Optimization Flow Control—I: Basic Algorithm and Convergence Present : Li-der.
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Transcript of Optimization Flow Control—I: Basic Algorithm and Convergence Present : Li-der.
![Page 1: Optimization Flow Control—I: Basic Algorithm and Convergence Present : Li-der.](https://reader035.fdocuments.us/reader035/viewer/2022062408/56649efd5503460f94c11839/html5/thumbnails/1.jpg)
Optimization Flow Control—I: Basic Algorithmand Convergence
Present : Li-der
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Outline
• Objective of the paper• Problem Framework• The optimization problem • Synchronous Distributed Algorithm• Experimental Results• Conclusion
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Objective of the paper
• Propose an optimization approach for flow control on a network whose resources are shared by a set of S of sources
• Maximization of aggregate source utility over transmission rates is aimed
• Sources select transmission rates that maximize their benefit (utility – bandwidth cost)
• Distributed algorithms for converging optimal behavior in static environment is presented
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Problem Framework
The problem is formulated for • A network that consists of a set L of unidirectional links
of capacities cl, where l is element of L.
• The network is shared by a set S of sources, where source s is characterized by a utility function Us(xs) that is concave increasing in its transmission rate xs
• The goal is to calculate source rates that maximize the sum of the utilities ∑s ϵ S Us(xs) over xs subject to capacity constraints.
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S
Problem Framework
s1 s2 s3 ss
..........
DESTINATION NODES
SOURCE NODES
L(s1)={l1,l2,l3,l4}
link l4 : S(l4)={s1,s3}
l1
l2
l3 l5
l6
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Problem Framework
c1 c2
Sources sL(s) - links used by source sUs(xs) - utility if source rate = xs
• Network– Links l of capacities cl
x1
x2x3
121 cxx 231 cxx
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The optimization problem :Primal problem
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Concave utility function
TYPICAL CONCAVE UTILITY FUNCTION
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The optimization problem :Lagrangian for primal problem
pl represents Lagrange multipliers utilized in standard convex optimization method By using this approach coupled link capacity constraints are integrated to the objective
function Notice separability in terms of xs so maximizing lagrangian function as aggregate of
different xs related terms means gives the same result as summing up maximum of each individual xs related term. Therefore we have
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The optimization problem :Dual problem
Here pl is the price per unit bandwidth at link l. ps is the total price per unit bandwidth for all links in the path of s The dual problem has been defined as minimization of D(p) (upper bound of Lagrangian
function) for non-negative bandwidth prices. Each source can independently solve maximization problem in (3) for a given p
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Source rate as demand function
The above figure depict xs(p) as a possible solution
Similar to inverse of U’ figure in previous slide the rate is obtained as a decreasing function of U’-1(rate)
This means that xs(p) acts as a demand function seen in Microeconomics.
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The dual problem is solved via gradient projection method where link prices are adjusted in the opposite direction of gradient of D(p)
Synchronous Distributed Algorithmbased on gradient projection applied to dual problem
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Synchronous Distributed Algorithm
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S
Problem Framework
s1 s2 s3 ss
..........
DESTINATION NODES
SOURCE NODES
L(s1)={l1,l2,l3,l4}
l4
l1
l2
l3 l5
l6
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Experiment Result
• Overview of Implementation– two IBM-compatible PC’s (Pentium 233 MHz)
running the FreeBSD-2.2.5 operating system. – Each PC was equipped with 64 MB of RAM and
100-MB/s PCI ether-net cards.
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Experiment Result
• Each source transmitted data for a total of 120 s, with their starting times staggered by intervals of 40 s.
• source 1 started transmitting at time 0, source 2 at time 40 s, and source 3 at time 80 s.
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Conclusion
• This paper have described an optimization approach to reactive flow control, and derived a simple distributed algorithm.
• The algorithm is provably convergent to the global optimal when network conditions are static and seems to track the optimum when network conditions vary slowly.