(IJCNS) International Journal of Computer and Network Security, Vol. 2, No. 3, March 2010
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A Hierarchical Genetic Algorithm for Topology Optimization and Traffic Management in ATM Networks Utilizing Bandwidth and Throughput Alaa Sheta1, Malik Braik2 and Mohammad Salamah3 1
Taif University, Information Systems Department, College of Computers and Information Systems, Taif, Saudi Arabia
[email protected]
2
Al-Balqa’ Applied University, Information Technology Department, Prince Abdullah Bin Ghazi Faculty of Science and Information Technology, Salt, Jordan
[email protected] 3
National Education Center for Robotics (NECR), King Hussein Foundation, Amman, Jordan
[email protected]
Abstract: The Asynchronous Transfer Mode (ATM) network is expected to become a backbone network for high speed digital multimedia services in distributed environments. This paper explores an optimization based hierarchical approach using Genetic Algorithms (GAs) for installing an ATM network with optimal structure and traffic flow. A Hierarchical Genetic Algorithm (HGA) is used to solve such an optimization problem. HGA approach can improve the performance of conventional GA, though it consumes more computation time. Thus, HGA approach is capable of reducing the overall delay while increasing the bits transmitted over the network. This will definitely improve the network performance and meets the requirements in multimedia service environments. The preliminary results indicate that HGA based ATM network design can be very efficacious.
Keywords: Genetic Algorithms, ATM Network, Flow Management.
1. Introduction There is a rapidly demand for telecommunication networks to function efficaciously despite obstacles such as disabling portions of the network, limiting the links capacities, high cabling costs, and so on [1]. Thus, networks must constantly able to maintain an acceptable level of performance. Despite this need, the problem of dynamic redesign of functioning networks still received a little attention. Today, with the intensifying role of using the Internet and networking technologies, there have been immense advances in using the telecommunications services in the present information society. Asynchronous Transfer Mode (ATM) is one of the most promising networking technologies. ATM was rated to reduce the complexity of the network and improve the flexibility of traffic performance which has good ability for transmitting many kinds of information, and carry traffic over all kinds of networks. Furthermore, ATM has been
increasingly used in telecommunications systems for high speed multimedia services [2], [3]. Recently, control flow and management are core concepts of the optimal structure of ATM networks; hence, flow control and management have drawn much attention by the researchers in telecommunication field. In addition to that the nodes in the ATM network design must be linked in an economical way to handle expected traffic and capacity constraints [1], [4]. As the models for the design of ATM networks are quite complex, and one of the limiting factors is the requirement of expensive exchange based equipments, therefore, Genetic Algorithms (GAs) were used to solve such problems of traffic assignment and topological design of local area networks [5], [6], flow optimization in survivable ATM networks [7], and flow assignment in ATM networks [8]–[10]. However, many programming models have been developed which deals with telecommunication network planning [11]. In this context, Evolutionary Algorithms (EAs) have been frequently applied to telecommunication services in the last years [9], [12]. This paper handles an optimal ATM network structure (i.e. topology and link capacities) using Hierarchical Genetic Algorithms (HGA) [13], [14]. The objectives of the network planning are: designing the network structure to carry out the estimated traffic, and minimizing the cost of network [15],[16]. Consequently, we are dealing with flow assignment and links capacities as complex optimization problems using GAs in two levels, the first GA level deals with selection the optimal network structure, and the second GA level reduces the overall delay while maintain the throughput, this cycle will continues until terminated by some condition. The remainder of the paper is organized as follows. In Section 2 we explain the details of GA levels; each alternative level of HGA is described extensively. The case
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study and the schematic diagram of the ATM network are discussed in section 3. In Section 4 we provide an extensive and comprehensive formulation of the use of HGA for solving the ATM design problem. In section 5 we present our preliminary results for the ATM network case study. Finally, in section 6 we offer several concluding remarks and address the challenges ahead in the future enhancements.
2. Hierarchical GA The ATM design problem was considered as a parameter estimation problem. GAs has been used in a hierarchical approach to optimize the ATM network. The proposed novel HGA optimization problem is shown in Figure 1. There are two levels were performed to produce an optimal ATM network design. The two levels are evolved in parallel. The fitness score of the individuals in each level depends on the performance of the chromosomes in both levels.
2.2 GA Level 2 GA is optimized to find the best value of (fi ) such that the overall delay is minimized. The computed network delay is then returned to GA Level 1 to be considered as the new fittest value. The fitness criterion, in this level, is considered as the mean time delay. The mean delay is computed as in Equation 3. T =
1
N
∑ θ i =1 C i
N fi , θ = ∑ fi i =1 − fi
(3)
where θ is the total traffic over the network, N is the number of links. This idea was presented in [16].
