ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010   On Demand Bandwidth Reservation for Real-    Ti...
ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010therefore the model establishes many reservation t...
ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010                                                  ...
ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010                                Genes             ...
ACEEE International Journal on Network Security, Vol 1, No. 2, July 201011) Perform mutation function.                    ...
ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010i.  Available bandwidth: 10, 20, 30, 40, 50 Mbps. ...
ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010   It is observed that when the rate of packet gen...
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On Demand Bandwidth Reservation for Real- Time Traffic in Cellular IP Network Using Evolutionary Techniques

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As real-time traffic requires more attention, it
is given priority over non-real-time traffic in Cellular IP
networks. Bandwidth reservation is often applied to serve
such traffic in order to achieve better Quality of Service
(QoS). Evolutionary Algorithms are quite useful in
solving optimization problems of such nature. This paper
employs Genetic Algorithm (GA) for bandwidth
management in Cellular IP network. It compares the
performance of the model with another model used for
optimizing Connection Dropping Probability (CDP) using
Particle Swarm Optimization (PSO). Both models, GA
based and PSO based, try to minimize the Connection
Dropping Probability for real-time users in the network
by searching the free available bandwidth in the user’s
cell or in the neighbor cells and assigning it to the realtime
users. Alternatively, if the free bandwidth is not
available, the model borrows the bandwidth from nonreal
time-users and gives it to the real-time users.
Experimental results evaluate the performance of the GA
based model. The comparative study between both the
models indicates that GA based model has an edge over
the PSO based one.

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On Demand Bandwidth Reservation for Real- Time Traffic in Cellular IP Network Using Evolutionary Techniques

  1. 1. ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010 On Demand Bandwidth Reservation for Real- Time Traffic in Cellular IP Network Using Evolutionary Techniques M. Anbar, D.P. Vidyarthi School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India Email: mohamm19_scs@mail.jnu.ac.in, dpv@mail.jnu.ac.inAbstract— As real-time traffic requires more attention, it evolutionary algorithm can be summarized as followsis given priority over non-real-time traffic in Cellular IP [2].networks. Bandwidth reservation is often applied to serve Generate a population of individualssuch traffic in order to achieve better Quality of Service Repeat {(QoS). Evolutionary Algorithms are quite useful in Test the individuals according to a fitness functionsolving optimization problems of such nature. This paper Select individuals to reproduceemploys Genetic Algorithm (GA) for bandwidth Produce new variations of selected individualsmanagement in Cellular IP network. It compares the Replace old individuals with old onesperformance of the model with another model used for }optimizing Connection Dropping Probability (CDP) using Until satisfiedParticle Swarm Optimization (PSO). Both models, GAbased and PSO based, try to minimize the Connection There are many Evolutionary Algorithms e.g.Dropping Probability for real-time users in the network Particle Swarm Optimization (PSO), Geneticby searching the free available bandwidth in the user’scell or in the neighbor cells and assigning it to the real- Algorithms (GA), Ant Colony Optimization (ACO) etc.time users. Alternatively, if the free bandwidth is not Evolutionary Algorithms often offer wellavailable, the model borrows the bandwidth from non- approximating solutions to all types of problems. Thereal time-users and gives it to the real-time users. proposed work uses Genetic Algorithms (GA) forExperimental results evaluate the performance of the GA bandwidth management in Cellular IP networks andbased model. The comparative study between both the compares the performance of the model with themodels indicates that GA based model has an edge over performance of the PSO based model, proposed earlierthe PSO based one. [3], with the same objective.Index Terms—Genetic Algorithm, Cellular IP networks, Many algorithms for bandwidth management haveQuality of Service, Connection Completion Probability,Bandwidth Reservation, Particle Swarm Optimization. been proposed in the literature. Adaptive Resource Reservation schemes and bandwidth reservation using I. INTRODUCTION Support Vector Machine and Particle Swarm Optimization have been proposed in [4]. This model is Wireless communications are considered to be the proposed in Cellular networks to avoid the unwillinglymost important development in communication forced termination and waste of limited bandwidth. Thetechnology. Services are getting better through scheme using both Support vector machine and Particlegenerations; though in return, the problems involved Swarm Optimization was applied when handoff trafficare also becoming more complicated. Further, is heavy. Probabilistic Resource Estimation and Semi-providing good Quality of Service (QoS) for users in reservation scheme for bandwidth management haswireless networks imposes more problems. Bandwidth been studied in [5]. In this model, the probability