Cabling and Cost Optimization System for IP Based ... - IEEE Xplore

2014 IEEE Region 10 Symposium

Cabling and Cost Optimization System for IP Based Networks Through Genetic Algorithm Charmaine B. Balubal*, Angela Rachel D. Bernardo*, Bryan Lloyd L. Lasheras*, Regina A. Uyehara*, Argel A. Bandala**, Elmer P. Dadios ** *Department of Computer Science Polytechnic University of the Philippines Manila, Philippines

** Gokongwei College of Engineering De La Salle University Manila, Philippines

Abstract — The creation of an optimized cabling plan in terms of cost through optimized cable length was introduced in this study. The researchers designed a system that utilized Genetic Algorithm for the said optimization. This system was integrated in a graphical user interface created using visual c# language which enables the users to upload an image representing the floor plan of the desired network to be optimized. The user can then place specified components on the floor plan. Lastly, the system will generate the optimized cabling plan which the user can readily print. Furthermore, a complete bill of materials and costing report will be generated also. The system generated these outputs by using genetic algorithm in the graphical inputs which were processed and converted in numerical representations. Upon accomplishing all the experimentations, the system yielded 99.51% optimization accuracy with 99.02% as the highest optimization level generated after accomplishing 100 trials on 10 different floor plans. Keywords -- Genetic Algorithm, Optimization, Network Cabling, IP networks

gives better performances in wired communication. This is achieved through the implementation of the Structured Cabling Standards. The Structured Cabling Standards were developed to create a structured and standardized cabling system. These structured cabling standards are published by the CISCO. A wired network involves the use of various cables such as the twisted pair, coaxial cables and fiber optic cables. The use of these cables depends on the network topology, the size and the protocol. In structured cabling, having an optimized yet organized network plan is very advantageous. This provides lesser funds in installing the cabling system which will be very much helpful to its users.

S

I.

Optimization means to upgrade or improve something. There are various algorithms which can be used in optimization problems. Genetic Algorithm is one of the algorithms for solving optimization problems It can easily find the best solution by an adaptation search algorithm which imitates the process of natural selection or the natural evolution [2]. It is proven that the genetic algorithm is efficient and consistent in searching for optimal solution [3] and can easily handle more number of variables [4] Genetic algorithm starts its process by randomly selecting an initial set of solutions. This set is called the population. Each element or solution within the set is called the chromosomes which evolve through repeated iterations, called generation. In each generation, these chromosomes are evaluated using the fitness function. The next generation can be generated using a crossover or a mutation operator. After doing a series of iterations, the best chromosome will be selected and would most likely be the optimized solution. [5]

INTRODUCTION

tructured cabling became prominent as technology develops and advances in the recent years. Analyzing and having a good choice of cabling systems provides various advantages such as minimizing expenses, saving time and also to refrain or at least lessen the needs for troublesome upgrades in the future. Structured cabling systems should also be capable of efficiently handling huge data streams which are nowadays involved in the use of internet and ecommerce applications [1]. A Wired Network allows the communication and the transmission of data through a wired-based communication technology. Structured Cabling serves as a huge advantage and

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The study aims to develop the said system to alleviate the problems introduced by designing simulation software which enables the optimization

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of cabling and network costs of a cabling system through the implementation of Genetic Algorithm which is effective for searching optimal connection of cables [6] . The study provides a system which is able to generate the optimized cabling connections and present a complete bill of materials and costing report. II.

converted into a matrix of the same size. The color value of each picture element is then evaluated.

ARCHITECTURE OF THE SYSTEM

Figure 1 displays the general block diagram of the designed system. As can be seen, there are three core blocks that was utilized by the system. The first block is named as the input block. This block deals with the translation of information from the user to the machine where the system runs. From the tangible information that the user can understand, the input block translates this into numerical values. The second block is the genetic algorithm block. This stage deals with the processing of the information from the previous block into a numeric transformation. This block is the central deciding block that generates the optimized set of routing. Lastly, the output block deals with the translation of the information from the genetic algorithm block into visual information from the user. This block also calculates the cost and bill of materials presented to the user. These blocks are embedded in the graphical user interface to provide ease of use for the users.

. Figure 2 Sample Floor Plan

The authors decided to convert the whole image into its binary representation. Using equation 1 this objective was obtained. The original image is stored in the variable P while the result or the output is stored in the matrix O. As can be derived, the value of the pixels are checked and rounded off to the nearest binary value.

