scholarly journals Optimal Multiculture Network Design for Maximizing Resilience in the Face of Multiple Correlated Failures

2019 ◽  
Vol 9 (11) ◽  
pp. 2256
Author(s):  
Yasmany Prieto ◽  
Nicolás Boettcher ◽  
Silvia Elena Restrepo ◽  
Jorge E. Pezoa
Keyword(s):  
Network Design ◽  
Optimization Problems ◽  
Current Data ◽  
Sequential Optimization ◽  
Node Degree ◽  
Network Resilience ◽  
Correlated Failures ◽  
The Face ◽  
Network Operation ◽  
Network Topologies

Current data networks are highly homogeneous because of management, economic, and interoperability reasons. This technological homogeneity introduces shared risks, where correlated failures may entirely disrupt the network operation and impair multiple nodes. In this paper, we tackle the problem of improving the resilience of homogeneous networks, which are affected by correlated node failures, through optimal multiculture network design. Correlated failures regarded here are modeled by SRNG events. We propose three sequential optimization problems for maximizing the network resilience by selecting as different node technologies, which do not share risks, and placing such nodes in a given topology. Results show that in the 75% of real-world network topologies analyzed here, our optimal multiculture design yields networks whose probability that a pair of nodes, chosen at random, are connected is 1, i.e., its ATTR metric is 1. To do so, our method efficiently trades off the network heterogeneity, the number of nodes per technology, and their clustered location in the network. In the remaining 25% of the topologies, whose average node degree was less than 2, such probability was at least 0.7867. This means that both multiculture design and topology connectivity are necessary to achieve network resilience.

Optical Network Optimization

2013 ◽  
pp. 1-23
Author(s):  
Bin Wang ◽  
Yousef S. Kavian
Keyword(s):  
Network Design ◽  
Optical Networks ◽  
Optimization Problems ◽  
Optical Network ◽  
Routing And Wavelength Assignment ◽  
Wavelength Assignment ◽  
Topology Design ◽  
Wdm Optical Networks ◽  
Design Problems ◽  
Optical Network Design

Optical networks form the foundation of the global network infrastructure; hence, the planning and design of optical networks is crucial to the operation and economics of the Internet and its ability to support critical and reliable communication services. This book chapter covers various aspects of optimal optical network design, such as wavelength-routed Wavelength Division Multiplexing (WDM) optical networks, Spectrum-Sliced Elastic (SLICE) optical networks. As background, the chapter first briefly describes optical ring networks, WDM optical networks, and SLICE optical networks, as well as basic concepts of routing and wavelength assignment and virtual topology design, survivability, and traffic grooming in optical networks. The reader is referred to additional references for details. Many optical network design problems can be formulated as sophisticated optimization problems, including (1) Routing and Wavelength Assignment (RWA) and virtual topology design problem, (2) a suite of network design problems (such as variants of traffic grooming, survivability, and impairment-aware routing), (3) various design problems aimed at reducing the overall energy consumption of optical networks for green communication, (4) various design optimization problems in SLICE networks that employ OFDM technologies. This chapter covers numerous optical network design optimization problems and solution approaches in detail and presents some recent developments and future research directions.

scholarly journals Implementation Basic Network Design with Netkit For Evaluation of Network Learning

2019 ◽  
Vol 15 (08) ◽  
pp. 18
Author(s):  
Yuri Ariyanto ◽  
Budi Harijanto ◽  
Yan Watequlis S.
Keyword(s):  
Network Design ◽  
Network Topology ◽  
Computer Network ◽  
Virtual Laboratory ◽  
Virtual Laboratories ◽  
Network Topologies ◽  
Low Costs ◽  
Network Emulator ◽  
Practical Trial ◽  
Basic Network

