Intradomain Traffic Engineering By Behzad Akbari These slides
Intradomain Traffic Engineering By Behzad Akbari These slides are based in part upon slides of J. Rexford (Princeton university)
Introduction n Do IP Networks Manage Themselves? q In some sense, yes: n n q But, does the network run efficiently? n n q Congested link when idle paths exist? High-delay path when a low-delay path exists? How should routing adapt to the traffic? n n q TCP senders send less traffic during congestion Routing protocols adapt to topology changes Avoiding congested links in the network Satisfying application requirements (e. g. , delay) … essential questions of traffic engineering
Traffic Engineering n What is traffic engineering? q n Two fundamental approaches to adaptation q q n Control and optimization of routing, to steer traffic through the network in the most effective way. Adaptive routing protocols n Distribute traffic and performance measurements n Compute paths based on load, and requirements Adaptive network-management system n Collect measurements of traffic and topology n Optimize the setting of the “static” parameters Big debates still today about the right answer
Effect of link weights n n n (a) Initial configuration with unit weights (b) Local change to the weight of the congested link (c) Global optimization of the link weights (a) (b) (c)
Three Alternatives for traffic engineering n Load-sensitive routing at packet level q q q n Load-sensitive routing at circuit level q q q n Routers receive feedback on load and delay Routers re-compute their forwarding tables Fundamental problems with oscillation Routers receive feedback on load and delay Router compute a path for the next circuit Less oscillation, as long as circuits last for a while Traffic engineering as a management problem q q q Routers compute paths based on “static” values Network management system sets the parameters Acting on network-wide view of traffic and topology
Load-Sensitive Routing Protocols: Pros and Cons n Advantages q q q n Efficient use of network resources Satisfying the performance needs of end users Self-managing network takes care of itself Disadvantages q q q Higher overhead on the routers Long alternate paths consume extra resources Instability from reacting to out-of-date information
Traffic Engineering as a Network-Management Problem
Using Traditional Routing Protocols n n Routers flood information to learn topology q Determine “next hop” to reach other routers… q Compute shortest paths based on link weights Link weights configured by network operator 2 3 2 1 1 1 3 5 4 3
Approaches for Setting the Link Weights n Conventional static heuristics q Proportional to physical distance n n q Inversely proportional to link capacity n n n Cross-country links have higher weights Minimizes end-to-end propagation delay Smaller weights for higher-bandwidth links Attracts more traffic to links with more capacity Tune the weights based on the offered traffic q q Network-wide optimization of the link weights Directly minimizes metrics like max link utilization
Measure, Model, and Control Network-wide “what if” model Offered Topology/ traffic Configuration measure Changes to the network control Operational network
Traffic Engineering in an ISP Backbone n Topology q n Traffic matrix q n Configurable parameters for routing protocol Performance objective q n Offered load between points in the network Link weights q n Connectivity and capacity of routers and links Balanced load, low latency, service level agreements … Question: Given the topology and traffic matrix, which link weights should be used?
Key Ingredients of the Approach n Instrumentation q q n Network-wide models q q n Topology: monitoring of the routing protocols Traffic matrix: fine-grained traffic measurement Representations of topology and traffic “What-if” models of shortest-path routing Network optimization q q Efficient algorithms to find good configurations Operational experience to identify key constraints
Formalizing the Optimization Problem n Input: graph G(R, L) q q q n Input: traffic matrix q n R is the set of routers L is the set of unidirectional links cl is the capacity of link l Mi, j is traffic load from router i to j Output: setting of the link weights q q wl is weight on unidirectional link l Pi, j, l is fraction of traffic from i to j traversing link l
Multiple Shortest Paths With Even Splitting 0. 25 0. 5 1. 0 0. 25 0. 5 Values of Pi, j, l
Complexity of the Optimization Problem n NP-complete optimization problem q q n No efficient algorithm to find the link weights Even for simple objective functions What are the implications? q Have to resort to searching through weight settings
Optimization Based on Local Search n n n Start with an initial setting of the link weights q E. g. , same integer weight on every link q E. g. , weights inversely proportional to capacity q E. g. , existing weights in the operational network Compute the objective function q Compute the all-pairs shortest paths to get Pi, j, l q Apply the traffic matrix Mi, j to get link loads ul q Evaluate the objective function from the ul/cl Generate a new setting of the link weights repeat
Making the Search Efficient n Avoid repeating the same weight setting q q q n Avoid computing shortest paths from scratch q q n Explore settings that changes just one weight Apply fast incremental shortest-path algorithms Limit number of unique values of link weights q n Keep track of past values of the weight setting … or keep a small signature of past values Do not evaluate setting if signatures match Do not explore 216 possible values for each weight Stop early, before exploring all settings
Incorporating Operational Realities n Minimize number of changes to the network q n n Tolerate failure of network equipment q Weights settings usually remain good after failure q … or can be fixed by changing one or two weights Limit dependence on measurement accuracy q n Changing just 1 or 2 link weights is often enough Good weights remain good, despite random noise Limit frequency of changes to the weights q Joint optimization for day & night traffic matrices
Application to AT&T’s Backbone Network n Performance of the optimized weights q Search finds a good solution within a few minutes q Much better than link capacity or physical distance q Competitive with multi-commodity flow solution n n Optimal routing possible with more flexible routing protocols How AT&T changes the link weights q Maintenance every night from midnight to 6 am q Predict effects of removing link(s) from network q Reoptimize the link weights to avoid congestion q Configure new weights before disabling equipment
More readings n Overview papers q q n Traffic measurement q q n "Measurement and analysis of IP network usage and behavior” (http: //www. research. att. com/~jrex/papers/ieeecomm 00. ps) “Deriving traffic demands for operational IP networks” (http: //www. research. att. com/~jrex/papers/ton 01. ps) Topology and configuration q q n “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/papers/ieeecomm 02. ps) “IP network configuration for intradomain traffic engineering” (http: //www. research. att. com/~jrex/papers/ieeenet 01. ps) “An OSPF topology server: Design and evaluation” (http: //www. cse. ucsc. edu/~aman/jsac 01 -paper. pdf) Intradomain route optimization q q “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/PAPERS/change_ospf. ps)
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