Reconfiguration of Traffic Reconfiguration of Traffic Grooming - PowerPoint PPT Presentation

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

Outline Outline 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 2 Rudra Dutta, NCSU, BroadNets '04 presentation

Virtual Topology, Traffic Grooming Virtual Topology, Traffic Grooming 3 Rudra Dutta, NCSU, BroadNets '04 presentation

Optical Cross Connect Optical Cross Connect • Certain wavelengths pass through optically • Others terminated at Digital Cross Connect (DXC) for OEO 4 Rudra Dutta, NCSU, BroadNets '04 presentation

Traffic Grooming Traffic Grooming l Traffic Grooming: Combining lower speed Traffic T traffic flows onto wavelengths to minimize Grooming network cost G l Traffic Grooming problem conceptually comprises of Virtual Topology 1. Virtual Topology SP G v 2. Routing & Wavelength l assign Routing Assignment SP R L 3. Traffic Routing SP Physical Topology G p 5 Rudra Dutta, NCSU, BroadNets '04 presentation

Reconfiguration Reconfiguration l Reconfiguration: possibility of adaptively creating virtual 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 and the number of changes l Computationally intractable l Many practical heuristics exist 6 Rudra Dutta, NCSU, BroadNets '04 presentation

Reconfiguring Groomed Networks Reconfiguring Groomed Networks l Are existing methods sufficient to reconfigure with subwavelength traffic? Traffic – If not, what are the needs? T l Observation: full wavelength reconfiguration cannot modify Grooming grooming of traffic onto G virtual topology Virtual – How to translate change of Topology subwavelength traffic to change G v of lightpaths? l assign Routing l Observation: reconfiguration R L cost is defined from Physical considerations different Topology from grooming G p 7 Rudra Dutta, NCSU, BroadNets '04 presentation

Problem Definition Problem Definition l Integrated Approach - reconfiguration of a topology as well as traffic assignment in a groomed network, with the objective to balance grooming gain and reconfiguration 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 8 Rudra Dutta, NCSU, BroadNets '04 presentation

The Need for a Cost Function The Need for a Cost Function l Grooming cost is normally represented as total number of LTEs or total electronic switching l Reconfiguration cost is normally represented as the number of network equipments that require reconfiguration l Our Integrated Cost Calculation: – Grooming Cost: total amount of electronic switching - total traffic weighted delay – Reconfiguration Cost: the number of OXCs and DXCs that need reconfiguration - total delay experienced by the traffic at these nodes – Both measure delay suffered by traffic 9 Rudra Dutta, NCSU, BroadNets '04 presentation

Reconfiguration Cost Function Reconfiguration Cost Function • Matrix representation of each node’s switching state 10 Rudra Dutta, NCSU, BroadNets '04 presentation

Matrix Distance as Cost Function Matrix Distance as Cost Function l Lightpath establishment - OXC, DXC l Different optical switching - only OXC l Lightpath termination and origination at a node - single change to both OXC and DXC. 11 Rudra Dutta, NCSU, BroadNets '04 presentation

ILP Formulation ILP Formulation 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 12 Rudra Dutta, NCSU, BroadNets '04 presentation

Proposed Heuristic Algorithm Proposed Heuristic Algorithm l Integrated Approach Solution as an ILP - optimal but computationally 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 to attain globally optimal solutions l Heuristic is proactive: over-provisioning 13 Rudra Dutta, NCSU, BroadNets '04 presentation

Over-provisioning Over-provisioning l Model: traffic components are relatively static, but may change somewhat 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 14 Rudra Dutta, NCSU, BroadNets '04 presentation

Over-provisioning Approaches Over-provisioning Approaches l Different Methods of Over-Provisioning – Equal over-provisioning method l Pick minimum over-provisioned over all traffic elements – Selective over-provisioning method l Pick minimum over-provisioned for each individual traffic element – Iterative over-provisioning method l Iteratively over-provision some traffic elements with any extra capacity, if available l Several variants possible l Similar performance for the variants 15 Rudra Dutta, NCSU, BroadNets '04 presentation

Over-provisioning Example Over-provisioning Example C = 15 3,7,2 Over-provision 1,1,1 16 Rudra Dutta, NCSU, BroadNets '04 presentation

Over-provisioning Strategies Over-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 choice l Iterative-Min l Iterative-Max l Iterative-Ratio l Iterative-Max-lightpath l Iterative-Min-Max 17 Rudra Dutta, NCSU, BroadNets '04 presentation

Over-provisioning comparison Over-provisioning comparison 18 Rudra Dutta, NCSU, BroadNets '04 presentation

Heuristic Description Heuristic Description l Traffic change - grooming cost may increase - reconfiguration needed – But very frequent reconfigurations undesirable l Critical region: sub-wavelength elements carrying traffic close to over-provisioned traffic (threshold) – reconfiguration triggered l LPlimit: ratio of lightpaths carrying sub-wavelength elements 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 19 Rudra Dutta, NCSU, BroadNets '04 presentation

Heuristic Flowchart Heuristic Flowchart 20 Rudra Dutta, NCSU, BroadNets '04 presentation

Numerical Results Numerical Results Given: a physical • topology, initial traffic matrix, a series of changing traffic matrices 4 Physical Topologies • l Traffic Evolution - Rising, Falling, Rising & Falling l 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 21 Rudra Dutta, NCSU, BroadNets '04 presentation

Reconfiguration Cost Reconfiguration Cost 22 Rudra Dutta, NCSU, BroadNets '04 presentation

Grooming Cost Grooming Cost 23 Rudra Dutta, NCSU, BroadNets '04 presentation

Integrated Objective Integrated Objective 24 Rudra Dutta, NCSU, BroadNets '04 presentation

Cumulation of Integrated Objective of Integrated Objective Cumulation 25 Rudra Dutta, NCSU, BroadNets '04 presentation

More Numerical Results More Numerical Results 26 Rudra Dutta, NCSU, BroadNets '04 presentation

More Numerical Results More Numerical Results 27 Rudra Dutta, NCSU, BroadNets '04 presentation