A Pareto based approach with elitist learning strategy for MPLS/GMPS networks

Mohsin Masood; Mohamed Mostafa Fouad; Ivan Glesk

A Pareto based approach with elitist learning strategy for MPLS/GMPS networks

Mohsin Masood, Mohamed Mostafa Fouad, Ivan Glesk

Electronic And Electrical Engineering

Research output: Contribution to conference › Proceeding

2 Citations (Scopus)

47 Downloads (Pure)

Abstract

Abstract—Modern telecommunication networks are based on diverse applications that highlighted the status of efficient use of network resources and performance optimization. Various methodologies are developed to address multi-objectives optimization within the traffic engineering of MPLS/ GMPLS networks. However, Pareto based approach can be used to achieve the optimization of multiple conflicting objective functions concurrently. The paper considered two objective functions such as routing and load balancing costs functions. The paper introduces a heuristics algorithm for solving multi-objective multiple constrained optimization (MCOP) in MPLS/ GMPLS networks. The paper proposes the application of a Pareto based particle swarm optimization (PPSO) for such network’s type and through a comparative analysis tests its efficiency against another modified version; Pareto based particle swarm optimization with elitist learning strategy (PPSO ELS). The simulation results showed that the former proposed approach not only solved the MCOP problem but also provide effective solution for exploration problem attached with PPSO algorithm.

Original language	English
Publication status	Published - 27 Sep 2017
Event	9th Computer Science & Electronic Engineering Conference - Tony Rich Teaching Centre, University of Essex, Colchester, United Kingdom Duration: 27 Sep 2017 → 29 Sep 2017 http://ceec.uk/

Conference

Conference	9th Computer Science & Electronic Engineering Conference
Abbreviated title	CEEC'17
Country	United Kingdom
City	Colchester
Period	27/09/17 → 29/09/17
Internet address	http://ceec.uk/

Keywords

MPLS
GMPLS network optimization
particle swarm optimization
heurastic algorithm
traffic engineering
weight inertia

Access to Document

Masood-etal-CEEC-2017-Pareto-based-approach-with-elitist-learning-strategy-for-MPLS-GMPS-networksAccepted author manuscript, 575 KB

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Cite this

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title = "A Pareto based approach with elitist learning strategy for MPLS/GMPS networks",

abstract = "Abstract—Modern telecommunication networks are based on diverse applications that highlighted the status of efficient use of network resources and performance optimization. Various methodologies are developed to address multi-objectives optimization within the traffic engineering of MPLS/ GMPLS networks. However, Pareto based approach can be used to achieve the optimization of multiple conflicting objective functions concurrently. The paper considered two objective functions such as routing and load balancing costs functions. The paper introduces a heuristics algorithm for solving multi-objective multiple constrained optimization (MCOP) in MPLS/ GMPLS networks. The paper proposes the application of a Pareto based particle swarm optimization (PPSO) for such network{\textquoteright}s type and through a comparative analysis tests its efficiency against another modified version; Pareto based particle swarm optimization with elitist learning strategy (PPSO ELS). The simulation results showed that the former proposed approach not only solved the MCOP problem but also provide effective solution for exploration problem attached with PPSO algorithm.",

keywords = "MPLS, GMPLS network optimization, particle swarm optimization, heurastic algorithm, traffic engineering, weight inertia",

author = "Mohsin Masood and Fouad, {Mohamed Mostafa} and Ivan Glesk",

note = "(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.; 9th Computer Science & Electronic Engineering Conference, CEEC'17 ; Conference date: 27-09-2017 Through 29-09-2017",

year = "2017",

month = sep,

day = "27",

language = "English",

url = "http://ceec.uk/",

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TY - CONF

T1 - A Pareto based approach with elitist learning strategy for MPLS/GMPS networks

AU - Masood, Mohsin

AU - Fouad, Mohamed Mostafa

AU - Glesk, Ivan

N1 - (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2017/9/27

Y1 - 2017/9/27

N2 - Abstract—Modern telecommunication networks are based on diverse applications that highlighted the status of efficient use of network resources and performance optimization. Various methodologies are developed to address multi-objectives optimization within the traffic engineering of MPLS/ GMPLS networks. However, Pareto based approach can be used to achieve the optimization of multiple conflicting objective functions concurrently. The paper considered two objective functions such as routing and load balancing costs functions. The paper introduces a heuristics algorithm for solving multi-objective multiple constrained optimization (MCOP) in MPLS/ GMPLS networks. The paper proposes the application of a Pareto based particle swarm optimization (PPSO) for such network’s type and through a comparative analysis tests its efficiency against another modified version; Pareto based particle swarm optimization with elitist learning strategy (PPSO ELS). The simulation results showed that the former proposed approach not only solved the MCOP problem but also provide effective solution for exploration problem attached with PPSO algorithm.

AB - Abstract—Modern telecommunication networks are based on diverse applications that highlighted the status of efficient use of network resources and performance optimization. Various methodologies are developed to address multi-objectives optimization within the traffic engineering of MPLS/ GMPLS networks. However, Pareto based approach can be used to achieve the optimization of multiple conflicting objective functions concurrently. The paper considered two objective functions such as routing and load balancing costs functions. The paper introduces a heuristics algorithm for solving multi-objective multiple constrained optimization (MCOP) in MPLS/ GMPLS networks. The paper proposes the application of a Pareto based particle swarm optimization (PPSO) for such network’s type and through a comparative analysis tests its efficiency against another modified version; Pareto based particle swarm optimization with elitist learning strategy (PPSO ELS). The simulation results showed that the former proposed approach not only solved the MCOP problem but also provide effective solution for exploration problem attached with PPSO algorithm.

KW - MPLS

KW - GMPLS network optimization

KW - particle swarm optimization

KW - heurastic algorithm

KW - traffic engineering

KW - weight inertia

M3 - Proceeding

T2 - 9th Computer Science & Electronic Engineering Conference

Y2 - 27 September 2017 through 29 September 2017

ER -