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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

Optimal Strategy For Graceful Network Upgrade

Ram Keralapura, UC-Davis

Chen-Nee Chuah, UC-Davis

Yueyue Fan, UC-Davis

outline
Outline
  • Introduction:

Graceful Network Upgrade Problem

  • Two-Phase Framework
    • Phase-1: Finding Optimal End-State
    • Phase-2: Multistage Node Addition
  • Numerical Example and Results
  • Conclusions and Future Directions

University of California, Davis

introduction
Introduction
  • Network Upgrade
    • Adding new nodes and links into an existing operational network
    • Does not refer to upgrading individual network components
  • “Graceful” Network Upgrade
    • Network performance deterioration from the perspective of existing customers should be minimized during upgrade process

University of California, Davis

introduction cont d
Introduction (cont’d)
  • 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

scenario
Scenario
  • Tier-1 ISP backbone networks
    • Nodes  Points of Presence (PoPs)
    • Links  Inter-POP links
  • 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

network performance customers view
Network Performance: Customers’ View
  • 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

graceful network upgrade
Graceful Network Upgrade
  • 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

phase 1 finding optimal end state
Phase-1: Finding Optimal End State

Revenue

  • Objective Function:

Subject to:

    • Budget constraint:
    • Node degree constraint

Link installation cost

Node installation cost

Budget

University of California, Davis

phase 1 finding optimal end state cont d
Phase-1: Finding Optimal End State (Cont’d)
    • SD Time constraint
    • TD constraint
    • Link utilization constraint
  • Can be solved by non-linear programming techniques – tabu search, genetic algorithms

University of California, Davis

phase 2 multistage node addition
Phase-2: Multistage Node Addition
  • 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
      • Node/link construction delays
      • Sequential budget allocation
    • Time period between every stage could range from several months to few years

University of California, Davis

phase 2 multistage node addition1

Cost instead of constraints to ensure we

find feasible solutions

Phase-2: Multistage Node Addition
  • Multistage dynamic programming problem with costs for performance degradation
    • Maximize (revenue – cost)
  • Cost considered:
    • Node degree
    • Link utilization
    • SD Time
    • TD Time

University of California, Davis

numerical example

Potential Node Locations

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

numerical example phase 1 solution

New Links

New Nodes

Original Nodes

Original Links

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

numerical example result
Numerical Example – Result

University of California, Davis

numerical example phase 2 solution

New Links

New Nodes

Original Nodes

Original Links

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

numerical example phase 2 solution1
Numerical Example – Phase-2 Solution
  • Ignoring SD time and TD in Phase-2 results in significant performance deterioration for customers

University of California, Davis

conclusions
Conclusions
  • 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

future work
Future Work
  • 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

questions
Questions?
  • http://www.ece.ucdavis.edu/rubinet/

University of California, Davis

phase 2 multistage node addition2
Phase-2: Multistage Node Addition
  • Multistage dynamic programming problem with costs for performance degradation
  • Cost of node degree:
  • Cost of link utilization:

University of California, Davis

phase 2 multistage node addition3
Phase-2: Multistage Node Addition
  • Cost of SD Time:
  • Cost of TD:

University of California, Davis

phase 2 multistage node addition4
Phase-2: Multistage Node Addition
  • Functional equation:

University of California, Davis

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