Models of multichannel interconnection systems

This work begins with presentation of modern teleinformatic systems functional problems. The authors indicate the necessity of using the multichannel communication technologies. Next, the building and exploitation costs analysis of interconnection systems was shown. Methodology of including designing system exploitation costs during the designing process was proposed. In the last part of the work the model allowing to design teleinformatic systems with expanded interconnection network more efficient was presented

A METHODOLOGY FOR NETWORK TOPOLOGY DESIGN USING FUZZY EVALUATIONS

The topology design of campus networks (CNs), a class of computer networks, is a NP-hard optimization problem. The design consists of three main steps and requires the optimization of several conflicting objectives such as minimization of cost, minimization of network delay, and minimization of maximum number of hops etc. Since some of the objectives are imprecise, fuzzy logic provides a suitable mathematical framework in such a situation. In this paper, we present a methodology to address design issues. This methodology is based on two algorithms, namely, fuzzy simulated evolution algorithm and the augmenting path algorithm. Test cases are used to evaluate the effectiveness of the methodology. Results suggest that the methodology is suitable to address the topology design proble

A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS

ABSTRACT The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes), and the links. This choice is dictated by physical and technological constraints and must optimize several objectives. Important objectives are monetary cost, network delay, hop count between communicating pairs, and reliability. Furthermore, due to the nondeterministic nature of network traffic and other design parameters, the objective criteria are imprecise. Fuzzy Logic provides a suitable mathematical framework in such a situation. In this paper, we present a Simulated Evolution algorithm for the design of campus network topology. To intensify the search, we have also incorporated Tabu Search-based characteristics in the allocation phase of the SE algorithm. The proposed fuzzy SE algorithm is compared with the Simulated Annealing heuristic. Comparison is also made with Esau–Williams (EW) algorithm, a well known constructive algorithm for the category of problems addressed in this work. Results show that on all test cases, the Simulated Evolution algorithm exhibits a more intelligent search of the solution subspace and was able to find better solutions than Simulated Annealing and Esau–Williams algorithm. Keywords: Campus Networks, Combinatorial Optimization, Fuzzy Logic, Iterative Heuristics, Network Topology, Simulated Annealing, Simulated Evolution, Tabu Search

A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS

ABSTRACT The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes), and the links. This choice is dictated by physical and technological constraints and must optimize several objectives. Important objectives are monetary cost, network delay, hop count between communicating pairs, and reliability. Furthermore, due to the nondeterministic nature of network traffic and other design parameters, the objective criteria are imprecise. Fuzzy Logic provides a suitable mathematical framework in such a situation. In this paper, we present a Simulated Evolution algorithm for the design of campus network topology. To intensify the search, we have also incorporated Tabu Search-based characteristics in the allocation phase of the SE algorithm. The proposed fuzzy SE algorithm is compared with the Simulated Annealing heuristic. Comparison is also made with Esau–Williams (EW) algorithm, a well known constructive algorithm for the category of problems addressed in this work. Results show that on all test cases, the Simulated Evolution algorithm exhibits a more intelligent search of the solution subspace and was able to find better solutions than Simulated Annealing and Esau–Williams algorithm. Keywords: Campus Networks, Combinatorial Optimization, Fuzzy Logic, Iterative Heuristics, Network Topology, Simulated Annealing, Simulated Evolution, Tabu Search

Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm

Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. Several conflicting objectives such as monetary cost, network delay, and maximum number of hops have to be optimized to achieve a desirable solution. Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. The overall cost function has been developed using fuzzy logic. Several variants of the algorithm are proposed and compared together via simulation and experimental results are provided. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Enterprise networks; Simulated evolution; Fuzzy logic; NP-hard; Multiobjective optimizatio

Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm

Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. Several conflicting objectives such as monetary cost, network delay, and maximum number of hops have to be optimized to achieve a desirable solution. Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. The overall cost function has been developed using fuzzy logic. Several variants of the algorithm are proposed and compared together via simulation and experimental results are provided. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Enterprise networks; Simulated evolution; Fuzzy logic; NP-hard; Multiobjective optimizatio

Radio Resource Virtualization in Cellular Networks

Virtualization of wireless networks holds the promise of major gains in resource usage efficiency through spectrum/radio resources sharing between multiple service providers (SPs). Radio resources however are not like a simple orthogonal resource such as time slots on a wire and its shared quantity is a function of geography and signal strength, rather than orthogonal slices. To better exploit the radio resource usage, we propose a novel scheme - radio resource virtualization (RRV) that allows SPs to access overlapping spectrum slices both in time and in space considering the transmit power, the interference, and the usage scenario (capabilities/needs of devices). We first investigate the system capacity of a simple two-cell network and show that RRV often leads to better efficiency than the well-known separate spectrum virtualization (SSV) scheme. However, the use of RRV requires careful air-interface configuration due to interference in the overlapping slices of spectrum. Therefore we next examine scenarios of a multi-cell network with fractional frequency reuse (FFR) implementing five radio resources configuration cases. From the evaluation of capacity data obtained from simulations, a variety of tradeoffs exist between SPs if RRV is applied. One example shows that capacity of the SP that operates smaller cells almost doubles while capacity of the SP deployed in larger cells may drop by 20% per subscriber. Based on these tradeoffs, we suggest configuration maps in which a network resource manager can locate specific configurations according to the demand and capabilities of SPs and their subscribers. Finally, we consider a case study on top of LTE. A system-level simulator is developed following 3GPP standards and extensive simulations are conducted. We propose and test 3 schemes that integrate RRV into the LTE radio resource management (RRM) -- unconditional RRV, time domain muting (TDM) RRV and major-interferer time domain muting (MI-TDM) RRV. Along the same line as the capacity analysis, we compare those schemes with the traditional SSV and suggest configuration maps based on the produced tradeoffs. Our investigation of RRV provides a framework that evaluates the resource efficiency, and potentially the ability of customization and isolation of spectrum sharing in virtualized cellular networks