3. Case Study: ATM Network There are two sets of customers to be considered while planning the ATM network design: 1) the user who uses the services through the network. The network should meet the user’s needs in terms of quality of service. The flow assignment and the capacities links are the two main entities affecting the cost of the ATM network. The maximum flow assignment must be tested, and the network should maintain a high capacity. 2) the company which will be building the ATM network and maintaining it. The network operation should be as cost effective as possible for both installation and maintenance. Minimizing the total cost is mainly an important matter of selecting the best design of ATM network. The schematic diagram for the ATM under study with all specified links and capacities is shown in Figure 2.
Figure 1. Hierarchical GAs in two levels 2.1 GA Level 1 GA is used to install a new network with a set of network links which satisfies the constraints demand criterion and has a minimum delay over the network. In this level, a new network structure is evolved which has all nodes of the original network and a subset of its links. The network configuration must be implemented subject to the constraints defined in Equations 1 and 2. f ≤ C × 95% i i
(1)
Thus, the maximum flow value (fi ) for link (i) in Equation 1 is restricted not to exceed 95% of the capacity (Ci ). Throughput ≥ 0.5 ×
n
∑
i =1
C
i
(2)
The throughput of the network is allowed not to be less than half the sum of all capacities. We must realize that the boundary range of the genes values is based on the link ID of the ATM experiments and the gene redundancy must be eliminated. A gene duplicate leads to a link duplicate, which leads to the increase of the network delay.
Figure 2. The ATM Network Topology This ATM network was presented in [17], [18]. The network has 7 nodes with 14 links. The links and capacities for each node are presented in Table 1. Each network link is characterized by a set of attributes; these attributes for a given link (i) form the flow (fi ) and the capacity (Ci ). (fi ) is defined as the effective quantity of information transported by link (i). The capacity (Ci ) is defined as the measure of the maximal quantity of information that can be transmitted by link (i).
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58 Table 1: ATM network capacity of each link Links ID
Start Node
End Node
Link Capacity
1
1
4
150
2
1
6
150
3
1
3
225
4
1
2
225
5
2
3
150
6
2
6
150
7
2
5
150
8
3
4
150
9
3
5
150
10
4
6
150
11
6
5
150
12
5
7
150
13
6
7
150
14
4
7
150
4. Problem Formulation The optimization problem of the ATM network is implemented in a hierarchical manner. Two levels of GAs are used. • GAs Level 1 is used to select the best network topology and forward it to GA Level 2. The ATM network should be effectively connected such that at least half the network bandwidth is busy. • GAs level 2 computes the minimum delay over the network. In GAs level 2 we determine the (fi ) parameters for each network link (i). HGA requires number of steps for the problem formulation as shown below: Encoding Mechanism: This step is used to encode the variables of the optimization problem in terms of genes. In this work, a table was created for all pairs of links combination. The links in the table correspond to the virtual paths included between pair of nodes. In the proposed algorithm each link is identified by an ID number which is in accordance to the row number in the table. The proposed GAs will have 14 genes. Each gene corresponds to a link and each link has an identified (Ci ). Initialization: This step uses the encoding method to create a random initial population by randomly generating a suitable number of chromosomes. In this paper, the chromosome length equals to the number of links in the original network. Number of Populations: Various population sizes were used while running GA in each level. Selection Mechanism: Selection schemes helps in determining the convergence properties of GAs. A selection scheme in GAs is known as the process which favorably selects better individuals in the current population for the mating pool [19], [20]. The selection method determines how individuals are chosen for mating. The first step in the
selection method is the fitness assignment. Each individual in the selection pool receives a reproduction probability depending on its objective value. This fitness is used for the selection step afterwards. Stochastic uniform selection mechanism is considered in this work. Reproduction: This step increases the number of good chromosomes in the next generation. This scheme determines how the new individuals will be assimilated into the population. Many number of reproduction operators were presented in the literature [3], [7]. Crossover: This procedure exchanges genes between the parents. Two chromosomes are randomly selected from the population as parent chromosomes. Two new chromosomes with the genes from both the parent chromosomes are obtained. A scattered function for the crossover is considered in this work. Mutation: This step is used to have a new chromosome which differs from the chromosomes in the population. A chromosome is randomly selected as the mutated chromosome. In this paper, the mutation type is Gaussian function with Scale equal 1 and Shrink equal 1. Fitness Evaluation: The fitness function is computed by using the mean time delay as given in Equation 3. This fitness is based on a given objective function on level 2 which is the minimum time delay over the evolved network in level 1. Each chromosome in the population is assigned a specific value associated with the gene arrangement in order to select the best individuals. Finally, the effectiveness of the proposed algorithm was tested based on the GA utilization. The GA utilization criterion can be computed as given in Equation 4. Utilizatio n =
f i C i
(4)
Improving GA utilization in all cases will increase the flow assignment of the links for the ATM network. HGA requires number of tuning parameters in accordance with GA optimization. We run GA using various population sizes in the two levels of HGA. The corresponding results of the network topology for the various population sizes are shown in Figures 3, 4, 5, 6, 7, 8, 9, 10 and 11.
