reconfiguration of traffic grooming optical networks

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

– Hard & Soft Decision Criterion

– Flowchart

l Numerical Results

l Conclusion

OutlineOutline


Virtual Topology, Traffic GroomingVirtual Topology, Traffic Grooming


• Certainwavelengthspass throughoptically

• Othersterminated atDigital CrossConnect (DXC)for OEO

Optical Cross ConnectOptical Cross Connect


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


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


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


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


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


• Matrix representation of each node’s switching state

Reconfiguration Cost FunctionReconfiguration Cost Function


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


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


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


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


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


C = 15

3,7,2Over-provision

1,1,1

Over-provisioning ExampleOver-provisioning Example


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


Over-provisioning comparisonOver-provisioning comparison


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


Heuristic FlowchartHeuristic Flowchart


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


Reconfiguration CostReconfiguration Cost


Grooming CostGrooming Cost


Integrated ObjectiveIntegrated Objective


Cumulation Cumulation of Integrated Objectiveof Integrated Objective


More Numerical ResultsMore Numerical Results


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

reconfiguration of traffic grooming optical networks

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