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Technical: what might I mean by multipath? 1.Via an overlay network 2.End-points are multi- homed, and expose multiple IP addresses; routing works as it does now 3.End-systems set a path selector (e.g. 2 bits in the IP header), and multipath- aware routers use this to choose one of several available routes

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Political: what can multipath achieve? It’s happening anyway, and we might as well make sure it happens right It will increase competition between network providers It allows end-users to signal what sort of service they want It increases the capacity of the network End-user performance is protected against link failures Multihoming is a simple and elegant way to support mobility and address aggregation Routing tables can be smaller Nice maths

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Mathematical models of multipath resource allocation Damon Wischik, UCL Partners from Trilogy Damon Wischik, UCL Partners from Trilogy

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4 What is the capacity of a network in which traffic flows can be split over multiple paths? → Generalized cut constraints What sort of rate-control algorithms might make full use of this capacity? → Resource pooling; fairness and stability What routing support does there need to be, in order to make use of the capacity? → Power of two choices Is there a conflict between routing at the end- systems, and traffic engineering in the network? → Wardrop equilibrium

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I. Generalized cut constraints “Resource pooling in queueing networks with dynamic routing”, Laws, 1992 “Loss Networks”, Kelly, 1991

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The multipath rate allocation problem 6 i 2 {1,2,3} are the flows x i is the arrival rate of flow i j 2 {1,2,3,4} are the resources C j is the capacity at resource j p 2 {1,…,5} are the paths Y p is the rate allocated to path p For what arrival rates x is it possible to allocate rates y without overloading the resources?

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Generalized cut constraints 7 x 1 + x 2 · C 1 x 1 + x 3 · C 3 x 3 · C 4 x 1 + x 2 + x 3 · C 1 + C 4 x 1 +2 x 3 · C 3 + C 4 x 1 + x 2 · C 1 + C 2 x 1 +2 x 3 · C 3 + C 4

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Generalized cut constraints 8 x 1 + x 2 · C 1 x 1 + x 3 · C 3 x 3 · C 4 x 1 + x 2 + x 3 · C 1 + C 4 x 1 +2 x 3 · C 3 + C 4 x 1 + x 2 · C 1 + C 2 x 1 +2 x 3 · C 3 + C 4 a cut constraint a generalized cut constraint

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9 Generalized cut constraints Theorem. The set X of admissible arrival rates can always be written X = { x : ¸ ( k ) ¢ x · ¹ ( k ) ¢ C for all k } for a suitable set of variables ¸ ( k ) and ¹ ( k ) These constraints are called generalized cut constraints, corresponding to virtual resources Consider the linear program: “what is the least amount of extra capacity I need to add to be able to serve x ?” The generalized cut constraints are the extreme points of the dual feasible set. There are algorithms for computing the generalized cut constraints

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Capacity region x1x1 x2x2 x3x3 These plots show the capacity region, i.e. the set of admissible arrival rates. Multipath will increase the capacity region. By how much? For which flows does multipath bring the greatest benefit?

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Questions Given Internet topology, what multipaths do we need to ensure that the generalized cut constraints are as broad as possible? –e.g. if each flow could have only two paths, what should they be? –if you had complete flexibility, how big is the admissible region? Q

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II. Resource pooling “State space collapse and diffusion approximation for a network operating under a proportional fair sharing policy”, Kang, Kelly, Lee, Williams “Heavy traffic analysis of optimal scheduling algorithms for switched networks”, Shah, Wischik

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Example of resource pooling The total capacity of the four links is 36, and it can be evenly shared between the flows, even though none of the flows can balance its load across all four links. This is called resource pooling. When a link fails, the flows should automatically re-balance themselves so that service is maintained. Link failures will mean there is less path diversity, and this may break resource pooling.

