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Title: Traffic Engineering for ISP Networks


1
Traffic Engineering for ISP Networks
  • Jennifer Rexford
  • Computer Science Department
  • Princeton University
  • http//www.cs.princeton.edu/jrex

2
Outline
  • Internet routing
  • Overview of the Internet routing architecture
  • Shortest-path link-state routing between edge
    routers
  • Optimization Tune routing to the traffic
  • Optimizing routing given a topology and traffic
    matrix
  • Local search to select the integer link weights
  • Design for optimizability Design routing
    protocol
  • Optimal traffic engineering with link-state
    routing
  • Tomography Infer the traffic matrix
  • Estimating traffic matrix from routing and link
    load

3
Internet Routing
4
Autonomous Systems (ASes)
  • Internet is divided into Autonomous Systems
  • Distinct regions of administrative control
  • Routers/links managed by a single institution
  • Service provider, company, university,
  • Hierarchy of Autonomous Systems
  • Large, tier-1 provider with a nationwide backbone
  • Medium-sized regional provider with smaller
    backbone
  • Small network run by a single company or
    university
  • Cooperate to ensure end-to-end reachability

5
Interdomain Routing
  • AS-level topology
  • Destinations are IP prefixes (e.g., 12.0.0.0/8)
  • Nodes are Autonomous Systems (ASes)
  • Edges are links and business relationships

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Web server
Client
6
Example Backbone Abilene Internet2 Newtork
7
Points-of-Presence (PoPs)
  • Inter-PoP links
  • Long distances
  • High bandwidth
  • Intra-PoP links
  • Short cables between racks or floors
  • Aggregated bandwidth
  • Links to other networks
  • Wide range of media and bandwidth

Inter-PoP
Intra-PoP
Other networks
8
Intradomain Routing Shortest-Path Routing
  • Path-selection model
  • Destination-based
  • Load-insensitive (e.g., static link weights)
  • Minimum hop count or sum of link weights

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Computing Shortest Paths Link-State Routing
  • Topology discovery
  • Routers flood information to learn the topology
  • Each router constructs a link-state database

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Computing Shortest Paths Link-State Routing
  • Shortest-path computation
  • Each router runs Dijkstras shortest-path
    algorithm
  • Computes the next hop to reach other routers

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11
Computing Shortest Paths Link-State Routing
  • Packet forwarding
  • Each router maintains a forwarding table
  • To forward incoming packets to the right next-hop
    link

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12
Our Focus Traffic Engineering (TE)
  • Adjusting routing to the flow of traffic
  • How should network administrators run their
    networks?
  • Specifically, how should they set the link
    weights?
  • Designing protocols for better traffic
    engineering
  • How should future routing protocols be designed?
  • Specifically, how to make TE efficient and easy?
  • Collecting measurements of the offered traffic
  • How should administrators learn the traffic
    matrix?
  • Specifically, how to infer the matrix from link
    loads?

13
Optimization Tuning Routing to the Traffic
  • Joint work with Bernard Fortz and Mikkel Thorup
  • http//www.cs.princeton.edu/jrex/papers/ieeecomm0
    2.pdf
  • http//www.cs.princeton.edu/jrex/papers/opthand04
    .pdf

14
Link Weights Control the Flow of Traffic
  • Routers compute paths
  • Shortest paths as sum of link weights
  • Operators set the link weights
  • To control where the traffic goes

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15
Heuristics for Setting the Link Weights
  • Proportional to physical distance
  • Cross-country links have higher weights than
    local ones
  • Minimizes end-to-end propagation delay
  • Inversely proportional to link capacity
  • Smaller weights for higher-bandwidth links
  • Attracts more traffic to links with more capacity
  • Tuned based on the offered traffic
  • Network-wide optimization of weights based on
    traffic
  • Directly minimizes key metrics like max link
    utilization

16
Why Are the Link Weights Static?
  • Strawman alternative load-sensitive routing
  • Link metrics based on traffic load
  • Flood dynamic metrics as they change
  • Adapt automatically to changes in offered load
  • Reasons why this is typically not done
  • Delay-based routing unsuccessful in the early
    days
  • Oscillation as routers adapt to out-of-date
    information
  • Most Internet transfers are very short-lived
  • Research and standards work continues
  • but operators have to do what they can today

17
Big Picture Measure, Model, and Control
Network-wide what if model
Offered traffic
Topology/ Configuration
Changes to the network
measure
control
Operational network
18
Traffic Engineering in an ISP Backbone
  • Topology
  • Connectivity and capacity of routers and links
  • Traffic matrix
  • Offered load between points in the network
  • Link weights
  • Configurable parameters for Interior Gateway
    Protocol
  • Performance objective
  • Balanced load, low latency, service level
    agreements
  • Question Given the topology and traffic matrix
    in an IP network, which link weights should be
    used?