Figure 3. Network Topology evolved by Pop. Size L1 = 10 and Pop. Size L2 = 25
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Figure 4. Network Topology evolved by Pop. Size L1 = 10 and Pop. Size L2 = 50. Delay time = 11.0223
Figure 8. Network Topology evolved by Pop. Size L1 = 30 and Pop. Size L2 = 75. Delay time = 10.7585
Figure 9. Network Topology evolved by Pop. Size L1 = 50 and Pop. Size L2 = 25. Delay time = 10.76630 Figure 5. Network Topology evolved by Pop. Size L1 = 10 and Pop. Size L2 = 75. Delay time = 10.76282
Figure 10. Network Topology evolved by Pop. Size L1 = 50 and Pop. Size L2 = 50. Delay time = 10.7643
Figure 6. Network Topology evolved by Pop. Size L1 = 30 and Pop. Size L2 = 25. Delay time = 10.8009
Figure 7. Network Topology evolved by Pop. Size L1 = 30 and Pop. Size L2 = 50. Delay time = 10.7581
Figure 11. Network Topology evolved by Pop. Size L1 = 30 and Pop. Size L2 = 75
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60 The HGA related parameters used in the experiments were determined as shown in Table 2. Table 2: Parameters evolved for GA-based experiments Operator
Type
Creation function
random initial with uniform distribution
Initial range
lower bound is 1 and upper bound is 95% of line capacity
Selection mechanism
Stochastic uniform
Crossover type
Scattered function
Mutation type
Gaussian function with Scale = 1 and Shrink = 1
Maximum generations
50
was presented to show the performance of the proposed HGA algorithm. Figure 12 shows the convergence process of GAs with various population sizes for both level 1 and level 2.
5. Experimental Results From the developed results, the proposed HGA was able to provide number of ATM network with various topologies. Having a closer look to each developed structure, we can find that all evolved network have seven links except one network which has 8 links (see Figure 4). This network has the maximum delay which is expected since the number of links increased. The evolved network were able to achieve the required criteria which is specified by the designer (i.e. increasing the network throughput to at least 50% of the network capacity and manage the traffic to be with minimum delay). The developed network could be unreliable in some sense because the reduced number of links may cause a problem if failure occurs in a link. This could be another objective to investigate in the future. Of course the best reliable network will be the fully connected network. This will be, of course, a very expensive network to implement. The computed delay times for various population sizes of the GA in levels 1 and 2 are shown in Table 3.
Table 3: Delay time for various Pop. sizes of GA levels Description
GAs level 1
GAs level 2
Delay time
Pop. Size
10
25
10.81690
Pop. Size
10
50
11.02230
Pop. Size
10
75
10.76282
Pop. Size
30
25
10.80090
Pop. Size
30
50
10.75810
Pop. Size
30
75
10.75850
Pop. Size
50
25
10.76630
Pop. Size
50
50
10.76430
Pop. Size
50
75
10.76006
It was found that the minimum delay was achieved with population size 30 for GA level 1 and 50 for GA level 2. In general, as a common practice, the convergence of the GA
Figure 12. The convergence process of GAs with various population sizes The algorithm converges to the optimal solution at ”high” speed and finds a good solution in less than 20 generations of running. Moreover, the algorithm reached a solution with the lowest (i.e. optimal) delay in most cases. In some cases, the algorithm finds the optimal solution very quickly; however, there are some cases where longer convergence times are observed in order to obtain the optimal solution.
6. Conclusions and Future Work A Hierarchical Genetic Algorithms (HGA) based optimization mechanism for Asynchronous Transfer Mode (ATM) network has been formulated. Minimizing the total cost is mainly the purpose of the proposed approach, subsequently; the work addresses the maximum allowable flow assignment in each link while simultaneously keeping the overall delay within the minimum acceptable range. It can be inferred from the results obtained that ATM network design using HGA produces good network plans in terms of network throughput and GA utilization. Also, it allows more flexibility in the traffic management, and less complexity of the planning task. It is suggested to improve the current work by adding new issues related to dynamic capacity allocation within the network. Additionally, improvement can be achieved if using Parallel Genetic Algorithms (PGAs). This way the computation time can be reduced to a reasonable computational effort.
