Reconfiguration of Traffic Reconfiguration of Traffic Grooming Optical Networks Grooming Optical Networks Ruhiyyih Mahalati and Rudra Dutta Computer Science, North Carolina State University This research was supported in part by NSF grant # ANI-0322107
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Reconfiguration of Traffic Grooming Optical Networks
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Reconfiguration of TrafficReconfiguration of TrafficGrooming Optical NetworksGrooming Optical Networks
Ruhiyyih Mahalati and Rudra DuttaComputer Science, North Carolina State University
This research was supported in part by NSF grant # ANI-0322107
Rudra Dutta, NCSU, BroadNets '04 presentation 2
l Context
l Problem Definition
l Integrated Approach Formulation
l Reconfiguration Heuristic– Over-Provisioning Methods
l Traffic Grooming:Combining lower speedtraffic flows ontowavelengths to minimizenetwork cost
l Traffic Grooming problemconceptually comprises of
1. Virtual Topology SP
2. Routing & WavelengthAssignment SP
3. Traffic Routing SP
Traffic GroomingTraffic Grooming
PhysicalTopology
Gp
VirtualTopology
Gv
TrafficT
GroomingG
RoutingR
l assignL
Rudra Dutta, NCSU, BroadNets '04 presentation 6
l Reconfiguration: possibility of adaptively creatingvirtual topologies, based on network need– Independence between the virtual and the physical topology
l Goal: Improve performance metric
l Tradeoff between the performance metric value andthe number of changes
l Are existing methods sufficient to reconfigure withsubwavelength traffic?– If not, what are the needs?
l Observation: full wavelengthreconfiguration cannot modifygrooming of traffic ontovirtual topology– How to translate change of
subwavelength traffic to changeof lightpaths?
l Observation: reconfigurationcost is defined fromconsiderations differentfrom grooming
PhysicalTopology
Gp
VirtualTopology
Gv
TrafficT
GroomingG
RoutingR
l assignL
Rudra Dutta, NCSU, BroadNets '04 presentation 8
l Integrated Approach - reconfiguration of a topology aswell as traffic assignment in a groomed network, withthe objective to balance grooming gain andreconfiguration cost
l Assumptions:– Each node is equipped with an OXC and DXC
– Physical links and lightpaths are directed
– No wavelength converters
Æ No more than a single lightpath between two nodes
Æ Disallowing bifurcated routing of traffic
Problem DefinitionProblem Definition
Rudra Dutta, NCSU, BroadNets '04 presentation 9
l Grooming cost is normally represented as totalnumber of LTEs or total electronic switching
l Reconfiguration cost is normally represented as thenumber of network equipments that requirereconfiguration
l Our Integrated Cost Calculation:– Grooming Cost: total amount of electronic switching - total
traffic weighted delay
– Reconfiguration Cost: the number of OXCs and DXCs thatneed reconfiguration - total delay experienced by the traffic atthese nodes
– Both measure delay suffered by traffic
The Need for a Cost FunctionThe Need for a Cost Function
Rudra Dutta, NCSU, BroadNets '04 presentation 10
• Matrix representation of each node’s switching state
Reconfiguration Cost FunctionReconfiguration Cost Function
Rudra Dutta, NCSU, BroadNets '04 presentation 11
l Lightpath establishment - OXC, DXC
l Different optical switching - only OXC
l Lightpath termination and origination at anode - single change to both OXC and DXC.
Matrix Distance as Cost FunctionMatrix Distance as Cost Function
Rudra Dutta, NCSU, BroadNets '04 presentation 12
l Global Reconfiguration Cost Calculation Methods– RC-I = Total no. of OXCs, Total no. of DXCs– RC-II = Total no. of OXC wavelength changes, Total no. of
DXCs– RC-III = Total no. of OXC changes, Total no. of DXCs– RC-IV = Total no. of OXC changes, Total no. of DXC
changes : linear
l Integrated Approach as an ILP– Objective: Maximize (Grooming gain) g - (RC-IV) - d– g : relative weightage parameter: related to average delay
between reconfigurations
– d : to prevent chattering
ILP FormulationILP Formulation
Rudra Dutta, NCSU, BroadNets '04 presentation 13
l Integrated Approach Solution as an ILP - optimal butcomputationally expensive– Note: Optimal in the next state
l The heuristic approach must– Avoid resorting to the full ILP whenever possible
– Ward off failure of the network - remain feasible
– Avoid adopting very suboptimal grooming solutions
l Problem is intractable - tractable heuristic unlikely toattain globally optimal solutions
l Model: traffic components are relatively static, but may changesomewhat over time (LCAS)– For revenue, increases are desirable to serve, decreases are
desirable to leverage
– For resilience, need to react fast to opportunities
l Over-provisioning at traffic demand level: use extra capacity,otherwise unutilized
l OXCs and DXCs configured to carry over-provisioned traffic
l Family of traffic matrices supported– All new traffic matrices that are subset of the initial traffic matrix
l Lightpath slack limits over-provisioning– Equal allocation
– Prorated allocation
– Inverse allocation
Over-provisioningOver-provisioning
Rudra Dutta, NCSU, BroadNets '04 presentation 15
l Different Methods of Over-Provisioning– Equal over-provisioning method
l Pick minimum over-provisioned over all traffic elements
– Selective over-provisioning methodl Pick minimum over-provisioned for each individual traffic
element
– Iterative over-provisioning methodl Iteratively over-provision some traffic elements with any extra
l Equal– Every t(sd) gets the same (therefore min) - simplistic
l Selective– Every t(sd) gets the max they can get
l Iterative– One t(sd) is assigned its max, then slacks recalculated– Different flavors depending on the choicel Iterative-Minl Iterative-Maxl Iterative-Ratiol Iterative-Max-lightpathl Iterative-Min-Max