Network control Frank Kelly University of Cambridge Conference on Information Science and Systems Princeton, 22 March 2006

Outline Example: end-to-end congestion control in the Internet –square-root formula Control of elastic network flows –utilities, fairness and optimization formulation Stability, under –delays –noise –fluctuating demand

End-to-end congestion control Senders learn (through feedback from receivers) of congestion at queue, and slow down or speed up accordingly. With current TCP, throughput of a flow is proportional to senders receivers T = round-trip time, p = packet drop probability. (Jacobson 1988, Mathis, Semke, Mahdavi, Ott 1997, Padhye, Firoiu, Towsley, Kurose 1998, Floyd and Fall 1999)

Control of elastic network flows user resource flow How should available resources be shared between competing streams of elastic traffic? Conceptual problem: fairness Pragmatic problem: stability of rate control algorithm

utility, U(x) rate, x utility, U(x) rate, x Elastic flows elastic traffic - prefers to share (Shenker) inelastic traffic - prefers to randomize

Network structure (J, R, A) - set of resources - set of routes - if resource j is on route r - otherwise resource route

Notation - set of resources - set of users, or routes - resource j is on route r - flow rate on route r - utility to user r - capacity of resource j - capacity constraints resource route

The system problem Maximize aggregate utility, subject to capacity constraints SYSTEM(U,A,C):

The user problem User r chooses an amount to pay per unit time, w r, and receives in return a flow x r =w r / r USER r (U r ; r ):

As if the network maximizes a logarithmic utility function, but with constants {w r } chosen by the users NETWORK(A,C;w): The network problem

Problem decomposition Theorem: the system problem may be solved by solving simultaneously the network problem and the user problems Johari, Tsitsiklis 2005, Yang, Hajek 2006

Max-min fairness Rates {x r } are max-min fair if they are feasible: and if, for any other feasible rates {y r }, Rawls 1971, Bertsekas, Gallager 1987

Proportional fairness Rates {x r } are proportionally fair if they are feasible: and if, for any other feasible rates {y r }, the aggregate of proportional changes is negative:

Weighted proportional fairness A feasible set of rates {x r } are such that are weighted proportionally fair if, for any other feasible rates {y r },

Fairness and the network problem Theorem: a set of rates {x r } solves the network problem, NETWORK(A,C;w), if and only if the rates are weighted proportionally fair

Bargaining problem (Nash, 1950) Solution to NETWORK(A,C;w) with w = 1 is unique point satisfying Pareto efficiency Symmetry Independence of Irrelevant Alternatives (General w corresponds to a model with unequal bargaining power)

Market clearing equilibrium (Gale, 1960) Find prices p and an allocation x such that (feasibility) (complementary slackness) (endowments spent) Solution solves NETWORK(A,C;w)

Fairness criteria We’ve seen three fairness criteria: proportional fairness max-min fairness TCP-fairness (as described by the square-root formula) Can we unify these fairness criteria within a single framework?

subject to maximize Optimization formulation Suppose the allocation x is chosen to (weighted -fair allocations, Mo and Walrand 2000)

- shadow price (Lagrange multiplier) for the resource j capacity constraint Observe alignment with square-root formula when Solution

- maximum flow - proportionally fair - TCP fair - max-min fair Examples of -fair allocations subject to maximize

Example 0 11 maximum flow: 1/3 2/3 1/2 max-min fairness: proportional fairness:

Rate control algorithms Can rate control algorithms be interpreted within the optimization framework? Several types of algorithm possible, based on, e.g., –congestion indication using increase and decrease rules at sources –explicit rates determined by shadow prices estimated by resources Low, Srikant 2004, Srikant 2004

Network structure - set of resources - set of routes - resource j is on route r - flow rate on route r at time t - rate of congestion indication, at resource j at time t resource route

A primal algorithm x r (t) - rate changes by linear increase, multiplicative decrease p j (.) - proportion of packets marked as a function of flow through resource

Global stability Theorem: the above dynamical system has a stable point to which all trajectories converge. The stable point is proportionally fair with respect to the weights {w r }, and solves the network problem, when K, Maulloo, Tan 1998

What’s missing? The global stability results ignore: time delays stochastic effects

General TCP-like algorithm Source maintains window of sent, but not yet acknowledged, packets - size cwnd On route r, cwnd incremented by a r cwnd n on positive acknowledgement cwnd decremented by b r cwnd m for each congestion indication (m>n) a r = 1, b r = 1/2, m=1, n= -1 corresponds to Jacobson’s TCP

Differential equations with delays r j

Equilibrium point a r = 1, b r = 1/2, m=1, n= -1 corresponds to Jacobson’s TCP, and recovers square root formula But what is the impact of delays on stability? Can we choose m, n,… arbitrarily?

