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# Optimal Strategy For Graceful Network Upgrade - PowerPoint PPT Presentation

Optimal Strategy For Graceful Network Upgrade. Ram Keralapura, UC-Davis Chen-Nee Chuah, UC-Davis Yueyue Fan, UC-Davis. Outline. Introduction: Graceful Network Upgrade Problem Two-Phase Framework Phase-1: Finding Optimal End-State Phase-2: Multistage Node Addition

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### Optimal Strategy For Graceful Network Upgrade

Ram Keralapura, UC-Davis

Chen-Nee Chuah, UC-Davis

Yueyue Fan, UC-Davis

• Introduction:

• Two-Phase Framework

• Phase-1: Finding Optimal End-State

• Numerical Example and Results

• Conclusions and Future Directions

University of California, Davis

• Does not refer to upgrading individual network components

• Network performance deterioration from the perspective of existing customers should be minimized during upgrade process

University of California, Davis

• Steps for Graceful Network Upgrade

• Identifying set of potential locations where new nodes can be added

• Determining a subset of potential locations where nodes should added and how they should be connected to the rest of the network

• Determining an ideal sequence for adding nodes and links into the network

University of California, Davis

• Tier-1 ISP backbone networks

• Nodes  Points of Presence (PoPs)

• Assumptions:

• ISPs can reliably determine the future traffic demands when new nodes are added

• ISPs can reliably estimate the revenue associated with the addition of every new node

• Revenues resulting from different node additions are independent of each other

University of California, Davis

• Link failures are the primary reason for performance deterioration in tier-1 ISP backbone networks

• Impact of link failures: Service Disruption (SD) Time and Traffic Disruption (TD)

• Depends on the operational network conditions like shortest paths between nodes, exit points for prefixes, traffic demands, etc.

• Adding new nodes and links can change the above operational network conditions

University of California, Davis

• Given a budget constraint:

• Find where to add new nodes and how they should be connected to the rest of the network

• Determine optimal sequence for this upgrade

• Two-Phase Framework

• Phase-1: Finding Optimal End State

• Formulated as an optimization problem

• Phase-2: Multistage Node Addition Strategy

• Formulated as a multistage dynamic programming (DP) problem

University of California, Davis

Revenue

• Objective Function:

Subject to:

• Budget constraint:

• Node degree constraint

Node installation cost

Budget

University of California, Davis

• SD Time constraint

• TD constraint

• Can be solved by non-linear programming techniques – tabu search, genetic algorithms

• University of California, Davis

• Assumptions:

• Already have the solution from Phase-1

• ISP wants to add one node and its associated links into the network at every stage

• Hard to debug if all nodes are added simultaneously

• Sequential budget allocation

• Time period between every stage could range from several months to few years

University of California, Davis

find feasible solutions

• Multistage dynamic programming problem with costs for performance degradation

• Maximize (revenue – cost)

• Cost considered:

• Node degree

• SD Time

• TD Time

University of California, Davis

Original Nodes

Numerical Example

1

Chicago

Seattle

C

New York

D

A

San Jose

B

2

3

Phoenix

Atlanta

Dallas

4

Miami

University of California, Davis

New Nodes

Original Nodes

1

Chicago

Seattle

C

New York

D

A

San Jose

B

2

3

Phoenix

Atlanta

Dallas

4

Miami

Solution from Phase-1

Numerical Example – Phase-1 Solution

University of California, Davis

University of California, Davis

New Nodes

Original Nodes

Numerical Example – Phase-2 Solution

1

Chicago

Seattle

C

New York

D

A

San Jose

B

2

3

Phoenix

Atlanta

Dallas

4

Miami

Solution from Phase-1

University of California, Davis

• Ignoring SD time and TD in Phase-2 results in significant performance deterioration for customers

University of California, Davis

• We propose a two phase framework for graceful upgrade of operational network

• Important to consider performance from users’ perspective (service/traffic disruptions)

• We illustrated the feasibility of our approach using a simple numerical example

• The framework is flexible and can be easily adapted to include other constraints or scenario-specific requirements

University of California, Davis

• Need to relax simplifying assumptions

• Revenue and traffic demands in the network after node additions could dynamically change because of endogenous and exogenous effects

• Adaptive and stochastic dynamic programming

• Revenue generated by adding one node is usually not independent of other node additions

• Uncertainty in predicting traffic demands

• Applications of this framework in different environments

• Wireless networks (WiFi, WiMax, celllular)

University of California, Davis

• http://www.ece.ucdavis.edu/rubinet/

University of California, Davis

• Multistage dynamic programming problem with costs for performance degradation

• Cost of node degree:

University of California, Davis

• Cost of SD Time:

• Cost of TD:

University of California, Davis

• Functional equation:

University of California, Davis