Hot Sticky Random Multipath or Energy Pooling
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Transcript of Hot Sticky Random Multipath or Energy Pooling
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Hot Sticky Random Multipath or Energy Pooling
Jon Crowcroft, http://www.cl.cam.ac.uk/~jac22
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Multipath: Network & End2End
High level argument is simply a Trilogy rerun: Multipath is resource pooling in capacity terms Multipath IP spreads traffic
Currently only ECMP. pre-computed/designated equivalent paths Better would be random j out of k shortes paths Basis: stick random (DAR), per Gibbens/Kelly/Key et al
Multipath TCP splits load Goal is same rate as std TCP, but use eqn to determine proportion
on subpath Subpath is indeicated easily for multihomed host(s) More tricky for single home, multiple route...tbd Rate set from loss/rtt or ECN, ideally
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S/Congestion/Carbon Let us set a price for power in the routers and transmission lines Then we give feedback (EECN?) based on this price
Time of day variation in traffic means that at night, typically we see night:daytime traffic matrix about 6 fold reduction in load
So re-confugure the notional cost of the shortest paths only to be 6-10 times the price
And reduce the actual rate on all links by the same ratio
Need energy proportional interfaces (of course) i.e. need line cards/transmission that can Slow down and use less energy That isn't rocket science (phones and laptops do this routinely with typically 3 settings)
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Sprint 24 hour coast-coast trace
DigiComm II
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Cue:Optimization-based congestion control (ack: uMass for slides)
Resource allocation as optimization problem: how to allocate resources (e.g., bandwidth) to
optimize some objective function maybe not possible that optimality exactly
obtained but… optimization framework as means to explicitly
steer network towards desirable operating point
practical congestion control as distributed asynchronous implementations of optimization algorithm
systematic approach towards protocol design
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c1 c2
Model Network: Links l each of capacity cl
Sources s: (L(s), Us(xs))L(s) - links used by source sUs(xs) - utility if source rate = xs
x1
x2 x3
121 cxx 231 cxx
Us(xs)
xs
example utilityfunction for elastic application
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Optimization Problem
maximize system utility (note: all sources “equal) constraint: bandwidth used less than capacity centralized solution to optimization impractical
must know all utility functions impractical for large number of sources we’ll see: congestion control as distributed
asynchronous algorithms to solve this problem
Llcx
xU
lSsls
sss
xs
, subject to
)( max
)(
0 “system” problem
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The user view
user can choose amount to pay per unit time, ws Would like allocated bandwidth, xs in proportion to ws
0 wsubject to
w Umax
s
ss
s
s
wp
ps could be viewed as charge per unit flow for user s
s
ss p
wx
user’s utility cost
user problem
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The network view
suppose network knows vector {ws}, chosen by users
network wants to maximize logarithmic utility function
S(l)s
ls
sss
x
cx
x w s
subject to
log max0 network problem
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Solution existence
There exist prices, ps, source rates, xs, and amount-to-pay-per-unit-time, ws =
psxs such that {Ws} solves user
problem {Xs} solves the
network problem {Xs} is the unique
solution to the system problem
S(l)s
ls
sss
x
cx
x w s
subject to
log max0
0 wsubject to
w Umax
s
ss
s
s
wp
Llcx
xU
lSsls
sss
xs
, subject to
)( max
)(
0
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Proportional Fairness
Vector of rates, {Xs}, proportionally fair if feasible and for any other feasible vector {Xs
*}:
0*
Ss s
ss
x
xx
result: if wr=1, then {Xs} solves the network problem IFF it is proportionally fair
Related results exist for the case that wr not equal 1.
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Solving the network problem
Results so far: existence - solution exists with given properties
How to compute solution? ideally: distributed solution easily embodied
in protocol insight into existing protocol
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Solving the network problem
)()()()(
tptxwktxdt
d
sLllsss
congestion “signal”: function of aggregate rate at link l, fed back to s.
change inbandwidth
allocation at s
linearincrease
multiplicative decrease
)()()(
txgtpsLl
sllwhere
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Solving the network problem
)()()()(
tptxwktxdt
d
sLllsss
Results: * converges to solution of “relaxation” of
network problem
xs(t)pl(t) converges to ws
Interpretation: TCP-like algorithm to iteratively solve optimal rate allocation!
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Optimization-based congestion control: summary
bandwidth allocation as optimization problem: practical congestion control (TCP) as distributed
asynchronous implementations of optimization algorithm
optimization framework as means to explicitly steer network towards desirable operating point
systematic approach towards protocol design
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Motivation
Congestion Control:maximize user utility
Traffic Engineering:minimize network congestion
Given routing Rli how to adapt end rate xi?
Given traffic xi how to perform routing Rli?
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Congestion Control Model
max. ∑ i Ui(xi)s.t. ∑i Rlixi ≤ cl
var. x
aggregate utility
Source rate xi
UtilityUi(xi)
capacity constraints
Users are indexed by i
Congestion control provides fair rate allocation amongst users
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Traffic Engineering Model
min. ∑l f(ul)s.t. ul =∑i Rlixi/cl
var. R
Link Utilization ul
Costf(ul)
aggregate cost
Links are indexed by l
Traffic engineering avoids bottlenecks in the network
ul = 1
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Model of Internet Reality
xi Rli
Congestion Control:
max ∑i Ui(xi), s.t. ∑i Rlixi ≤ cl
Traffic Engineering: min ∑l f(ul),
s.t. ul =∑i Rlixi/cl
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System Properties
Convergence Does it achieve some objective? Benchmark:
Utility gap between the joint system and benchmark
max. ∑i Ui(xi)s.t. Rx ≤ cVar. x, R
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Numerical Experiments
System converges Quantify the gap to optimal aggregate utility Capacity distribution: truncated Gaussian with average 100 500 points per standard deviation
Abilene Internet2
Access-Core
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Results for Abilene: f = eu_l
Gap exists
Standard deviation
Aggre
gate
utility
gap
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Simulation of the joint system suggests that it is stable, but suboptimal
Gap reduced if we modify f
Backward Compatible Design
Link load ul
Cost f
ul =1
f(ul)f(ul)
0
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Abilene Continued: f = n(ul)n
Gap shrinks with larger nn
Aggre
gate
utility
gap
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Theoretical Results
Modify congestion control to approximate the capacity constraint with a penalty function
Theorem: modified joint system model converges if Ui’’(xi) ≤ -Ui’(xi) /xi
Master Problem:min. g(x,R) = - ∑iUi(xi) + γ∑lf(ul)
Congestion Control:argminx g(x,R)
Traffic Engineering:argminR g(x,R)
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Now re-do for Energy…
• Need energy propotional interfaces from vendor
• Need k-shortest path (& BGP equiv)• Traffic Manager report aggregate
demand• Configure rates/energy based on
aggregate demand• Future:-
– put topology&energy* figures (e.g. from JANET) in Xen/VM based emulator
– See how much we can gain…