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Traffic Engineering for ISP Networks

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Title: Traffic Engineering for ISP Networks


1
Traffic Engineering for ISP Networks
  • Jennifer Rexford
  • IP Network Management and Performance
  • ATT Labs - Research Florham Park, NJ
  • http//www.research.att.com/jrex

2
Outline
  • Internet routing protocols
  • Traffic engineering using traditional protocols
  • Optimizing network configuration to prevailing
    traffic
  • Requirements for topology, routing, and traffic
    info
  • Traffic demands
  • Volume of load between edges of the network
  • Technique for measuring the traffic demands
  • Route optimization
  • Tuning the link weights to the offered traffic
  • Incorporating various operational constraints
  • Other ways of measuring the traffic matrix
  • Conclusions

3
Autonomous Systems (ASes)
  • Internet divided into ASes
  • Distinct regions of administrative control
    (14,000)
  • Routers and links managed by a single institution
  • Service provider, company, university,
  • Hierarchy of ASes
  • Large, tier-1 provider with a nationwide backbone
  • Medium-sized regional provider with smaller
    backbone
  • Small network run by a single company or
    university
  • Interaction between ASes
  • Internal topology is not shared between ASes
  • but, neighbors interact to coordinate routing

4
Path Traversing Multiple ASes
Path 6, 5, 4, 3, 2, 1
4
3
5
2
6
7
1
Web server
Client
5
Interdomain Routing Border Gateway Protocol
  • ASes exchange info about who they can reach
  • IP prefix block of destination IP addresses
  • AS path sequence of ASes along the path
  • Policies configured by the ASs network operator
  • Path selection which of the paths to use?
  • Path export which neighbors to tell?

Client (12.34.158.5)
6
Intradomain Routing OSPF or IS-IS
  • Shortest path routing based on link weights
  • Routers flood the link-state information to each
    other
  • Routers compute the next hop to reach other
    routers
  • Weights configured by the ASs network operator
  • Simple heuristics link capacity or physical
    distance
  • Traffic engineering tuning the link weights to
    the traffic

7
Motivating Problem Congested Link
  • Detecting that a link is congested
  • Utilization statistics reported every five
    minutes
  • Sample probe traffic suffers degraded performance
  • Customers complain (via the telephone network?)
  • Reasons why the link might be congested
  • Increase in demand between some set of src-dest
    pairs
  • Failed router/link within the AS causes routing
    change
  • Failure/reconfiguration in another AS changes
    routes
  • Challenges
  • Know the cause, not just the manifestations
  • Predict the effects of possible changes to link
    weights

8
Traffic Engineering in an ISP Backbone
  • Topology of the ISP backbone
  • Connectivity and capacity of routers and links
  • Traffic demands
  • Offered load between points in the network
  • Routing configuration
  • Link weights for selecting paths
  • Performance objective
  • Balanced load, low latency,
  • Question Given the topology and traffic demands
    in an IP network, what link weights should be
    used?

9
Modeling Traffic Demands
  • Volume of traffic V(s,d,t)
  • From a particular source s
  • To a particular destination d
  • Over a particular time period t
  • Time period
  • Performance debugging -- minutes or tens of
    minutes
  • Time-of-day traffic engineering -- hours or days
  • Network design -- days to weeks
  • Sources and destinations
  • Individual hosts -- interesting, but huge!
  • Individual prefixes -- still big not seen by any
    one AS!
  • Individual edge links in an ISP backbone --
    hmmm.

10
Traffic Matrix
Traffic matrix V(in,out,t) for all pairs (in,out)
in
out
11
Problem Hot Potato Routing
  • ISP is in the middle of the Internet
  • Multiple connections to multiple other ASes
  • Egress point depends on intradomain routing
  • Problem with point-to-point models
  • Want to predict impact of changing intradomain
    routing
  • But, a change in weights may change the egress
    point!