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“No constraints but the network itself” x1x1 x2x2 x3x3 What are the characteristics of a good rate allocation algorithm? A good algorithm will have decent performance whenever the arrival rates are admissible. It will not add extra constraints. It will only ‘see’ the generalized cut constraints. x 1 + x 2 · C 1 + C 2 x 1 +2 x 3 · C 3 + C 4

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Revision of basic queueing theory Consider a M/G/1 processor sharing queue Jobs arise as a Poisson process, and job size distribution is arbitrary Let the utilization be ½ Then the number of active jobs has a geometric distribution with mean ½ /(1- ½ )

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A critically loaded network x1x1 x2x2 x3x3 Let x 1, x 2, x 3 be arrival rates for the arrival of TCP sessions, assumed to be Poisson arrivals. Let C 1,…, C 4 be link speeds divided by mean flow size. Suppose that one generalized cut constraint is tight, while the other is not. x 1 + x 2 ¿ C 1 + C 2 x 1 +2 x 3 ¼ C 3 + C 4

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Virtual queues at virtual resources x1x1 x2x2 x3x3 Define a virtual queue V as follows: For arrivals of type 1, put in one token Every arrival of type 3, put in two tokens Serve this queue at rate C 3 + C 4 Define the workload W for the critical constraint — the number of jobs of type 1, + 2 x number of jobs of type 3 Then W ¸ V. A perfect algorithm would achieve W = V, by ensuring that neither resource 3 nor resource 4 is underutilized whenever V is non- empty. If either of these becomes underutilized then W > V. A modified TCP achieves W = V. In this case V has a geometric distribution, and there are ¼ [ x 1 /( x 1 +2 x 3 )] W jobs on route 1 ¼ 0 jobs on route 2 ¼ [ x 1 /( x 1 +2 x 3 )] W jobs on route 3 x 1 + x 2 ¿ C 1 + C 2 x 1 +2 x 3 ¼ C 3 + C 4

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Resource pooling x1x1 x2x2 x3x3 In effect, the system only ‘sees’ the virtual resources, and each of them behaves like a standard processor- sharing link. This is as good as possible. To achieve this, whenever a virtual resource is non-empty none of the real resources that comprise it should go idle. This is called resource pooling. (In this example, resource pooling is a consequence of the fact that one virtual resource is heavily loaded. This is heavy traffic theory.)

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The multipath packet scheduling problem 19 Packets are sent on a route, chosen at the ingress. Servers have a choice of which routes to serve. Should packets be held back until there is a clear path, or should they commit to a route straight away? How should servers decide which packets to serve? Q

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III. Fairness and stability of congestion control “Overlay TCP for multi-path routing and congestion control”, Han, Shakkottai, Hollot, Srikant, Towsley, 2003. “Stability of end-to-end algorithms for joint routing and rate control”, Kelly, Voice, 2005

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Unfairness of per-path congestion control When some flows have several paths available, and other flows do not, it is fairer to allocate capacity to users rather than to paths Therefore we should not use standalone TCP congestion control on each path individually In maths: we want the congestion control to solve a user-centric utility maximization problem

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Stability and responsiveness When a link fails, or when flows come and go, rates should rebalance gradually Otherwise the network will be susceptible to route flap / oscillations In maths: the dynamical system describing the algorithm should be stable 12 10 8 6 4 2

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IV. Power of two choices “Queueing system with selection of the shortest of two queues: an asymptotic approach”, Vvedenskaya, Dobrushin, Karpelevich, 1996 “The power of two choices in randomized load balancing”, Mitzenmacher, 1996

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A simple example of load-balancing Packets arrive as a random process. When they arrive they look at m queues, selected at random, and join the shorter queue What is the resulting distribution of queue sizes?

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A simple example of load-balancing Packets arrive as a random process. When they arrive they look at m queues, selected at random, and join the shorter queue What is the resulting distribution of queue sizes?