ofis the most important parameter in wireless networks, real usage of resources by the Mobile Host isespecially in Cellular IP networks, that affects the QoS considered using a probabilistic resource estimation[1]. Bandwidth management falls in NP class of the and semi-reservation scheme. This scheme can improveproblems and thus soft computing methods can be connection blocking and connection droppingapplied to find a sub-optimal solution for call drop probabilities. Another approach that uses bandwidthminimization by managing the bandwidth properly. management is Dynamic Grouping Bandwidth Evolutionary Algorithms (EAs) are used for solving Reservation scheme for multimedia wireless networksoptimization problems that creates a big search space. which is based on probabilistic resource estimation [6].These algorithms maintain a population of individuals According to this scheme, when the Mobile Host (MH)(usually randomly generated initially) that evolves requests a new connection flow or it handoffs to a newaccording to the rules of selection, cross-over, mutation cell, it provides some important information e.g. theetc. All individuals are evaluated against a fitness estimated switching time and the estimated stayingfunction. The fittest individuals are more likely to be time etc. The main concern of this model is multimediaselected for reproduction in the next generation. An traffic and QoS guarantee for this type of traffic; 38© 2010 ACEEEDOI: 01.ijns.01.02.08
  2. 2. ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010therefore the model establishes many reservation time problem (e.g. bandwidth reservation problem) can besections called groups according to the mobility transformed to the function optimization problem. Asinformation of Mobile Hosts. This model is used to an algorithm, the main strength of the PSO is its fastreduce the connection blocking and connection convergence. Due to its well organized logic anddropping rates. In [7] a framework for bandwidth procedures the optimal solution for a specific problemmanagement in ATM networks unified with traffic can be attained very fast. PSO shares many commoncontrol has been proposed. The bandwidth required by points with the GA. Both algorithms start with a groupconnections carried in each output port of the ATM of randomly generated populations and both haveswitches is estimated. The estimation process in this fitness function to evaluate the population. Also, bothscheme takes into account both the traffic source update the population (search space) and search for thedeclaration and the connection measurements at the optimal solution with random techniques [11].output ports. In [8] two bandwidth strategies and PSO model is a swarm of individuals calledreservation scheme with fuzzy controller for real-time particles. Particles are initialized with the randomservices has been discussed. The purpose of this model solutions. These particles move through manyis to support multiple types of service with different iterations to search a new and better solution for theQoS requirements in heterogeneous wireless networks. problem. Each particle is represented by the twoThe model presented in [9] allows a packet transfer in factors; one the position, where each particle has athe switch and admits packets depending on the switch specific position and at the beginning initialized by theand network occupancy. Packets are transferred if the initial position (x) and the other factor is the velocityrequired bandwidth is smaller than the bandwidth (v), where each particle moves in the space accordingcurrently available. Otherwise the packets are stored in to this velocity. During the iteration time (t), thea buffer. In [10] an admission–level bandwidth particles update their position, and velocity [11].management scheme consisting of Call Admission PSO simulates the behavior of the bird flocking.Control (CAC) and dynamic pricing is proposed. The Consider the following scenario. A flock of birds ismain aim of this proposed scheme is providing randomly flying searching for the food in an area andmonetary incentives to users to use the wireless also there is only one piece of food in the area beingresources efficiently and rationally. searched. All the birds in the flock have learned that The proposed model, in this work, provides better there is food in this area but none of them know whereQoS by on demand bandwidth reservation using the food is. The best strategy to locate the food is toGenetic Algorithm in Cellular IP network. On-demand follow the nearest bird to the food [12].refers to a service which addresses the cell’s need for In PSO algorithm there are two types of best values:instant and immediate use. The performance of the one is Pbest which is the best position for each particleproposed model has been evaluated by conducting the in the swarm and must be updated depending on theexperimental studies and its comparison with another fitness value for each particle. The second best value ismodel. Gbest which is the global best value for the swarm in The rest of the paper is organized as follows. In the general. This value must be checked, and is exchangednext section, Particle Swarm Optimization (PSO) and by the best Pbest if the Pbest in this iteration is betterGenetic Algorithm (GA) have been briefly discussed. than Gbest for the last iteration.Section 3 elaborates the proposed model. A The pseudo-code of the PSO algorithm is as follows.comparative study, through experiment, has been done PSO ( )between GA-based model and PSO-based model in {Initialize the swarm by giving initial and randomsection 