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
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l Context
l Problem Definition
l Integrated Approach Formulation
l Reconfiguration Heuristic– Over-Provisioning Methods
– Hard & Soft Decision Criterion
– Flowchart
l Numerical Results
l Conclusion
OutlineOutline
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Virtual Topology, Traffic GroomingVirtual Topology, Traffic Grooming
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• Certainwavelengthspass throughoptically
• Othersterminated atDigital CrossConnect (DXC)for OEO
Optical Cross ConnectOptical Cross Connect
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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
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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 Computationally intractable
l Many practical heuristics exist
ReconfigurationReconfiguration
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Reconfiguring Groomed NetworksReconfiguring Groomed Networks
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
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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
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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
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• Matrix representation of each node’s switching state
Reconfiguration Cost FunctionReconfiguration Cost Function
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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
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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
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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 Heuristic is proactive: over-provisioning
Proposed Heuristic AlgorithmProposed Heuristic Algorithm
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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
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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
capacity, if available
l Several variants possible
l Similar performance for the variants
Over-provisioning ApproachesOver-provisioning Approaches
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C = 15
3,7,2Over-provision
1,1,1
Over-provisioning ExampleOver-provisioning Example
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Over-provisioning StrategiesOver-provisioning Strategies
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
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Over-provisioning comparisonOver-provisioning comparison
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l Traffic change - grooming cost may increase -reconfiguration needed– But very frequent reconfigurations undesirable
l Critical region: sub-wavelength elements carryingtraffic close to over-provisioned traffic (threshold)– reconfiguration triggered
l LPlimit: ratio of lightpaths carrying sub-wavelengthelements in critical region– LPlimit decides hard or soft decision criterion
l Hard Decision: global reconfiguration– Integrated ILP
l Soft Decision: local reconfiguration– only DXC reconfiguration
Heuristic DescriptionHeuristic Description
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l Traffic Evolution - Rising, Falling, Rising & Fallingl Parameters: g = 2, 7, 15, 200, LPlimit = 30%, 70%l “Grooming-only”, Integrated approach, Heuristic
– Reconfiguration Cost– Grooming Cost– Integrated Objective– Cumulation of the Integrated Objective
• Given: a physicaltopology, initial trafficmatrix, a series ofchanging trafficmatrices
• 4 Physical Topologies
Numerical ResultsNumerical Results
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Cumulation Cumulation of Integrated Objectiveof Integrated Objective
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l Proposed a new problem - joint grooming and reconfiguration
l Defined basis for comparison - provided integrated cost function
l Problem formulation as an ILP
l Heuristic – robust to variation in physical topology– Integrated approach - maximum integrated objective
– Cumulation of integrated objective - heuristic follows integratedapproach while “Grooming-only” approach deviates
– “Grooming-only” approach - not suitable for reconfiguration ofgroomed traffic
l Heuristic considerably reduces ILP calculation
l LPlimit reduced - Heuristic performance improves
l Very high g - Integrated approach gives optimal grooming cost,still incurring less reconfiguration cost
l Verified through numerical experiments
Summary and ConclusionSummary and Conclusion