19
Key Ingredients of Our Approach
  • Measurement
  • Topology monitoring of the routing protocols
  • Traffic matrix widely deployed traffic
    measurement
  • Network-wide models
  • Representations of topology and traffic
  • What-if models of shortest-path routing
  • Network optimization
  • Efficient algorithms to find good configurations
  • Operational experience to identify key
    constraints

20
Formalizing the Optimization Problem
  • Input graph G(R,L)
  • R is the set of routers
  • L is the set of unidirectional links
  • cl is the capacity of link l
  • Input traffic matrix
  • Mi,j is traffic load from router i to j
  • Output setting of the link weights
  • wl is weight on unidirectional link l
  • Pi,j,l is fraction of traffic from i to j
    traversing link l

21
Multiple Shortest Paths With Even Splitting
Values of Pi,j,l
22
Defining the Objective Function
  • Computing the link utilization
  • Link load ul Si,j Mi,j Pi,j,l
  • Utilization ul/cl
  • Objective functions
  • min(maxl(ul/cl))
  • min(Sl f(ul/cl))

23
Complexity of the Optimization Problem
  • NP-hard optimization problem
  • No efficient algorithm to find the link weights
  • Even for the simple convex objective functions
  • Why cant we just do multi-commodity flow?
  • E.g., solve the multi-commodity flow problem
  • and the link weights pop out as the dual
  • Because IP routers cannot split arbitrarily over
    ties
  • What are the implications?
  • Have to resort to searching through weight
    settings

24
Optimization Based on Local Search
  • Start with an initial setting of the link weights
  • E.g., same integer weight on every link
  • E.g., weights inversely proportional to link
    capacity
  • E.g., existing weights in the operational network
  • Compute the objective function
  • Compute the all-pairs shortest paths to get
    Pi,j,l
  • Apply the traffic matrix Mi,j to get link loads
    ul
  • Evaluate the objective function from the ul/cl
  • Generate a new setting of the link weights

repeat
25
Making the Search Efficient
  • Avoid repeating the same weight setting
  • Keep track of past values of the weight setting
  • or keep a small signature (e.g., a hash) of
    past values
  • Do not evaluate a weight setting if signatures
    match
  • Avoid computing the shortest paths from scratch
  • Explore weight settings that changes just one
    weight
  • Apply fast incremental shortest-path algorithms
  • Limit the number of unique values of link weights
  • Do not explore all 216 possible values for each
    weight
  • Stop early, before exploring the whole search
    space

26
Incorporating Operational Realities
  • Minimize number of changes to the network
  • Changing just 1 or 2 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 dependence on measurement accuracy
  • Good weights remain good, despite random noise
  • Limit frequency of changes to the weights
  • Joint optimization for day and night traffic
    matrices

27
Application to ATTs Backbone Network
  • Performance of the optimized weights
  • Search finds a good solution within a few minutes
  • Much better than link capacity or physical
    distance
  • Competitive with multi-commodity flow solution
  • How ATT changes the link weights
  • Maintenance done every night from midnight to 6am
  • Predict effects of removing link(s) from the
    network
  • Reoptimize the link weights to avoid congestion
  • Configure new weights before disabling equipment

28
Example from My Visit to ATTs Operations Center
  • Amtrak repairing/moving part of the train track
  • Need to move some of the fiber optic cables
  • Or, heightened risk of the cables being cut
  • Amtrak notifies us of the time the work will be
    done
  • ATT engineers model the effects
  • Determine which IP links go over the affected
    fiber
  • Pretend the network no longer has these links
  • Evaluate the new shortest paths and traffic flow
  • Identify whether link loads will be too high

29
Example Continued
  • If load will be too high
  • Reoptimize the weights on the remaining links
  • Schedule the time for the new weights to be
    configured
  • Roll back to the old weight setting after Amtrak
    is done
  • Same process applied to other cases
  • Assessing the networks risk to possible failures
  • Planning for maintenance of existing equipment
  • Adapting the link weights to installation of new
    links
  • Adapting the link weights in response to traffic
    shifts