4. Section 5 contains some observations about values to each particle.the experimental results. For each particle do {Calculate the fitness function II. PARTICLE SWARM OPTIMIZATION If the value of the fitness function for each AND GENETIC ALGORITHMS particle at the current position is better than the fitness value at pbest then, set the current As the paper compares the two models, the tools value as the new Pbest.used in both of them have been briefed here. A. Particle Swarm Optimization Choose the particle with the best fitness value Swarm intelligence is an intelligent paradigm based of all the particles as the Gbest.on the behavior of the social insects such as bird flocks,fish school, ant colony etc. in which individual species Update the velocity of each particle aschange its position and velocity depending on its k +1 k k k k kneighbor. Particle Swarm Optimization (PSO) is based on the V j = w.V j + c1.r1.( Pbest j − X j ) + c2.r 2.(Gbest − X j )swarm intelligence. It is a population based tool used to Update the position of each particle asfind a solution to some optimization problems. The 39© 2010 ACEEEDOI: 01.ijns.01.02.08
  3. 3. ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010 Selection: Through selection operation, good produced X j = X j + V j.∆ t k +1 k k offspring (solutions) are selected depending on their } fitness value for producing more offspring. Until the solution converges. Cross-over: In this operation parts of two} chromosomes are swapped to produce new offspring.1In the pseudo code above, Cross-over can be either one site cross-over or multiple k sites cross-over. Cross-over operation is doneVj is the velocity of particle (j) in iteration (k). according to a cross-over probability.Pbest is the best achieved solution for each individual so far. Mutation: In mutation the parent chromosome isGbest is the global best value for the swarm. changed by mutating one gene or more for yielding a kX is the current position of particle (j) in iteration (k). new offspring [15]. Mutation operation is done j according to mutation probability which is often low.w is inertia weight and is varied from 0.9 till 0.4.r1, r2 are random numbers between 0 and 1.c1, c2 are acceleration factors that determine the relative III. PROPOSED MODEL pull for each particle toward Pbest and Gbest The model, proposed in this work, applies GA for and usually c1, c2 = 2. the optimization of the Connection Dropping ∆t is the time step and usually 1. Probability (CDP). The model has been described as B. Genetic Algorithms follows. When a cell in a Cellular IP network requests bandwidth for completing a real-time traffic Genetic Algorithm is computerized search and transaction, the base station performs a GA basedoptimization algorithm based on the mechanics of processing to reserve the available free bandwidth in allnatural genetics and natural selection. In general it is the cells. If it fails in doing so, the cell looks for thenot good to navigate through potentially huge search bandwidth assigned to the non-real time users. If it isspace for optimal solutions. It may incur huge amount not possible in any of the way as above the call isof time. GA is a technique which can be applied in dropped. Other details are as below.many cases to produce sub-optimal results duringreasonable amount of time. GA has many good features A. Assumptionssuch as broad applicability, ease of use, and global Following assumptions have been made in the model.perspective; therefore GA has been applied to various • A Cellular IP network of 50 cells is considered.search and optimization problems in the recent past. • The cells are of hexagonal shape as shown in Fig.Because of its population based approach, GA has also 3.been extended to solve other search and optimization • Bandwidth is distributed among the cellsproblems such as multi-objective and scheduling randomly and is divided into three parts: part I isproblems [13]. Population in GA consists of number of reserved for the users having real-time traffic,individuals and each individual is considered as a part II is reserved for the users having non-real-potential solution for the given problem. The individual time traffic and part III is free bandwidth in thesolution is also called chromosome [14] and consists of cell.many genes, as shown in Fig. 1. The size of • Two typed of users (real-time and non-real-time)chromosome depends on the type of the problem being are distributed randomly among the cells.solved. Data in the chromosome can be either in binary or B. Encoding Usedreal as shown in Fig. 2(a) and 2(b). • Each solution (individual) is represented by a A pseudo-code of the GA is as follows. chromosome. GA ( ) • A chromosome is an array of length nine, as { Create a random population of any size; shown in Fig.4. Data representation in the Evaluate the fitness function for each chromosome is real. The genes in the individual in the population; chromosome are as follows. For number of generations { Gene (0) is number of real-time users. Select parents for reproduction; Gene (1) is number of non-real-time users. Perform crossover (); Gene (2) is number of real-time packets. Perform mutation (); Gene (3) is number of non-real-time packets. Evaluate population; Gene (4) is size of real-time session. } Gene (5) is size of non-real-time session. } Gene (6) is bandwidth assigned for real-time users. Gene (7) is bandwidth assigned for non-real-time users. Some of the functions used in the GA are as follows. Gene (8) is free bandwidth in the cell 40© 2010 ACEEEDOI: 01.ijns.01.02.08
