PROFITABLE CONNECTION
ASSIGNMENT IN ALL OPTICAL
WDM NETWORKS
VISHAL ANAND
LANDER
(Lab. for Advanced Network Design, Evaluation and Research)
In collaboration with:
Tushar Katarki and Chunming Qiao
CSE Dept., SUNY at Buffalo
Outline
 Introduction
 Related work
 Maximum Profitability Problem
 Concluding remarks
 Questions and discussion
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Introduction
 Optical WDM networks - future backbone
for wide area networks.
 Physical Topology - Optical wavelength
routers connected by fiber links.
 Lightpath or connection - Path between
two end nodes and a wavelength on that
path.
 No wavelength conversion - Any lightpath
uses the same wavelength on all the links
its path spans.
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The RWA(Routing & Wavelength
Assignment) Problem
 Given :
– a network topology
– a set of traffic demands (or connection
requests).
 Determine the routes and wavelengths to use
so as to satisfy the demands.
 The RWA problem is usually solved to
optimize some specified objective(s).
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Previous and related work
 Example objectives
– Minimize network-wide packet delay (e.g.
number of hops).
– Maximize throughput (e.g. number of
lightpaths).
– Maximize allowable capacity upgrade or
scalability (for future traffic demands).
 Minimizing cost (network resources used)
can also be an important objective.
 For a bandwidth broker (or carrier)
maximizing profits is most important.
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The Maximum Profitability Problem
 Given:
– a set of connection requests, N.
– a network topology.
– earnings (revenue) Ei associated with each
connection request, i.
– cost of using any wavelength on a link l, Cl.
 Solve the RWA problem to maximize the
profit, P = Total Earnings - Total Costs.
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 Maximizing profit problem is a more
general formulation.
– If Ei=E, for each connection/lightpath i (i.e., all
connections have equal earnings) OR if n=N
(i.e., all the connection requests have to be
satisfied) then the problem is same as the
minimizing cost problem.
– If Ei=E and if all connections/lightpaths have
equal costs. Then the problem is the same as
maximizing throughput problem.
 Hence a more direct study of the
maximizing profit problem is necessary.
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Network Model
 Network topology considered: 16 node
NSFNET.
 Cost of using each wavelength on a link , is
the same, but varies from link to link.
 No wavelength conversion capabilities at
any of the nodes.
 Number of wavelengths on each link in the
network is the same.
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Heuristic based approach
 The RWA problem is known to be NP Hard
and hence computationally intractable.
 Maximizing profit heuristic: MaxPro
– Find a cheapest path for each connection request
and compute the profit.
– Sort the requests in the order of decreasing
profit and store in a list.
– Satisfy connection requests in decreasing order
of profit (a greedy approach).
– If a connection request is satisfied.
• delete that connection from the sorted list.
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– If the cheapest path for a connection request is
not available.
• Re-compute a new cheapest path for only that
connection request.
• Compute the new profit for this connection request.
• Insert this connection into the sorted list depending
on the profit.
– Repeat till no other connection request can be
satisfied.
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Results and Comparison
 1) Results obtained from MaxPro compared
with:
– a minimizing cost heuristic
– a random assignment heuristic
 2) Results of Maxpro compared with the
optimal results from integer linear program.
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Comparison of the heuristics
 MaxPro performs the best.
 Better than a minimizing cost heuristic.
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Integer Linear Model
 Definitions:
– W :Number of wavelengths on each link.
– n, L :Number of nodes, links in the network.
– E :Earnings obtained by satisfying a connection request between
nodes  and .
– R : Total number of alternate routes/paths available to reach node
 from node .
r
– C
: Cost of reaching node  from node  on route r.
rj
rj
– l
: l
= 1, if link j is used by the route r between nodes  and , 0
otherwise.
– d : Number of connection requests between nodes  and .
– xrk : xrk = 1, indicates that the connection between node  and  is
routed on route r using wavelength k.
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 Objective function
 R W rk R r W rk 
max   E  x   C  x 
 1  1 
r 1 k 1
r 1
k 1

 Subject to:
n
n
R  W
rk
x
   d
 , 
r 1 k 1
– The total number of lightpaths established between a node pair
should not exceed the number of requests between that node pair.
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And
n
n
R
W
rj
rk
l
x
     W
 1  1 r 1
j  1,..., L
k 1
– The total number of lightpaths established on any link should not
exceed the number of wavelength on that link.
n
n R
rj rk
l
  x  1
j  1,..., L, k  1,..., W
 1  1 r 1
– A wavelength on a link can support at most one lightpath.
xrk  0,1
 ,  , r , k
– The Integrality constraint.
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Comparison of Maxpro with ILP
 MaxPro obtains on the average 90% of the
results got from the ILP(optimal profit).
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Summary and Future work
 Formulated a RWA problem with the
objective of maximizing the profit.
 Proposed a maximizing profit heuristic.
 Compared results of a profit maximizing
heuristic with a minimizing cost heuristic
and ILP.
 Future work
– Study the maximizing profit problem for the
On-line traffic model.
– Extend to cases where protection and
restoration is required for the traffic.
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