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[2] D. Raychaudhuri and D. Wilson, “ATM–based transport architecture for multiservices wireless personal communication networks,” IEEE Journal On Selected Areas In Communications, vol. 12, no. 8, pp. 1401–1413, 1994. [3] H. El-Madbouly, “Design and bandwidth allocation of embedded ATM networks using genetic algorithm,” In Proceedings of World Academy of Science, Engineering and Technology, vol. 8, pp. 249–252, 2005. [4] D. W. Coit and A. E. Smith, “Reliability optimization of series-parallel systems using genetic algorithm,” IEEE Transactions on Reliability, vol. 45, pp. 254–260, 1996. [5] Alaa Sheta, Mohammad Salamah and Malik Braik,” Topology Optimization and Traffic Management in Telecommunication Network Using Hierarchical Genetic Algorithms”, In the Proceedings of ICICIS2009, Ain Shams Univ., Cairo, Egypt, pp. 143148, March, 2009. [6] E. lbaum and M. Sidi, “Topological design of localarea networks using genetic algorithms,” In IEEE/ACM Transactions on Networking, vol. 4, pp. 766–778, 1996. [7] K. Walkowiak, “Genetic algorithms for backup virtual path routing in survivable ATM networks,” In Proceedings of MOSIS 99, vol. 2, pp. 123–130, 1999. [8] A. Kasprzak and K. Walkowiak, “Algorithms for flow assignment ATM virtual private networks,” In Proceedings Of First Polish-German Teletraffic Symposium PGTS 2000, vol. 2, pp. 24–26, 2000. [9] E. Alba, “Parallel evolutionary algorithms in telecommunications: Two case studies,” In Proceedings of the CACIC02, Buenos Aires, Argentina, 2002. [10] L. He and N. Mort, “Hybrid genetic algorithms for telecommunications network back-up routing,” BT Technology Journal, vol. 18, no. 4, pp. 42–50, 2000. [11] L. Xian, “Network capacity allocation for traffic with time priorities,” Journal of International Network Management, vol. 13, pp. 411–417, 2003. [12] C. Rose and R. Yates, “Genetic algorithms and call admission to telecommunications networks,” Computers and Operations Research, vol. 23, pp. 9–6, 1996. [13] L. Davis and Coombs, Optimizing networks link sizes with genetic algorithms. Modeling and Simulation Methodology, Knowledge System’s Paradigms, Amsterdam, The Netherland: Elsevier, 1989. [14] S. Pierre and H. H. Hoang, “An artificial intelligence approach for improving computer communication network design,” Journal Of Operational Research Society, vol. 41, no. 5, pp. 405–418, 1990. [15] M. Gerla, J. A. S. Monteiro, and R. Pazos, “Topology design and bandwidth allocation in ATM nets,” IEEE. JSAC, vol. 7, no. 8, pp. 1253–1262, 1989. [16] A. Sheta, M. Salamah, H. Turabieh, and H. Heyasat, “Selection of appropriate traffic of a computer network with fixed topology using GAs,” International Journal of Computer Science, IAENG, vol. 34, no. 1, 2007. [17] S. Routray, A. Sherry, and B. Reddy, “Bandwidth optimization through dynamic routing in ATM
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Authors Profile Alaa Sheta received his B.E., M.Sc. degrees in Electronics and Communication Engineering from Faculty of Engineering, Cairo
University in
1988
and
1994,
respectively. A. Sheta received his Ph.D. degree
from
the
Computer
Science
Department, School of Information Technology and Engineering, George Mason University, Fairfax, VA, USA. His published work exceeds 70 publications between book chapters, journal papers, conferences and invited talks. His research interests include Evolutionary Computation, Computer Networks, Image Processing, Software Reliability and Software Cost Estimation. Currently, Dr. Sheta is a Professor with the College of Computers and Information Systems and the Director of the Vision and Robotics Laboratory at Taif University, Taif, Saudi Arabia. He is on leave from the Electronics Research Institute (ERI), Giza, Egypt.
Malik Braik received his B.Sc. degree in Electrical Engineering from Faculty of Engineering, Jordan University of Science and Technology in 2000. Five years later, M. Braik received his M.Sc in Computer Science from Department of Information Technology, Al-Balqa Applied University, Jordan in 2005. His research interests focus on Evolutionary Computation, Image Processing, Data Security and Computer Networks. Currently, Braik is working with the Department of Information Technology, Prince Abdullah Bin Ghazi Faculty of Science and Information Technology, Al-Balqa Applied University, Al-Salt, Jordan.
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Mohammad Salameh received his B.E. degree in Computer Science from Faculty of King Abdullah II School for Information Technology (KASIT), University Of Jordan in 2005, and in 2009 Mohammad received his M.Sc. degree in Computer Science from Faculty of Graduate Studies and Scientific Research, Al-Balqa` Applied University in 2009. Mohammad Salameh is a joiner researcher; his research interest fields are Robotics Design and Programming, Evolutionary Computation, Computer Networks, Image Processing. Mohammad is a Robotics Trainer and Programmer in the National Education Center for Robotics (NECR), King Hussein Foundation, Amman, Jordan.