12
Demand Matrix Motivating Example
Big Internet
User Site
Web Site
13
Coupling of Inter and Intradomain Routing
AS 2
Web Site
User Site
U
AS 3
AS 1
AS 4, AS 3, U
AS 4
14
Intradomain Routing Hot Potato
Zoom in on AS1
OUT 1
25
110
110
300
200
75
300
OUT 2
10
110
110
IN
OUT 3
Hot-potato routing change in internal routing
(link weights) configuration changes flow exit
point!
15
Traffic Demand Multiple Egress Points
  • Definition V(in, out, t)
  • Entry link (in)
  • Set of possible egress links (out)
  • Time period (t)
  • Volume of traffic (V(in,out,t))
  • Computing the traffic demands
  • Measure the traffic where it enters the ISP
    backbone
  • Identify the set of egress links where traffic
    could leave
  • Sum over all traffic with same in, out, and t

16
Traffic Mapping Ingress Measurement
  • Packet measurement (e.g., Netflow, sampling)
  • Ingress point i
  • Destination prefix d
  • Traffic volume Vid

destination
ingress
d
i
17
Traffic Mapping Egress Point(s)
  • Routing data (e.g., forwarding tables)
  • Destination prefix d
  • Set of egress points ed

destination
d
18
Traffic Mapping Combining the Data
  • Combining multiple types of data
  • Traffic Vid (ingress i, destination prefix d)
  • Routing ed (set ed of egress links toward d)
  • Combining sum over Vid with same ed

ingress
egress set
i
19
Application on the ATT Backbone
  • Measurement data
  • Netflow data (ingress traffic)
  • Forwarding tables (sets of egress points)
  • Configuration files (topology and link weights)
  • Effectiveness
  • Ingress traffic could be matched with egress sets
  • Simulated flow of traffic consistent with link
    loads
  • Challenges
  • Loss of Netflow records during delivery (can
    correct for it!)
  • Egress set changes between table dumps (not very
    many)
  • Topology changes between configuration dumps
    (just one!)

20
Traffic Analysis Results
  • Small number of demands contribute most traffic
  • Optimize routes for just the heavy hitters
  • Measure a small fraction of the traffic
  • Must watch out for changes in load and set of
    exit links
  • Time-of-day fluctuations
  • Reoptimize routes a few times a day (three?)
  • Traffic (in)stability
  • Select routes that are good for different demand
    sets
  • Reoptimize routes after sudden changes in load

21
Three Traffic Demands in San Francisco
22
Underpinnings of the Optimization
  • Route prediction engine (what-if tool)
  • Model the influence of link weights on traffic
    flow
  • Select a closest exit point based on link weights
  • Compute shortest path(s) based on link weights
  • Capture splitting over multiple shortest paths
  • Sum the traffic volume traversing each link
  • Objective function
  • Rate the goodness of a setting of the link
    weights
  • E.g., max link utilization or sum of
    exp(utilization)

23
Weight Optimization
  • Local search
  • Generate a candidate setting of the weights
  • Predict the resulting load on the network links
  • Compute the value of the objective function
  • Repeat, and select solution with lowest objective
    function
  • Efficient computation
  • Explore the neighborhood around good solutions
  • Exploit efficient incremental graph algorithms
  • Performance results on ATTs network
  • Much better than simple heuristics (link
    capacity, distance)
  • Quite competitive with multi-commodity flow
    solution

24
Incorporating Operational Realities
  • Minimize changes to the network
  • Changing just one or two link weights is often
    enough
  • Tolerate failure of network equipment
  • Weights settings usually remain good after
    failure
  • or can be fixed by changing one or two weights
  • Limit the number of distinct weight values
  • Small number of integer values is sufficient
  • Limit dependence on accuracy of traffic demands
  • Good weights remain good after introducing random
    noise
  • Limit frequency of changes to the weights
  • Joint optimization for day and night traffic
    matrices