30
Conclusions on Traffic Engineering
  • IP networks do not adapt on their own
  • Routers compute shortest paths based on static
    weights
  • Service providers need to adapt the weights
  • Due to failures, congestion, or planned
    maintenance
  • Leads to an interesting optimization problems
  • Optimize link weights based on topology and
    traffic
  • Optimization problem is computationally difficult
  • Forces the use of efficient local-search
    techniques
  • Results of the local search are pretty good
  • Near-optimal solutions that minimize disruptions

31
Ongoing Work
  • Robust link-weight assignments
  • Link/node failures
  • Range of traffic matrices
  • More complex routing models
  • Hot-potato routing
  • BGP routing policies
  • Interaction between ASes
  • Inter-AS negotiation for joint optimization
  • Grappling with scalability and trust issues

32
Design for Optimizability Optimal Link-State
Routing Protocol
  • Joint work with Dahai Xu
  • and Mung Chiang
  • http//www.cs.princeton.edu/jrex/papers/pefti.pdf

33
Revisiting TE With Link-State Routing Protocols
  • Advantages of link weights
  • One parameter for each unidirectional link
  • Hop-by-hop forwarding (no tunneling, no per-flow
    state)
  • New routes computed automatically after failure
  • Changing just a few weights can alleviate
    congestion
  • Disadvantages of link weights
  • Computationally expensive optimization
  • Suboptimal distribution of traffic
  • (Disruptions when changing the link weights)

34
Example of Inefficient TE
  • Simple topology
  • Demand of 300 units
  • All on top path 300 utilization of top path
  • All on bottom path 150 utilization of bottom
    path
  • Even splitting 150 on top path, 75 on bottom

c1 100
t
s
c2 200
35
Stepping Back Design for Optimizability
  • Two research approaches
  • Bottom up do the best with what you have
  • Top down design systems that are easier to
    manage
  • Design for manage-ability
  • If you are both the professor and the student,
    you create exam questions that are easy to
    answer. Mung Chiang
  • Knowing what we know now
  • How should intradomain routing protocols work
  • to make TE more efficient and hopefully easier?

36
Optimal TE With Multicommodity Flow
  • Problem with shortest-path routing
  • Inflexible even splitting over shortest paths
  • Optimal distribution of traffic
  • Send traffic over any paths in any proportions
  • Using tunneling to force traffic on the paths
  • Realizable with MultiProtocol Label Switching
    (MPLS)
  • Disadvantage of MPLS high overhead
  • Large number of paths between pairs of routers
  • Must adapt the splitting ratios after each failure

37
Can We Have Link-State Routing and Optimal TE?
  • Link-state routing and hop-by-hop forwarding
  • Single weight on each link
  • Local rule to compute splitting over paths
  • Each router forwards based only on the
    destination
  • Link-state routing ! shortest-path routing
  • Routers could use other traffic-splitting rules
  • as long as they are locally computable
  • only from the link weights

38
Forward Packet Based on Link Weights
  • Available information at router u
  • wu,v weights for all links
  • dut shortest distance from u to t
  • hu,vt distance gap (dvt wu,v dut)

distance gap of 1
2
1
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5
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distance gap of 0
39
Traffic-Splitting Function
  • Relative flow distributed on outgoing links
  • G(hu,vt) proportion sent out link v toward t
  • Split traffic to t in proportion
  • Even splitting
  • G(hu,vt) is 1 if hu,vt 0 (all traffic on
    shortest paths)
  • G(hu,vt) is 0 if hu,vt gt 0 (no traffic on longer
    paths)

G(hu,vt)
SG(hu,jt)
40
Exponential Splitting
  • Exponentially diminishing traffic on longer paths
  • Proportion on path i proportional to exp(-pi)
  • where pi is the cost of path i

41
Optimal TE
  • A surprising result
  • This kind of link-state routing can achieve
    optimal TE
  • Optimality
  • Can realize the multicommodity flow traffic
    distribution
  • Expressible in terms of settings of link weights
  • Efficient algorithm
  • Computationally tractable to compute optimal
    weights
  • for a given traffic matrix and capacitated
    topology

42
Intuition Behind the Theory
Realizable with link-state routing
Optimal flow routing
Feasible flow routing
43
Finding Link-State Protocols That Achieve Optimal
TE
  • Need an additional objective function
  • To find solutions expressible in terms of link
    weights
  • But we already have an objective function
  • So, how can we add another one?
  • First, solve the original optimization problem
  • To determine the load on each link at optimality
  • i.e., the necessary capacity of each link
  • Then, solve a second optimization problem
  • On this new topology, with our new objective