ࡻሾ࢏ǡ࢔ሿ  ൌ

࢏ୀ࢘࢕࢝ σ࢔ୀࢉ࢕࢒࢛࢓࢔ ࡼሾ࢏ǡ࢔ሿ ࢏ୀ࢔ୀ૚

(1) ࡼ ൌ  ૙૙ழ௉ழଵଶ଼ ࢏ ൜ ࡼ ൌ  ૚૚૛ૢழ௉ழଶହହ ࢏

Equation 1

The input block also serves the purpose of enabling the user to customize the position and equipment to be included in the network design. All of the networking equipment are available for the user’s utilization. Figure 3 displays the sample layout of the network plan with equipment’s symbols included. Upon the inclusion of the networking devices, the matrix generated by uploading the floor plan will automatically be updated. The numeric value assigned to a certain device will automatically overwrite the matrix. Figure 4 shows the matrix information of the floor plan with equipment included. As can be seen, a space is represented by 0 while walls are represented by 1.

Figure 1 The General Block Diagram of the System

A. Input Block The first stage that the system initiates is the input block. The first process that this block requires is the floor plan image that the user uploads into the system. As can be seen in figure 2, a sample of a floor plan is presented. An image processing system is included. The image is Figure 3 Component Layout

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The numeric equivalent of a part of the image from Figure 3, being the 1's as the walls or the obstacles and 0's as free spaces, is derived from the formula on Equation 1 and adding 4's or their corresponding indexes used for equipment identification.

Figure 5 Process Flow of Genetic Algorithm

C. Output Block The final block of the system is the output block of the system. Since the outputs of the genetic algorithm are the coordinates of the path of the cables, another stage is needed to convert this numeric value into information that the user can easily understand. Given the coordinates of the optimized cable line paths, the block draws a line in accordance with the path coordinates. As can be seen in figure 6, the red line is the automated and optimized cabling route from the genetic algorithm block.

Figure 4 Numeric Equivalent of Floor Plan

B. Genetic Algorithm The Genetic Algorithm is the technique used by the proponents to generate wire routing as well as cost optimization. The block requires the coordinates of the two points to be connected. From figure 4, the coordinates of the equipment are the index of the matrix elements whose values are either 0 or 1. The objective of the algorithm is to generate a path connecting two devices with the minimum length while avoiding obstacles. Note that the switches' positions are user defined given that it does not violate the cable length requirement otherwise, it will be moved slightly through the GA to cope with the requirement.

Figure 6 Output Floor Plan

Figure 5 illustrates the flow of data for the genetic algorithm used. The first process is to generate the population pool for the genetic algorithm. The number of characteristics or genes of the population depends on the number of bends that the obstacle introduces. A collection of these possible values are then stored. After which randomly two of the candidate information are chosen and undergo cross mating. Then, a new set of paths is generated and subjected for testing. The fitness function is used to evaluate the accurateness of the said information. In this study, the distance formula was used as the fitness function. Finally, after a number of iterations, a final solution will be generated. The generated solution is composed of coordinates in which the cabling symbol will be drawn. These values are then fed to the next block, which is the output block, for transformation of information.

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The user is given the option to generate the bill of material list of the generated system. Figure 7 shows a sample of the text file output of the bill of materials. As can be seen, all of the devices used are listed together with the number of units. Moreover, cost per unit and total cost of the devices are also displayed. Finally, the summary of prices or cost is also given.

Figure 7 Output Bill of Materials

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III.

Each optimization level on each trial on each floor plan is computed by the use of Relative True Error formula.

EXPERIMENT RESULTS

The system’s performance is evaluated by conducting several experiments that will test certain parameters which can determine the system’s level of optimization. There are two parameters tested during the experimentation. The first parameter tested is the total cable length. Another is the overall cost of the generated network plan. The researchers performed 100 trials on 10 different floor plans. The paths generated will be subjected to the fitness function, computed with the use of straight lines connecting the devices, which is the derived using the distance formula. The path with the least error computed will be considered the best path.

