Download
traffic engineering for isp networks n.
Skip this Video
Loading SlideShow in 5 Seconds..
Traffic Engineering for ISP Networks PowerPoint Presentation
Download Presentation
Traffic Engineering for ISP Networks

Traffic Engineering for ISP Networks

226 Vues Download Presentation
Télécharger la présentation

Traffic Engineering for ISP Networks

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

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

  2. Outline • Overview of Internet routing • IP addressing and forwarding • Interdomain and intradomain routing • Optimization: Tuning routing to the traffic • Optimizing routing given a topology and traffic matrix • Local search to select the integer link weights • Tomography: Inferring the traffic matrix • Estimating traffic matrix from routing and link loads • Conclusion and ongoing work

  3. source destination IP network IP Service Model: Best-Effort Packet Delivery • Packet switching • Send data in packets • Header with source and destination address • Best-effort delivery • Packets may be lost • Packets may be corrupted • Packets may be delivered out of order

  4. 00001100 00100010 10011110 00000101 Packet Delivery Based on Destination IP Address • 32-bit number in dotted-quad notation (12.34.158.5) • Divided into network & host portions (left and right) • 12.34.158.0/24 is a 24-bit prefix with 28 addresses 12 34 158 5 Network (24 bits) Host (8 bits)

  5. Longest-Prefix Match Forwarding • Forwarding tables in IP routers • Maps each IP prefix to next-hop link(s) • Destination-based forwarding • Packet has a destination address • Router identifies longest-matching prefix forwarding table 4.0.0.0/8 4.83.128.0/17 12.0.0.0/8 12.34.158.0/24 126.255.103.0/24 destination 12.34.158.5 outgoing link Serial0/0.1

  6. Where do Forwarding Tables Come From? • Routers have forwarding tables • Map prefix to outgoing link(s) • Entries can be statically configured • E.g., “map 12.34.158.0/24 to Serial0/0.1” • But, this doesn’t adapt • To failures • To new equipment • To the need to balance load • That is where routing protocols come in…

  7. Two-Tiered Internet Routing Architecture • Goal: distributed management of resources • Internetworking of multiple networks • Networks under separate administrative control • Solution: two-tiered routing architecture • Intradomain: inside a region of control • Okay for routers to share topology information • Routers configured to achieve a common goal • Interdomain: between regions of control • Not okay to share complete information • Networks may have different/conflicting goals

  8. Autonomous Systems (ASes) • Autonomous Systems • Distinct regions of administrative control • Routers and links managed by an institution • Service provider, company, university, … • AS hierarchy • Tier-1 provider with national or global backbone • Regional provider with smaller backbone • Campus or corporate network • Interaction between ASes • Internal topology is not shared between ASes • … but, neighboring ASes interact to coordinate routing

  9. AS Numbers (ASNs) Currently around 25,000 in use. • Level 3: 1 • MIT: 3 • Harvard: 11 • Yale: 29 • Princeton: 88 • AT&T: 7018, 6341, 5074, … • UUNET: 701, 702, 284, 12199, … • Sprint: 1239, 1240, 6211, 6242, … • … ASNs represent units of routing policy

  10. Traffic Traverses Multiple ASes Path: 6, 5, 4, 3, 2, 1 4 3 5 2 6 7 1 Web server Client

  11. 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 AS’s network operator • Path selection: which of the paths to use? • Path export: which neighbors to tell? “I can reach 12.34.158.0/24 via AS 1” “I can reach 12.34.158.0/24” 2 3 1 data traffic data traffic 12.34.158.5

  12. Interior Gateway Protocol (Within an AS) • Routers flood information to learn the topology • Routers determine “next hop” to reach other routers… • By computing shortest paths based on the link weights • Link weights configured by the network operator 2 1 3 1 3 2 1 5 12.34.158.0/24 Serial0/0.1 4 3

  13. Constructing the Forwarding Table • Two routing protocols • BGP: learn the external route at some border router • IGP: learn outgoing link on path to other router • Router joins the data • Prefix 12.34.158.0/24 reached through red router • Red router reached via link Serial0/0.1 • Forwarding entry: 12.34.158.0/24  Serial0/0.1 • Router forwards packets • Lookup destination 12.34.158.5 in table • Forward packet out link Serial0/0.1

  14. Topology information is flooded within the routing domain Best end-to-end paths are computed locally at each router. Best end-to-end paths determine next-hops. Based on minimizing some notion of distance Works only if policy is shared and uniform Examples: OSPF, IS-IS Each router knows little about network topology Only best next-hops are chosen by each router for each destination. Best end-to-end paths result from composition of all next-hop choices Does not require any notion of distance Does not require uniform policies at all routers Examples: RIP, BGP Two Kinds of Routing Protocols Link State Vectoring

  15. Optimization: Tuning Routing to the Traffic

  16. 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 2 1 3 1 3 2 3 1 5 4 3

  17. 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

  18. 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

  19. Big Picture: Measure, Model, and Control Network-wide “what if” model Offered traffic Changes to the network Topology/ Configuration measure control Operational network

  20. 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?

  21. 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

  22. 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 • wlis weight on unidirectional link l • Pi,j,lis fraction of traffic from i to j traversing link l

  23. 0.25 0.25 0.5 1.0 1.0 0.25 0.25 0.5 0.5 0.5 Multiple Shortest Paths With Even Splitting Values of Pi,j,l

  24. f(x) x 1 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)) • minl(S f(ul/cl))

  25. 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 can’t 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

  26. 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

  27. 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

  28. 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

  29. Application to AT&T’s 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 AT&T 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

  30. Example from My Visit to AT&T’s 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 • AT&T 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

  31. 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 network’s 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

  32. 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 good • Near-optimal solutions that minimize disruptions

  33. 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

  34. Tomography: Inferring the Traffic Matrix

  35. 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

  36. Inference: Network Tomography From link counts to the traffic matrix Sources 5Mbps 3Mbps 4Mbps 4Mbps Destinations

  37. 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 p’s 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)

  38. 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 • Doesn’t work all that well in practice…

  39. 6 3 Approach Used at AT&T: 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 9 20 10

  40. Approach Used at AT&T: Tomo-gravity • Problem with gravity model • Gravity model ignores the load on the inside links • Gravity assumption isn’t 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

  41. Conclusions • Managing IP networks is challenging • Routers don’t adapt on their own to congestion • Routers don’t reveal much information about traffic • Measurement provides a network-wide view • Topology • Traffic matrix • Optimization enables the network to adapt • Inferring the traffic matrix from the link loads • Optimizing the link weights based on the traffic matrix

  42. New Research Direction: Design for Manage-ability • Two main parts of network management • Control: optimization • Measurement: tomography • 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