• nodes that are conflict free increases
  monotonically.
• Proof
• Sset of conflict free nodes
• S forms a routing tree rooted at the destination,
  and grows as
• the nodes in S advertise their chosen paths.
• By induction show that S grows monotonically
• Basis At the beginning, S. Destination is
  added.
• Hypothesis At step k of the execution, assume
  the size of S
• is n, and up to this point S
  grew monotonically.
• Induction Step Show that at step (k1), the size
  of S will be
• greater than n.

40
Convergence Analysis of APMS
At step (k1)

Case I (u v)Pv is not permitted, then the size
of S will stay the same.
Ex
41
Convergence Analysis of APMS
At step (k1)

• Case II u stabilizes on path (u v)Pv and then
  added to S
• u is a conflict free node
• its path to destination may have node(s) involved
  in conflict(s)

Ex
42
Convergence Analysis of APMS
At step (k1)

Case II u stabilizes on path (u v)Pv, and then
added to S b) u is a
conflicting node, and path (u v)Pv is node us
most preferred path
Ex
420 430
4
Node 2 advertises (20) to node 4
210 20
S
3420 30
2
3
0
2
4
1
0
3
4
130 10
1
43
Convergence Analysis of APMS
S with n nodes already stabilized to their paths

At step (k1)
v advertises Pv to u
pv
0
v
u
Case III (u v)Pv is permitted at u, but u does
not stabilize on this path - (u v)Pv is a
safe path at node u - u must be conflicting
safe alternative node - u performs rank
change and stabilizes on (u v)Pv - for each
conflict there are at least 2 safe alternative
nodes, this is the step where they are
breaking the conflict

560 50
Ex
5
6420 60
450 420
4
6
130 10
2
210 20
1
2
0
320 30
3
44
Convergence Analysis of APMS

• For cases II and III, size of S increases
  monotonically. What about case I?
• If for each node u outside of S, the paths (u
  v)Pv advertised by peers v in S are not
  permitted, then node u converges to epsilon.
• Then all the nodes outside of S will converge to
  epsilon at this point.
• APMS returns with a stable routing tree.
• After finite number of steps, all nodes will be
  in S and APMS converges.

45
Advantages of APMS over Related Work

• 1) GaoRexford Algorithm Infocom01
• 2) GriffinWilfong Algorithm Infocom00
• 3) CobbMusunuri Algorithm Globecomm04

46

• GaoRexford
• Static solution
• Requires a global database to keep relationships
  between ASes
• Global authority checks periodically for
  conformance with guidelines
• Eliminates lots of paths from the beginning
• too restrictive
• Path elimination is the only means of resolving
  conflicts
• Stability of the system is the only goal

• APMS
• Dynamic solution
• Distributed computation
• Allows ASes to adopt to the current state
  conflict free or potentially conflicting
• Path elimination is not the primary means of
  removing conflicts
• Paths are eliminated only during the policy
  conflict control phase
• Helps to limit the number of paths eliminated
• For the stabilized system, there will be as many
  paths as possible
• better connectivity
• more flexibility in path selection
• Both stability and limiting the number of path
  eliminations are concerns of algorithm

47

• GriffinWilfong
• Dynamic solution
• History carried with each Update message
• Potentially very long messages
• High communication overhead
• History may reveal preferences of other ASes
• Cycle in the history is necessary but not
  sufficient condition for divergence
• There may be false positives
• Stability is the only goal
• Path elimination is the only means of resolving
  conflicts
• Eliminated paths cannot be used later under any
  condition
• Cannot differentiate between persistent and
  transient oscillations
• Suggests observing the same loop for a number of
  times in history
• bigger values increase communication overhead
  even more
• smaller values lead to false positives