Delay stability Equilibrium is locally stable if there exists a global constant  such that condition on sensitivity for each resource j condition on aggressiveness for each route r Johari, Tan 1999, Massoulié 2000, Vinnicombe 2000, Paganini, Doyle, Low 2001

Consequences? n = -1 (Jacobson’s TCP) instability if congestion windows are too small, sluggishness if congestion windows are too big n = 0 (Scalable TCP, Vinnicombe, T. Kelly) condition independent of size of congestion window, and choice of b r can remove round-trip time bias Delay stability condition:

Stochastic stability The signalling of congestion is noisy: for example, the receiver might see receiver

Variance If m=1, then: variance does not depend on T r coefficient of variation (= standard deviation/mean) does not depend on x r - scale invariance Ott 1999, K 2003, Baccelli, McDonald, Reynier 2002

a r = a, n = 0, m = -1 is attractive, giving both delay stability and stochastic stability Suggestion on single path congestion control?

Routing? Can we extend the algorithm to allow load balancing across routes? Danger: route flap. resource route source destination - set of source-destination pairs - route r serves s-d pair s

Combined rate control and routing algorithm On route r x r (t) increased by a / T r on positive acknowledgement x r (t) decreased by b r y s(r) (t) / T r for each congestion indication, where is rate of returning acknowledgements for s-d pair s at time t s = {r} corresponds to Scalable TCP

Delay stability Equilibrium is locally stable if there exists a global constant  such that condition on sensitivity for each resource j condition on aggressiveness of sources Han, Shakkottai, Hollot, Srikant, Towsley 2003, K, Voice 2005

Delay stability Equilibrium is locally stable if there exists a global constant  such that condition on sensitivity for each resource j condition on aggressiveness of sources Han, Shakkottai, Hollot, Srikant, Towsley 2003, K, Voice 2005 impact of routing

Suggestion on routing? Stable, scalable load balancing across paths, based on end-to-end measurements, can be achieved on the same time-scale as rate control For load balancing, the key constraint on the responsiveness of each route is the round-trip time of that route. While it is natural for structural information to be provided by the network layer, load balancing is more naturally part of the transport layer. Han, Shakkottai, Hollot, Srikant, Towsley 2003, K, Voice 2005 Key, Massoulié, Towsley 2006 Chiang, Low, Calderbank, Doyle 2003

Example: fair dual algorithm A sufficient condition for delay stability: average round-trip time of packets through resource j Low, Lapsley 1999 Katabi, Handley, Rohrs 2002 Liu, Basar, Srikant 2003, K 2003

Stochastic stability For a single resource neither coefficient of variation depends on scale invariance with respect to promising for ad-hoc networks, where there are no natural scalings for prices (of battery power, bandwidth, etc )

Flow level model Define a Markov process with transition rates at rate - Poisson arrivals, exponentially distributed file sizes - model originally due to Roberts and Massoulié 1998 where is an -fair allocation

Suppose is positive recurrent Stability Then Markov process De Veciana, Lee, Konstantopoulos 1999, Bonald, Massoulié 2001, Ye 2003, Kang, K, Lee, Williams 2004, Lin, Shroff, Srikant 2006, Massoulié 2006

Suppose vertical streams have priority: then condition for stability is and not C=1 What goes wrong without fairness? (Bonald and Massoulié 2001)

Conclusions End-to-end congestion control can be viewed as a distributed algorithm that: solves an optimization problem computes a fair allocation finds a market clearing equilibrium Choices of distributed algorithm are limited by the requirement of stability under delays noise fluctuating load

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