‫ ݎ݋ݎݎ݁݁ݑݎݐ‬ൌ ݉݁ܽ‫ ݁ݑ݈ܽݒ݀݁ݎݑݏ‬െ ݈݅݀݁ܽ‫݁ݑ݈ܽݒ‬ To compute for the true error, the ideal value is subtracted from the measured value. It is the magnitude of the difference between the exact value and the approximation. After computing for the True Error, the researchers computed for the Relative True Error in each trial. ‫ ݎ݋ݎݎ݁݁ݑݎݐ݁ݒ݅ݐ݈ܽ݁ݎ‬ൌ

‫ͲͲͳ כ ݎ݋ݎݎ݁݁ݑݎݐ‬ ݈݅݀݁ܽ‫݁ݑ݈ܽݒ‬

To get the Relative True Error, multiply the True Error by 100 and divide it by the true value. ܾ݈݈ܿܽ݁݁݊݃‫݈݁ݒ݈݁݊݋݅ݐܽݖ݅݉݅ݐ݌݋݄ݐ‬ ൌ ͳͲͲ െ ‫ݎ݋ݎݎ݁݁ݑݎݐ݁ݒ݅ݐ݈ܽ݁ݎ‬ ݊݁‫ݕܿܽݎݑܿܿܽݐݏ݋݈ܿ݊ܽ݌݇ݎ݋ݓݐ‬ ൌ ͳͲͲ െ ‫ݎ݋ݎݎ݁݁ݑݎݐ݁ݒ݅ݐ݈ܽ݁ݎ‬ The accuracy or the optimization level is then computed after getting the True Error and the Relative True Error. To compute for the accuracy level, the Relative True Error is subtracted from 100. The ideal value is the fitness function which is derived using distance formula.

Figure 8. Results for Cable Length Optimization

Figure 9 shows the graph that illustrates the performance of the system in optimizing cable length with 10 floor plans used for testing. Furthermore, the overall characteristics or the mean was graphed. The mean yields at 90.59%.

ඥሺ‫ݔ‬ଶ െ ‫ݔ‬ଵ ሻଶ ൅  ሺ‫ݕ‬ଶ െ ‫ݕ‬ଵ ሻଶ Table 1 summarizes the mean of 100 trials for all of the 10 floor plans selected by the researchers.

Table 1 Cable Length Optimization

Floor Plan # 1 2 3 4 5 6 7 8 9 10

Average Optimization Levels of Cable Length 66.87% 93.85% 97.31% 97.97% 84.93% 92.53% 97.98% 97.38% 91.54% 85.54%

Table 2 Cable Length Optimization

Figure 9 Result of Network Cost Optimization

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Figure 10 illustrates the performance of the system in optimizing network cost with the same 10 floor plans used for testing cable length. Furthermore, the overall characteristics or the mean was graphed. The mean yields at 91.32%.

Optimization of CFST Arch Bridge," in IEEE CONFERENCE PUBLICATIONS, Jinan, 2009. [3] P. Kumsawat, "A Genetic Algorithm Optimization Technique for MultiwaveletBased Digital Audio Watermarking," EURASIP Journal on Advances in Signal Processing, 2010.

Table 3 Optimization of Network Cost

Floor Plan # 1 2 3 4 5 6 7 8 9 10

Average Accuracy Levels of Network Plan Cost 83.05% 83.97% 93.94% 99.51% 94.74% 89.99% 98.74% 95.17% 93.44% 80.64%

[4] V. Lute, A. Upadhyay and K. K. Singh, "Genetic Algorithms-Based Optimization of Cable Stayed Bridges," J. Software Engineering & Applications, vol. 4, pp. 571-578, 2011. [5] P. Guo, X. Wang and Y. Han, "The Enhanced Genetic Algorithms," 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010) , pp. 2990-2994, 2010. [6] G. G. Lai, C.-F. Yang, H. M. Huang and C.-T. Su, "Optimal Connection of Power Transmission Lines With Underground Power Cables to Minimize Magnetic Flux Density Using Genetic Algorithms," vol. 23, no. 3, pp. 1553-1560, 2008.

Table 2 summarizes the mean of 100 trials for all of the 10 floor plans selected by the researchers. IV.

CONCLUSIONS

The experiment results present a concrete evidence to generalize that using genetic algorithm would yield a favourable level of optimization in network cabling and cost optimization. Based on the results gathered after conducting 100 trials on 10 different floor plans, the lowest optimization level of the Network Plan Costs was 57.11%. The highest optimization level of the Network Plan Cost was 99.51%.The overall performance of the network design cost delivered by the system can be described by the mean which is 91.32%. On the other hand, the lowest optimization level of Cable Length recorded was 60.95%. And the highest optimization level of Cable Length recorded was 99.02% with an overall cabling optimization of 90.59%. REFERENCES

[1] C. Frazer, "Structured Cabling Comes of Age," Data Cabling, pp. 33-36, 2002. [2] G.-f. Sun, J.-h. Li, S.-c. Li and Z. Bo, "A Hybrid Genetic Algorithm for Cable Forces

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