• APMS
• Dynamic solution
• No communication overhead unless there is a
  topology change
• No privacy concerns
• There may be false positives due to local
  solution
• Paths are not eliminated immediately
• Eliminated paths may be reused after the system
  stabilizes
• Goal is both stability and limiting the number of
  path eliminations
• Changing policies is the primary means of
  resolving conflicts
• Eliminated paths can be used later
• Adapts to every state of the network
• Topology change
• Differentiate between persistent and transient
  oscillations due to topology change
• More effective mechanism for this purpose
• Helps to minimize false positives
• If transient oscillation is not because of
  topology change cannot distinguish
• Uses min_treshold for this purpose
• bigger values lead to longer convergence

48

• CobbMusunuri
• Dynamic solution
• Costs are associated with nodes, not paths
• Aggregates paths through the same node
• One flapping path may cause all the
• alternatives to be rejected
• Costs of the nodes involved in the same conflict
  grows in tandem
• Simultaneous path eliminations
• Solves conflicts through path elimination
• Hard to adapt to the dynamics of the system after
  conflicts disappear
• Suggests resetting costs via diffusing
  computations periodically
• Has to keep min-hop spanning tree for each
  destination
• Cannot be done very often, expensive
• Blindly resetting the costs introduces the
  resolved conflicts back to the system
• Weekly or monthly resets are suggested hoping
  that conflicts resolved by themselves in the
  meantime!
• Local state at each node
• node count per destination

• APMS
• Dynamic solution
• Costs are associated with paths
• Can exactly pinpoint the paths causing problems
• Leads to less preference change and/or path
  suppression
• Costs of the nodes involved in the same conflict
  grows in tandem
• Due to probabilistic approach, nodes do not
  react simultaneously
• Leads to less preference change and/or path
  suppression
• Path elimination is the not the primary means of
  solving conflicts
• Easily adapt to the dynamics of the system
  conflict free or potentially conflicting
• Potentially larger local state at each node

49
Simulation Results
50
Performance Metrics

• Average percentage of paths that are eliminated
  per node among the permitted paths to provide
  stability
• Smaller values indicate better performance
• Eliminating permitted paths may
• strain reachability
• force router to use less preferred path
• Average percentage of the paths whose rank has
  been changed per node
• Smaller values indicate better performance
• higher number of rank changes mess with the
  policies placed for specific purposes
• Average of the percentage of the preference loss
  per node
• Preference loss of a path is the difference
  between its original local preference value and
  its current local preference value
• If a path is in bad path set, its preference loss
  is its original preference value
• Helps quantify the total effect of both path
  elimination and rank change
• Smaller values indicate better performance

51
Performance Metrics

• Number of Update messages exchanged between
  routers
• Indication of stability
• Smaller values show the efficiency of the
  protocols dealing with conflicts
• Number of octets carried with Update messages
• Measures overhead
• Average extra storage used (in bytes)
• For SPVP
• history carried and stored at the routing tables
  along the Updates
• bad path set
• For APMS
• local history, bad path set are main contributors
• per peerStability, per peer keepaliveCount
• Throughput
• Number of packets received in the last
  100sec is averaged over 100sec.
• Average delay for the packets received
• Delay of the packets received in the last
  100sec is averaged over 100sec

52
Simulation Set I

• 15 independent dispute wheels with increasing
  size
• Each node has 3 permitted paths
• Through its clockwise neighbor localPref(100)
• Direct path localPref (80)
• Path through its counterclockwise neighbor
  localPref(40)
• Constant data flow
• Unbounded buffers
• Periodic link failures
• ASes lose connection to 0
• Failures happen at 1000sec, 3000sec
• Failures last 1000sec
• APMS variations
• (min_threshold2, ka_threshold6)
• 1. max_threshold3, topology change diagnostic
  
• 2. max_threshold3, no topology change
  diagnostic
• 3. max_threshold10, topology change diagnostic
  
• 4. max_threshold10, no topology change
  diagnostic
• GriffinWilfong Uses path elimination after
  seeing the same loop twice