A virtual laboratory with a network emulator environment using NetKit is one of series of basic network laboratories on basic computer network competencies where students are given practical trial opportunities at low costs and little effort in their implementation. Teaching computer network subjects to be easily understood by students needs an instructional media as a tool in delivering material. This media uses computer virtualization technology, i.e. creating a virtual laboratory, as a means of students in conducting experiments from the material that has been obtained. In virtual laboratories it is possible to implement network topology designs based on actual network topologies. This implementation is used as a testing tool before the network topology is implemented on the actual network. Therefore, errors can be identified first without disturbing the system that is already running. For testing, the students are given training using a basic network design consisting of the implementation of routing tests, firewalls, ftp server implementation and web server. This paper is aimed at describing ways to develop a virtual laboratory with a network emulator environment using NetKit. Moreover, several exercises on network topology implementation that are applied directly to the real world with NetKit are introduced, such as describing laboratory settings, describing the main parts of the lab, illustrating lab instructions, and reporting lab feeds.

Multitriangle: A Constant Node Degree Interconnection Network

1997 ◽  
Vol 08 (02) ◽  
pp. 187-209 ◽  
Author(s):  
Jie Wu ◽  
Haifeng Qian
Keyword(s):  
Network Topology ◽  
Interconnection Network ◽  
Average Distance ◽  
Node Degree ◽  
Formal Definition ◽  
Corner Cutting ◽  
Network Topologies ◽  
Salient Features ◽  
Definition Of ◽  
Node Routing

We propose a constant node degree network topology, multitriangle, which is hierarchical, recursive, and expansive. First we introduce a corner cutting approach that generates a set of new network topologies (including multitriangles), followed by a formal definition of the multitriangle network and discussion of its properties. The salient features of this network are that it is a constant node degree network and it can be viewed as a hierarchical ring, a popular topology which has been adopted in several commercial systems. Algorithms for node-to-node routing, hierarchical ring routing, optimal ring routing, and broadcasting are presented. The multitriangle network is analyzed in terms of diameter, degree, average distance, and message density, and results are compared with other relevant networks.

An Approach to Identify Six Sigma Robust Solutions of Multi/Many-Objective Engineering Design Optimization Problems

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Tapabrata Ray ◽  
Md Asafuddoula ◽  
Hemant Kumar Singh ◽  
Khairul Alam
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Product Family ◽  
Latin Hypercube Sampling ◽  
General Aviation ◽  
Design Variables ◽  
Engineering Design Optimization ◽  
The Face ◽  
And Performance

In order to be practical, solutions of engineering design optimization problems must be robust, i.e., competent and reliable in the face of uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this study focuses on problems where uncertainties emanate from the design variables. While approaches to identify robust optimal solutions of single and multi-objective optimization problems have been proposed in the past, we introduce a practical approach that is capable of solving robust optimization problems involving many objectives building on authors’ previous work. Two formulations of robustness have been considered in this paper, (a) feasibility robustness (FR), i.e., robustness against design failure and (b) feasibility and performance robustness (FPR), i.e., robustness against design failure and variation in performance. In order to solve such formulations, a decomposition based evolutionary algorithm (DBEA) relying on a generational model is proposed in this study. The algorithm is capable of identifying a set of uniformly distributed nondominated solutions with different sigma levels (feasibility and performance) simultaneously in a single run. Computational benefits offered by using polynomial chaos (PC) in conjunction with Latin hypercube sampling (LHS) for estimating expected mean and variance of the objective/constraint functions has also been studied in this paper. Last, the idea of redesign for robustness has been explored, wherein selective component(s) of an existing design are altered to improve its robustness. The performance of the strategies have been illustrated using two practical design optimization problems, namely, vehicle crash-worthiness optimization problem (VCOP) and a general aviation aircraft (GAA) product family design problem.