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A simple example of load-balancing The larger m is, the more uniform the queue sizes should be. Surprisingly, m =2 is enough to get most of the benefit. If m =1 then Prob( Q > q )= ½ q If m ¸ 2 then Prob( Q > q )= ½ ( m q -1)/( m -1) (Here ½ is the average load, arrivals are Poisson, service is exponential.) m =1 m =2 m =5 A sample of queue sizes, for different values of m

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Two paths good… Recall resource pooling: This is what happens when all the real resources that make up a virtual resource are kept fully occupied as long as possible. It means that the system is operating at peak efficiency. Perhaps, if each flow could balance itself across two real resources in every virtual resource, then we will achieve resource pooling. x1x1 x2x2 x3x3 x 1 + x 2 · C 1 + C 2 x 1 +2 x 3 · C 3 + C 4

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…as long as they are really random choices If the choices are structured, e.g. each packet selects from a set of size m, and the sets are chosen at random from some arbitrary balanced distribution, then we need m = log (# queues ) to get load balancing “Balls and bins with structure: balanced allocations on hypergraphs”, Godfrey, 2008 !

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Two choices, in a loss network Several parallel links. Each call tries up to m links before giving up A fully-connected graph. Model 1. Each call tries the direct link, and if that is busy it fails Model 2. Each call tries the direct link, and if that is busy it tries a 2-hop link load drop prob. load drop prob. 100%

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Two choices, in a loss network The benefit of two choices is only apparent in critically loaded systems (in a model with equal load and no failures) Maybe multipath is only really useful for coping with traffic surges and link failures It is important to make sure that the alternative paths do not consume too many resources

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Questions What traffic mix / routing will give us load balancing? –Maybe, if 50% of flows using a given virtual resource have two choices of which real resource to use, this will be enough –Is BitTorrent doing all this already? –How can we formulate this question properly? What is the relationship between heavy traffic resource pooling and the example of ‘power of two choices’? –How does ‘power of two choices’ work in networks, i.e. not just an array of homogeneous resources? –One theory looks at the traffic matrix that leads to resource pooling, the other looks at the routing choice that leads to resource pooling. How can we integrate these two? Q

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V. Wardrop equilibrium “Partially optimal routing”, Acemoglu, Johari, Ozdaglar, 2007 “How bad is selfish routing?”, Roughgarden, Tardos, 2002

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Wardrop equilibrium Suppose the total traffic x is made up of many users who each can choose how to split their traffic, and they make their choice selfishly so as to minimize their congestion cost Let the congestion cost at resource 1 be C 1 ( y 1 + y 3 ) etc. Let the congestion cost of a path be the sum of the congestion costs along the route Then, if some path has greater congestion cost than others, no one will send any traffic over that path The resulting allocation is called a Wardrop equilibrium

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Braess’s paradox Braess discovered a situation where by removing resource 3 we end up with lower congestion costs for all users This is called Braess’s paradox It is not an uncommon problem! 10 z 50+ z 6z6z each path costs 92

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Braess’s paradox Braess discovered a situation where by removing resource 3 we end up with lower congestion costs for all users This is called Braess’s paradox It is not an uncommon problem! 10 z 50+ z 6z6z each path costs 83

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Generalized Braess’s paradox Suppose an AS owns the middle of the network, and does load-balancing This will decrease the net congestion cost of traversing the middle of the network This may INCREASE the average congestion costs to users, by an extension of Braess’s paradox The relative cost C (selfish users & load-balancing) / C (selfish users) can be arbitrarily large Plus other perverse outcomes...

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Questions Why do these problems not show up in the earlier model of capacity constraints? Is it because the model of congestion costs is fishy? The difference seems to be to do with congestion signals: the signals to which users respond are in this case not in alignment with the costs. It may be that the network has much more routing flexibility than do individual users. Does this lead to a different answer? How can we fit it into the model? Q

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More questions Are there appropriate ways of signalling congestion, such that user and network incentives align? For example, if BitTorrent responded appropriately, could it do a much better job of traffic engineering? Multipath should lead to more competition, which should force prices closer to costs. What are appropriate models of this? Is P2P already enough to force competition? Q

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