25
Ways to Populate the Domain-Wide Models
  • Mapping assumptions about routing
  • Traffic data packet/flow statistics at network
    edge
  • Routing data egress point(s) per destination
    prefix
  • Inference assumptions about traffic and routing
  • Traffic data byte counts per link (over time)
  • Routing data path(s) between each pair of nodes
  • Direct observation no assumptions
  • Traffic data packet samples at every link
  • Routing data none

26
Inference Network Tomography
From link counts to the traffic matrix
Sources
3Mbps
5Mbps
4Mbps
4Mbps
Destinations
27
Tomography Formalizing the Problem
  • Source-destination pairs
  • p is a source-destination pair of nodes
  • xp is the (unknown) traffic volume for this pair
  • Routing
  • Rlp 1 if link l is on the path for src-dest
    pair p
  • Or, Rlp is the proportion of ps traffic that
    traverses l
  • Links in the network
  • l is a unidirectional edge
  • yl is the observed traffic volume on this link
  • Relationship y Rx (now work back to get x)

28
Tomography Single Observation is Insufficient
  • Linear system is underdetermined
  • Number of nodes n
  • Number of links e is around O(n)
  • Number of src-dest pairs c is O(n2)
  • Dimension of solution sub-space at least c - e
  • Multiple observations are needed
  • k independent observations (over time)
  • Stochastic model with src-dest counts Poisson
    i.i.d
  • Maximum likelihood estimation to infer traffic
    matrix
  • Vardi, Network Tomography, JASA, March 1996

29
Tomography Limitations
  • Cannot handle packet loss or multicast traffic
  • Assumes traffic flows from an entry to an egress
  • Statistical assumptions dont match IP traffic\
  • Poisson, Gaussian
  • Significant error even with large of samples
  • Faulty assumptions and traffic changes over time
  • High computation overhead for large networks
  • Large matrix inversion problem

30
Promising Extension Gravity Models Zhang,
Roughan, Duffield, Greenberg
  • Gravitational assumption
  • Ingress point a has traffic via
  • Egress point b has traffic veb
  • Pair (a,b) has traffic proportional to via veb
  • Incorporating hot-potato routing
  • Combine traffic across egress points to the same
    peer
  • Gravity divides as traffic proportional to peer
    loads
  • Hot potato identifies single egress point for
    as traffic
  • Experimental results on ATT network
  • Reasonable accuracy, especially for large (a,b)
    pairs
  • Sufficient accuracy for traffic engineering
    applications

31
Conclusions
  • Our approach
  • Measure network-wide view of traffic and routing
  • Model data representations and what-if tools
  • Control intelligent changes to operational
    network
  • Application in ATTs network
  • Capacity planning
  • Customer acquisition
  • Preparing for maintenance activities
  • Comparing different routing protocols
  • Ongoing work interdomain traffic engineering

32
To Learn More
  • Overview papers
  • Traffic engineering for IP networks
    (http//www.research.att.com/jrex/papers/ieeenet
    00.ps)
  • Traffic engineering with traditional IP routing
    protocols(http//www.research.att.com/jrex/pape
    rs/ieeecomm02.ps)
  • Traffic measurement
  • "Measurement and analysis of IP network usage and
    behavior(http//www.research.att.com/jrex/paper
    s/ieeecomm00.ps)
  • Deriving traffic demands for operational IP
    networks(http//www.research.att.com/jrex/paper
    s/ton01.ps)
  • Topology and configuration
  • IP network configuration for intradomain traffic
    engineering (http//www.research.att.com/jrex/pa
    pers/ieeenet01.ps)
  • Intradomain route optimization
  • Internet traffic engineering by optimizing OSPF
    weights(http//www.ieee-infocom.org/2000/papers/
    165.ps)
  • Optimizing OSPF/IS-IS weights in a changing
    world(http//www.research.att.com/mthorup/PAPER
    S/change_ospf.ps)
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