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This page is a per-chapter index of the Net2Plan resources (online and offline algorithms, reports) appearing in the book:

Pablo Pav?n Mariņo, Optimization of computer networks. Modeling and algorithms. A hands-on approach, Wiley 2016. Pablo Pavón Mariño,
Chapter 3 
Performance Metrics in Networks
Section 3.2
  • Report_delay: This report receives as an input a network design, where the network is assumed to be based on packet switching, and estimates the packet delay of the different flows.
Section 3.3.3
  • Online_evGen_generalGenerator: Generates events to a technology-agnostic network, consisting of connection requests/releases and failures and repairs.
  • Online_evProc_generalProcessor: Implements the reactions of a technology-agnostic network to connection requests under various CAC options, and reactions to failures and repairs under different recovery schemes.
Section 3.7.3
  • Report_availability: This report receives as an input a network design, the network recovery scheme algorithm, and the network risks (SRGs), and estimates the availability of the network (including individual availabilities for each demand), using an enumerative process that also provides an estimation of the estimation error.
  • Report_perSRGFailureAnalysis: This report receives as an input a network design, the network recovery scheme algorithm, and a set of network risks (SRGs), and computes the availability of each network and each demand, in the no-failure state, and in each of the single-SRG failure states.
Exercise 3.7
  • Online_evGen_generalGenerator: Generates events to a technology-agnostic network, consisting of connection requests/releases and failures and repairs.
  • Online_evProc_generalProcessor: Implements the reactions of a technology-agnostic network to connection requests under various CAC options, and reactions to failures and repairs under different recovery schemes.
Exercise 3.8
  • Online_evGen_generalGenerator: Generates events to a technology-agnostic network, consisting of connection requests/releases and failures and repairs.
  • Online_evProc_generalProcessor: Implements the reactions of a technology-agnostic network to connection requests under various CAC options, and reactions to failures and repairs under different recovery schemes.
Chapter 4 
Routing Problems
Section 4.2
Section 4.3
  • Offline_fa_xde11PathProtection: Solves several variants of unicast routing problems with 1+1 protection, with flow-link formulations
  • Offline_fa_xdeFormulations: Solves several variants of unicast routing problems, with flow-link formulations
  • Offline_fa_xdeSharedRestoration: Solves several variants of unicast routing problems with flow-link formulations, so that designs are fault tolerant to a set of failure states, using shared restoration
Section 4.4
Section 4.6.2
Section 4.6.3
Section 4.6.6
Section 4.6.7
  • Offline_fa_xdeSharedRestoration: Solves several variants of unicast routing problems with flow-link formulations, so that designs are fault tolerant to a set of failure states, using shared restoration
Section 4.6.8.1
Section 4.6.8.2
Chapter 5 
Capacity Assignment Problems
Section 5.2.3
Section 5.4.1
Section 5.4.2
Section 5.5
Chapter 6 
Congestion Control Problems
Section 6.2
Section 6.3
Chapter 7 
Topology Design Problems
Section 7.2
Section 7.3
Section 7.4
Chapter 9 
Primal Gradient Algorithms
Section 9.3
Section 9.4
  • Online_evProc_congestionControlPrimal: This module implements a distributed primal-gradient based algorithm using a barrier function, for adapting the demand injected traffic (congestion control) in the network, to maximize the network utility enforcing a fair allocation of the resources.
Section 9.5
  • Online_evProc_persistenceProbAdjustmentPrimal: This module implements a distributed primal-gradient based algorithm for adjusting the link persistence probabilities in a wireless network with a ALOHA-type random-access based MAC, to maximize the network utility enforcing a fair allocation of the resources.
Section 9.6
  • Online_evProc_powerAssignmentPrimal: This module implements a distributed primal-gradient based algorithm for adjusting the transmission power of the links in a wireless network subject to interferences, to maximize the network utility enforcing a fair allocation of the resources.