44
TE Optimization Problem Compute Necessary
Capacity
  • Convex objective
  • Min sum f() over all links
  • Constraints
  • Flow conservation must carry the traffic matrix
  • Capacity constraint cannot exceed link capacity
  • Variables
  • Flow along each path
  • Given
  • Traffic matrix and link capacities

45
New Optimization Problem
  • Necessary link capacity
  • Flow on link u,v in the multicommdity-flow
    solution
  • becomes the capacity of the link in the new
    problem
  • In the new optimization problem
  • Any feasible solution is optimal
  • relative to the original optimization problem
  • So, now we can pick a new objective
  • Key intuition maximizing entropy

46
Entropy Maximization
  • Assume we could enumerate all paths from s to t
  • (Though in practice this wouldnt be practical)
  • Entropy
  • xks,t fraction of traffic from s to t put on
    path k
  • z(x) - x log(x) entropy function
  • New objective maximize entropy
  • S Mi,j (S z(xks,t))

47
High-Level Overview of the Details
  • NEM problem always has a solution
  • Earlier multicommodity flow solution
  • Solving directly is not efficient
  • Need to avoid enumerating all the paths
  • Solving with dual decomposition
  • Derivation leads to the exponential function
  • for splitting traffic over the multiple paths
  • Derivation also leads to weight-setting algorithm
  • Computationally efficient, better than local
    search

48
Conclusions
  • Protocols induce optimization problems
  • E.g., setting link weights to do traffic
    engineering
  • Complexity of the optimization problems
  • A symptom that the protocol is not quite right
  • E.g., NP-hard problem and suboptimal traffic flow
  • Design for optimizability
  • Design the protocol to be easy to optimize
  • using optimization theory as a protocol design
    tool

49
Tomography Inferring the Traffic Matrix
  • Work by Yin Zhang, Matthew Roughan, Nick
    Duffield, and Albert Greenberg
  • http//www.cs.utexas.edu/yzhang/papers/tomogravit
    y-sigm03.pdf

50
Computing the Traffic Matrix Mi,j
  • Hard to measure the traffic matrix
  • IP networks transmit data as individual packets
  • Routers do not keep traffic statistics, except
    link utilization on (say) a five-minute time
    scale
  • Need to infer the traffic matrix Mi,j from
  • Current topology G(R,L)
  • Current routing Pi,j,l
  • Current link load ul
  • Link capacity cl

51
Inference Network Tomography
From link counts to the traffic matrix
Sources
3Mbps
5Mbps
4Mbps
4Mbps
Destinations
52
Tomography Formalizing the Problem
  • Ingress-egress pairs
  • p is a ingress-egress pair of nodes (i,j)
  • xp is the (unknown) traffic volume for this pair
    Mi,j
  • Routing
  • Plp is proportion of ps traffic that traverses l
  • Links in the network
  • l is a unidirectional edge
  • ul is the observed traffic volume on this link
  • Relationship u Px (work backwards to get x)

53
Tomography One Observation Not Enough
  • Linear system of n nodes is underdetermined
  • Number of links e is around O(n)
  • Number of ingress-egress 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 Poisson iid counts
  • Maximum likelihood estimation to infer matrix
  • Doesnt work all that well in practice

54
Approach Used at ATT Tomo-gravity
  • Gravitational assumption
  • Ingress point a has traffic via
  • Egress point b has traffic veb
  • Pair (a,b) has traffic proportional to via veb

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21
10
55
Approach Used at ATT Tomo-gravity
  • Problem with gravity model
  • Gravity model ignores the load on the inside
    links
  • Gravity assumption isnt always 100 correct
  • Resulting traffic matrix might not satisfy the
    link loads
  • Combining the two techniques
  • Gravity find a traffic matrix using the gravity
    model
  • Tomography find the family of traffic matrices
    consistent with all link load statistics
  • Tomo-gravity find the tomography solution that
    is closest to the output of the gravity model
  • Works extremely well (and fast) in practice

56
Conclusions on Tomography
  • Routers dont reveal much information about
    traffic
  • Measurement provides a network-wide view
  • E.g., network topology and traffic matrix
  • Available data induces a tomography problem
  • Input network topology, routing, and link loads
  • Output inferred traffic matrix
  • Design for tomography?
  • Design future monitoring systems to induce
    easier-to-solve tomography problems?

57
Conclusions
  • Internet routing is a rich problem space
  • Designed incrementally as Internet evolved
  • Not designed with network management in mind
  • Network management bottom up
  • Working with what you have
  • Tuning link weights, and inferring traffic
    matrices
  • Exciting new area design for manageability
  • Protocols that are easy to tune
  • Measurements that make inference easy
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