Reverse Logistics Network Design of Waste Electrical Appliances

2014 ◽  
Vol 513-517 ◽  
pp. 474-477
Author(s):  
Yang Liu ◽  
Yun Feng Zhang ◽  
Yu Xin Jin
Keyword(s):  
Network Design ◽  
Reverse Logistics ◽  
Multiple Objective ◽  
Logistics Network ◽  
Existing Problems ◽  
Reverse Logistics Network ◽  
Network Operation ◽  
Enterprise Logistics ◽  
Effective Output ◽  
Objective Program

The characteristics of waste electrical appliances and the current existing problems and difficulties product are pointed out. The network model with the effective output as a measure of enterprise logistics capability standard, is established using theory of constrains. The corresponding constraints is joined, and the parameters in the model is set. Through converting multiple objective program problems to target problems, and using LINDO6.1 software to calculate the examples results, and WITNESS software is used to simulate the real environment network operation.

scholarly journals A Scalable, Black-box, Hybrid Genetic Algorithm for Continuous Multimodal Optimization in Moderate Dimensions

2021 ◽  
Author(s):  
Klaus Johannsen ◽  
Nadine Goris ◽  
Bjørnar Jensen ◽  
Jerry Tjiputra
Keyword(s):  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Black Box ◽  
Sequential Optimization ◽  
Multimodal Optimization ◽  
Objective Functions ◽  
Special Configuration ◽  
Scalable Algorithm ◽  
Climate Research ◽  
Local Searches

Abstract Optimization problems can be found in many areas of science and technology. Often, not only the global optimum, but also a (larger) number of near-optima are of interest. This gives rise to so-called multimodal optimization problems. In most of the cases, the number and quality of the optima is unknown and assumptions on the objective functions cannot be made. In this paper, we focus on continuous, unconstrained optimization in moderately high dimensional continuous spaces (<=10). We present a scalable algorithm with virtually no parameters, which performs well for general objective functions (non-convex, discontinuous). It is based on two well-established algorithms (CMA-ES, deterministic crowding). Novel elements of the algorithm are the detection of seed points for local searches and collision avoidance, both based on nearest neighbors, and a strategy for semi-sequential optimization to realize scalability. The performance of the proposed algorithm is numerically evaluated on the CEC2013 niching benchmark suite for 1-20 dimensional functions and a 9 dimensional real-world problem from constraint optimization in climate research. The algorithm shows good performance on the CEC2013 benchmarks and falls only short on higher dimensional and strongly inisotropic problems. In case of the climate related problem, the algorithm is able to find a high number (150) of optima, which are of relevance to climate research. The proposed algorithm does not require special configuration for the optimization problems considered in this paper, i.e. it shows good black-box behavior.

Constraint Handling in Bayesian Optimization -- A Comparative Study of Support Vector Machine, Augmented Lagrangian and Expected Feasible Improvement

2021 ◽  
Author(s):  
Yuan Jin ◽  
Zheyi Yang ◽  
Shiran Dai ◽  
Yann Lebret ◽  
Olivier Jung
Keyword(s):  
Support Vector Machine ◽  
Comparative Study ◽  
Constrained Optimization ◽  
Augmented Lagrangian ◽  
Optimization Problems ◽  
Bayesian Optimization ◽  
Constraint Handling ◽  
Sequential Optimization ◽  
Support Vector ◽  
Complex Constraints

Abstract Many engineering problems involve complex constraints which can be computationally costly. To reduce the overall numerical cost, such constrained optimization problems are solved via surrogate models constructed on a Design of Experiment (DoE). Meanwhile, complex constraints may lead to infeasible initial DoE, which can be problematic for subsequent sequential optimization. In this study, we address constrained optimization problem in a Bayesian optimization framework. A comparative study is conducted to evaluate the performance of three approaches namely Expected Feasible Improvement (EFI) and slack Augmented Lagrangian method (AL) and Expected Improvement with Probabilistic Support Vector Machine in constraint handling with feasible or infeasible initial DoE. AL is capable to start sequential optimization with infeasible initial DoE, while EFI requires extra a priori enrichment to find at least one feasible sample. Empirical experiments are performed on both analytical functions and a low pressure turbine disc design problem. Through these benchmark problems, EFI and AL are shown to have overall similar performance in problems with inequality constraints. However, the performance of EIPSVM is affected strongly by the corresponding hyperparameter values. In addition, we show evidences that with an appropriate handling of infeasible initial DoE, EFI does not necessarily underperform compared with AL solving optimization problems with mixed inequality and equality constraints.