Chapter 10 
Dual Gradient Algorithms
Section 10.2
Section 10.3
  • Online_evProc_backpressureRoutingDual: This module implements a distributed dual-gradient based algorithm for adapting the network routing to the one which minimizes the average number of hops, that results in a purely decentralized backpressure scheme.
Section 10.4
  • Online_evProc_congestionControlDual: This module implements a distributed dual-gradient based algorithm, for adapting the demand injected traffic (congestion control) in the network, to maximize the network utility enforcing a fair allocation of the resources.
Section 10.5
  • Online_evProc_csmaBackoffOptimizationDual: This module implements a distributed dual-gradient based algorithm for adjusting the backoff windows sizes in a wireless network with a CSMA-mased MAC, to maximize the network utility enforcing a fair allocation of the resources.
Chapter 11 
Decomposition Techniques
Section 11.3
  • Offline_cba_congControLinkBwSplitTwolQoS: In a network with demands of two QoS, jointly optimizes the demand injected traffic and link capacity assigned to each solving a formulation.
  • Online_evProc_congControlAndQoSTwoClassesPrimalDecomp: This module implements a distributed primal-decomposition-based gradient algorithm, for a coordinated adjustment of the congestion control of two types of demands (with different utility functions), and the fraction of each link capacity to grant to the traffic of each type, to maximize the network utility enforcing a fair allocation of the resources.
Section 11.4
  • Online_evProc_congControlAndBackpressureRoutingDualDecomp: This module implements a distributed dual-decomposition-based gradient algorithm, for a coordinated adjustment of the traffic to inject by each demand (congestion control), and the routing (backpressure based) of this traffic in the network, to maximize the network utility enforcing a fair allocation of the resources.
  • Online_evProc_congControlAndTransmissionPowerAssignmentDualDecomp: This module implements a distributed dual-decomposition-based gradient algorithm, for a coordinated adjustment of the traffic to inject by each demand (congestion control), and the transmission power in each link of the underlying wireless network, to maximize the network utility enforcing a fair allocation of the resources.
Section 11.5
Section 11.6
  • Online_evProc_multidomainRoutingPrimalDecomp: This module implements a distributed primal-decomposition-based gradient algorithm, for a coordinated adjustment of the routing in multiple domains (or cluster, or autonomous systems) in a network, so that domains do not need to exchange sensitive internal information, and minimize the average number of hops in the network.
Section 11.7
Chapter 12 
Heuristic Algorithms
Section 12.3
  • Offline_fa_ospfWeightOptimization_localSearch: Searches for the OSPF link weights that minimize a measure of congestion, using a local-search heuristic The time evolution of different metrics can be stored in output files, for later processing.
Section 12.4
  • Offline_fa_ospfWeightOptimization_SAN: Searches for the OSPF link weights that minimize a measure of congestion, using a simulated annealing (SAN) heuristic The time evolution of different metrics can be stored in output files, for later processing.
Section 12.5
  • Offline_fa_ospfWeightOptimization_tabuSearch: Searches for the OSPF link weights that minimize a measure of congestion, using a tabu search heuristic The time evolution of different metrics can be stored in output files, for later processing.
Section 12.6
Section 12.7
  • Offline_fa_ospfWeightOptimization_GRASP: Searches for the OSPF link weights that minimize a measure of congestion, using a GRASP heuristic The time evolution of different metrics can be stored in output files, for later processing.
Section 12.8
Section 12.9
  • Offline_fa_ospfWeightOptimization_EA: Searches for the OSPF link weights that minimize a measure of congestion, using an evolutionary algorithm (genetic algorithm) heuristic The time evolution of different metrics can be stored in output files, for later processing.
Section 12.10
  • Offline_tcfa_wdmPhysicalDesign_graspAndILP: This algorithm is devoted to solve the several network planning problems in an optical WDM network (fiber placement, RWA, under different recovery schemes), appearing in the case study in the book section mentioned below.