53
Results
Average percentage of paths that are eliminated

• APMS with max_threshold3, no topology change
  diagnostic
• False positives
• APMS with max_threshold3, with topology change
  diagnostic
• Big improvement, 0.48
• APMS with max_threshold10
• Resolves conflicts by path rank change
• Minimal path elimination
• SPVP eliminates the flapping paths to deal with
  conflicts, 14.4

• Average percentage of the paths whose rank has
  been changed per node
• Using topology change diagnostic improves
  performance
• For max_threshold10, metric value drops
• from 18 to 7
• There is not a single path elimination
• for this case
• For max_threshold3, metric value drops
• from 15 to 5.4

54
Results

• Average of the percentage of the preference loss
  per node
• SPVP causes loss of 18, only because of
• eliminated paths
• Performance with APMS is always better than
• SPVP
• Larger values of max_threshold along with
• topology change diagnosis significantly
• improves performance to less than 1 loss of
• path preferences.

• Number of Update messages exchanged between
  routers
• Link failure and restoration causes burst
• of Updates
• Failures Paths to the destination are
• withdrawn
• Recovery BGP Session is re-established,
• whole routing tables are
• exchanged
• Metric value for BGP4 for non-fail periods
• is not 0 system does not stabilize

55
Results

• Number of octets carried with Update messages
• SPVP has the highest number of octets
• carried
• APMS shows best performance
• APMSs way of differentiating temporary
• oscillations due to topology change is more
• efficient than SPVPs
• BGP4 has nonzero value for the metric for
• non-fail periods due to instability

• Average extra storage used (in bytes)
• Due to history, SPVP requires much larger
• storage than APMS
• APMS requires 10KB extra storage,
• SPVP requires 200KB-360KB
• For non-fail periods, the metric value is
• higher due to better reachability

56
Simulation Set II

• 7 dispute wheels, some intervening
• AS1, AS2, AS3,
• AS4, AS5, AS6, AS7, AS8, AS9, AS10,
  AS11, AS12,
• AS13, AS14, AS15, AS16, AS17, AS18,
  AS19, AS20, AS21
• No topology change
• Limited buffer size, routing packets are given
  priority over data packets
• Constant data flow
• From servers located at AS0 to the clients
  located at the other ASes
• From servers located at other ASes to the clients
  located at AS0

57
Topology and Path Ranks
58
Results

• Throughput
• APMS is better than SPVP
• Size of Update messages are short
• Does not eliminate as many paths
• APMS is better than BGP4
• Reaches stability quickly
• leads to smaller number of exchanged Updates
• BGP4 performs better than SPVP
• Does not eliminate paths permanently
• Some packets may not reach destination due to
  temporary stability
• Update messages are shorter than SPVP

• Delay
• SPVP causes the highest packet delay
• due to the longest Update messages
• BGP4 performs better than SPVP due to
• shorter Update messages
• BGP4 performs better than APMS since
• APMS forces some nodes to stabilize on
• their longer paths

59
Conclusion

• Proposed new dynamic algorithm, APMS, adapting to
  the system dynamics while resolving policy
  conflicts and overcoming the shortcomings of
  available solutions
• Correctness analysis
• Simulation results
• Future Work
• Transient performance analysis
• More detailed evaluation model
• include IBGP
• Prototype implementation

60
Other Work

• Class based Isolation of UDP, short lived TCP and
  long lived TCP flows
• separate service queues at the routers
• better fairness
• improved predictability for all kinds of flows
• better control over QoS of a particular traffic
  type
• Evaluated scalability vs performance tradeoffs in
  MPLS and IP Routing
• per-packet routing stateless
• Widest Shortest Path per-flow state
• MIRA per-flow state, uses ingress-egress pair
  matrix
• PBR per-flow state, per-class state, uses both
  ingress-egress pairs and traffic matrix
• WSP is the most scalable among per-flow
  algorithms, shows good performance
• PBR is the least scalable, most complex (time and
  space), performance suffers due to unsplitability
  of flows