Nature-Inspired Informatics for Telecommunication Network Design

2010 ◽  
pp. 323-371 ◽  
Author(s):  
Sergio Nesmachnow ◽  
Héctor Cancela ◽  
Enrique Alba
Keyword(s):  
Network Design ◽  
Social Life ◽  
Spanning Trees ◽  
Optimization Problems ◽  
Telecommunication Network ◽  
Access Network ◽  
Planning Horizon ◽  
Optimization Techniques ◽  
Significant Research ◽  
Local Access

The speedy pace of change in telecommunications and its ubiquitous presence have drastically altered the way people interact, impacting production, government, and social life. The infrastructure for providing telecommunication services must be continuously renewed, as innovative technologies emerge and drive changes by offering to bring new services to the end users. In this context, the problem of efficiently designing the underlying networks in order to satisfy different requirements while at the same time keeping the capital and operative expenditures bounded is of ever growing importance and actuality. Network design problems have many variations, depending on the characteristics of the technologies to be employed, as well as on the simplifying hypothesis that can be applied on each particular context, and on the planning horizon. Nevertheless, in most cases they are extremely complex problems, for which exact solutions cannot be found in practice. Nature-inspired optimization techniques (belonging to the metaheuristic computational methods) are important tools in these cases, as they are able to achieve good quality solutions in reasonable computational times. The objective of this chapter is to present a systematic review of nature-inspired techniques employed to solve optimization problems related to telecommunication network design. The review is aimed at providing an insight of different approaches in the area, in particular covering four main classes of applications: minimum spanning trees, reliable networks, local access network design and backbone location, and cellular and wireless network design. A large proportion of the papers deal with single objective models, but there is also a growing number of works that study multi-objective problems, which search for solutions that perform well in a number of different criteria. While genetic algorithms and other evolutionary algorithms appear most frequently, there is also significant research on other methods, such as ant colony optimization, particle swarm optimization, and other nature-inspired techniques.

Nature-Inspired Informatics for Telecommunication Network Design

2010 ◽  
pp. 302-350
Author(s):  
Sergio Nesmachnow ◽  
Héctor Cancela ◽  
Enrique Alba
Keyword(s):  
Network Design ◽  
Social Life ◽  
Spanning Trees ◽  
Optimization Problems ◽  
Telecommunication Network ◽  
Access Network ◽  
Planning Horizon ◽  
Optimization Techniques ◽  
Significant Research ◽  
Local Access

The speedy pace of change in telecommunications and its ubiquitous presence have drastically altered the way people interact, impacting production, government, and social life. The infrastructure for providing telecommunication services must be continuously renewed, as innovative technologies emerge and drive changes by offering to bring new services to the end users. In this context, the problem of efficiently designing the underlying networks in order to satisfy different requirements while at the same time keeping the capital and operative expenditures bounded is of ever growing importance and actuality. Network design problems have many variations, depending on the characteristics of the technologies to be employed, as well as on the simplifying hypothesis that can be applied on each particular context, and on the planning horizon. Nevertheless, in most cases they are extremely complex problems, for which exact solutions cannot be found in practice. Nature-inspired optimization techniques (belonging to the metaheuristic computational methods) are important tools in these cases, as they are able to achieve good quality solutions in reasonable computational times. The objective of this chapter is to present a systematic review of nature-inspired techniques employed to solve optimization problems related to telecommunication network design. The review is aimed at providing an insight of different approaches in the area, in particular covering four main classes of applications: minimum spanning trees, reliable networks, local access network design and backbone location, and cellular and wireless network design. A large proportion of the papers deal with single objective models, but there is also a growing number of works that study multi-objective problems, which search for solutions that perform well in a number of different criteria. While genetic algorithms and other evolutionary algorithms appear most frequently, there is also significant research on other methods, such as ant colony optimization, particle swarm optimization, and other nature-inspired techniques.

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