Globally Optimal Distributed Globally Optimal Distributed Batch Batch Reconfiguration for Hazard-free Reconfiguration for Hazard-free Dynamic Provisioning: Dynamic Provisioning: How an Entire Network can “Think How an Entire Network can “Think Globally and Act Locally” Globally and Act Locally” Wayne D. Grover Wayne D. Grover [email protected][email protected]University of Alberta and TRLabs University of Alberta and TRLabs Edmonton, AB, Canada Edmonton, AB, Canada DRCN 2007 DRCN 2007 La Rochelle, France, Oct. 70-10 La Rochelle, France, Oct. 70-10
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Globally Optimal Distributed Batch Reconfiguration for Hazard-free Dynamic Provisioning:
Globally Optimal Distributed Batch Reconfiguration for Hazard-free Dynamic Provisioning: How an Entire Network can “Think Globally and Act Locally” Wayne D. Grover [email protected] University of Alberta and TRLabs Edmonton, AB, Canada DRCN 2007 La Rochelle, France, Oct. 70-10. - PowerPoint PPT Presentation
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Setting the stage..what motivates this proposal?Setting the stage..what motivates this proposal?
• US National Science Foundation:– Calls for “completely new approaches to network operations.”
– Identifies robust networking as one of the “grand challenges” in networking science
• Concern that existing peer-to-peer asynchronous distributed provisioning scheme has the risks of network state incoherence– E.g. [1] Pandi & Wosinska, ICTON-RONEXT 2005
• Separately, in other industries, there is a move to exploring the applications and benefits of “on-line O.R.” – Existing distributed provisioning schemes can only employ greedy
solution methods
• What if a whole network could “think globally, but act locally”?– Greater resource efficiencies, greatly reduced signalling, hazard-free
What Problem(s) are we trying to solve in dynamic What Problem(s) are we trying to solve in dynamic protected service provisioning?protected service provisioning?
1. The inherent risk of schemes that operate dynamically, on the per-connection timescale, assuming global state coherency at all times.• Risky !• High signalling volumes
Rather than trying to quantity and lower the risk: is there some approach that fundamentally avoids the risk in the first place?
2. In existing concepts provisioning is per-path with no chance to globally optimize • Periodic re-optimization of overall network
Observations / Concerns about Dynamic SBPPObservations / Concerns about Dynamic SBPP
• Every node needs and assumes a complete and current network state database, and existing current protection capacity sharing relationships• Link state updates are advertised on a per-connection basis• Link state updates are disseminated asynchronously by any node at the same time other nodes are relying and acting upon time critical state information.• The total database of network state that is operationally “critical” grows at least as O(n3) with size of the network or operating domain and also intensifies with frequency of changes in the network.
Alternatives for Dynamic Automated Provisioning Alternatives for Dynamic Automated Provisioning
• Centralized Control: Global view, one operation at a time.– Safe (in the present regard) but other downsides
• Apply packet priorities to update messages, use TE summary packets, etc. – i.e., measures to try to just mitigate the risk– Will eventually crash when provisioning is dynamic enough
• “Protected Working Capacity Envelope” Concept– Removes protection arrangements from the per-connection time
scale Refs: Grover- Comm Mag, Shen & Grover, Shen PhD; available at www.ece.ualberta.ca/~grover
• Proposal: “Globally Optimal” Distributed Synchronous Batch Re-optimization – Eliminates the hazard of database incoherence– Framework yields other advantages
• Globally synchronous change actions, not asynchronous actions– Reliance on “precise time” to coordinate actions and decisions.
• Relegating all operationally critical signaling for state update to non- real-time communication requirements– Robust confirmation of global state database coherence before any
reliance upon it for network actions
• Solving a globally optimal reconfiguration solution – But nodes act locally to put into effect their parts only of globally optimal
• 5 to 10 minute interval envisaged– More generally, the period is relative to the connection holding
time and request rate (I.e. provisioning traffic intensity)
• Nodes make no changes to network connection state during this time
• Nodes observe:– New requests– Departures (released connections)as they arise at their location only.
• At end of the period nodes emit a change summary packet– Like an existing LSA, but contains batch change info– Robust error detection / correction encoded on packet– This dissemination is not real-time-critical
• Each node locally solves an instance of the globally optimal reconfiguration problem– May be any problem version network operator prefers– Example: Route and protect maximum number of the new service
requests• While reclaiming capacity from released connections• With or without permission to re-optimize existing protection
other Variants: – Multiple priorities or protection classes (multi-QoP)– Permission to re-arrange selected working paths– Strategies to include hedging against future uncertainty– Impairment aware, availability aware routing, etc.
• Nodal solutions have to be “identical not just equivalent”• Prospect here for true “on-line O.R.”
– any reduced complexity version of the optimal problem can also be substituted here
Step 4.“Acting Locally”: Step 4.“Acting Locally”: Node do Node do theirtheir part of the solution part of the solution (only)(only)
• On the next globally precise-time mark:– Each node activates the switching matrix changes to put into
effect its part (only) of the complete network reconfiguration solution.
– No continuing existing connection is altered– Service access nodes observe the turn-up of their new
connections and test end-to-end.
• New operating phase commences• Change request accumulation continuesNote that this results in creation of a complete set of new service paths and their protection arrangements simultaneously in parallel on the network with no signaling. Correctness of the outcome is independently validated by each end-node pair (as it would be in any case).
Sub-Study: Benefits of Optimal Batch Incremental Re-Sub-Study: Benefits of Optimal Batch Incremental Re-Provisioning Provisioning (with Z. Pandi on COST 270 STSM to TRLabs)(with Z. Pandi on COST 270 STSM to TRLabs)
• Simulation of an “on-line O.R.” application for batch incremental re-optimization that is made possible by this framework.
• Statistically non-stationary random traffic demand – i.e., not just random but spatially and temporally evolving random arrival /
departure traffic
– Tests / illustrates ability for scheme to inherently track and re-optimize for time-evolving demand patterns
• Each node accumulates batch change info• At end of each batch period, globally optimal incremental
reconfiguration problem solved (on a single CPU)– Global changes put into effect locally in simulated network
• Compared performance against asynchronous independent provisioning using best known SBPP provision algorithm
Research Directions within this frameworkResearch Directions within this framework• Incremental re-optimization models and strategies that the framework enables
– Options such as spare capacity re-optimization or not– Multi QoP classes, priorities– Working re-arrangeable service classes?– Maximum revenue, minimum load, etc: different objectives – Multi-QoP provisioning solutions– Incremental on-line grooming optimization
• Different approaches to “identical not just equivalent” solution of the disparate instances of the same global optimization problem.
• Links to the “scheduled lightpath” connection planning problem and the “network consolidation problem.”
• Accommodation for a top-priority no-delay service class– If thought essential
• Extension to Domains rather than nodes
• Links to the PWCE concept
• Collaborations in this area already begun with B. Jaumard, Networks OR group at Concordia U., Montreal
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