  4. 4. ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010 Genes mutating one gene of the parents’ chromosomes. Mutation probability is 0.4. D. Fitness Function Following symbols are used in the fitness function. Figure 1. Chromosome structure AB r : Available bandwidth for real-time users. S r : Size of real-time session. 1 1 1 0 1 0 0 1 0 P s : Packet size. Figure 2(a). Binary representation of data in the chromosome N p : Number of packets. N r : Number of real-time users. 2 50 24 100 300 500 210 10 2 T s : Time of a session. 0 0 P r : Packet generation rate. CCP : Connection Completion Probability. Figure 2(b). Real representation of data in the chromosome CDP : Connection Dropping Probability. The model tries to minimize the fitness function CDP 33 as shown below: 32 30 29 ABr 31 25 26 CCP = (1) 28 21 Sr 27 24 20 19 23 19 15 CDP = 1 − CCP (2) 22 18 14 16 17 12 9 Available bandwidth for real-time users in the cell is 16 11 8 the amount of bandwidth given to the cell when performing bandwidth distribution module. 10 7 5 6 4 Size of real-time session is calculated as follows. 3 2 1 S r =P s∗N p∗N r (3) Figure 3. Network model used in GA Number of packets generated in a session is: C. Modules Used N p =P r∗T s (4) 1) Bandwidth distribution: this module randomly distributes the bandwidth among all the cells in the Cellular IP network and divides the bandwidth From (1), (2), (3), and (4) the fitness function is into three parts: real-time bandwidth (for real- time users), non-real-time bandwidth (for non- AB r real-time users) and free bandwidth. CDP = 1− (5) P s∗P r ∗T s∗N r 2) User distribution: module for random users’ distribution among the cells and is classified into two types: real-time users and non-real-time E. Algorithm users. 1) Input population size (50 cells). 3) Borrow: this module performs borrowing 2) Input total number of users in the network. bandwidth in the same cell (by borrowing the free 3) Input total amount of bandwidth to be distributed bandwidth in it) or borrowing bandwidth from in the network. another cell (by borrowing free bandwidth or 4) Distribute the users among cells. bandwidth assigned to the non-real-time users 5) Distribute the amount of bandwidth among cells. from another cell). 6) Generate the initial population 4) Cross-over: the cross-over operation is performed 7) Calculate the fitness function for all generated between two chromosomes (two arrays) that chromosomes using (5). generate two offspring from them i.e. two arrays. 8) Perform borrow function for cells that have CDP Cross-over used in this algorithm is a single site less than 0.5. cross-over with probability one. 9) For number of generations repeat the steps from 10 5) Mutation: this operation is used to generate new until 15 offspring that may have better fitness values by 10) Perform crossover function. 41© 2010 ACEEEDOI: 01.ijns.01.02.08
  5. 5. ACEEE International Journal on Network Security, Vol 1, No. 2, July 201011) Perform mutation function. 3) Number of generated real-time packets: 5, 10, 15,12) Check the relevancy of data in the new generated 20, 25, Available bandwidth: 40 Mbps, Number of chromosomes. users in the cell: 4 real-time, 4 non-real-time,13) Calculate the fitness functions for the new Packet generation rate: 30 packets/sec. generated population using (5). Time of the session has been generated randomly.14) Sort the new generated population according to the best fitness value. Changing Connection Dropping Probability with different number of real-time packets15) Select the best chromosome as the chromosome with the best fitness value. 70 Connection Dropping 60 Probability(CDP%)16) Store the results in an output file. 50 5 packets 10 packets17) Consider the new population as the old population 40 15 packets 30 20 packets for the next generation. 20 10 25 packets18) Display the stored results in the output file. 0 1 4 7 10 13 16 19 Generation num ber 0 1 2 3 4 5 6 7 8 Figure 7. Effect of number of real-time packet generated on CDP Figure 4. Chromosome structure used in the proposed model 4) Packet generation rates: 10, 20, 30, 40, 50 IV.SIMULATION EXPERIMENTS packet/sec, Available bandwidth: 40 Mbps, Number of real-time users: 5 (same as in PSO In this section, the performance of the proposed GA- based model).based model is evaluated. The experiment has beenperformed up to 20 generations with the given Time of the session has been generated randomly.parameters. Changing Connection Dropping Probability with1) Available bandwidth: 10, 20, 30, 40, 50 Mbps, changing packet generation rate Number of users in the cell: 4 real-time, 4 non- 90 Connection Dropping real-time; Packet generation rate: 30 packets/sec 80 Probability(CDP%) 70 Prate10 Time of the session has been generated randomly. 60 50 Prate20 Prate30 40 30 Prate40 20 Prate50 Changing Connection Dropping Probability with the 10 available bandwidth 0 13 19 11 15 17 1 3 5 7 9 100 Ge neration num ber Connection Dropping Probability (CDP%) 80 10 Mbps 20 Mbps 60 30 Mbps Figure 8. Effect of packet generation rate on CDP 40 40 Mbps 20 0 50 Mbps 5) Times of sessions in each cell: 2, 4, 6, 8, 10 minutes. 1 4 7 10 13 16 19 Generation num ber Available bandwidth: 40 Mbps, Packet generation rate: 30 packet/sec, Number of real-time users: 5 Figure 5. Effect of available bandwidth on CDP (same as in PSO based model). Time of the session has been generated randomly.2) Number of real-time users in the cell: 5, 6, 7, 8, 9. Available bandwidth: 40 Mbps, Packet generation Changing COnnection Dropping Probability with different values of sessions timerate: 30 packets/sec. Time of the session has been generated randomly. 80 Connection Dropping 70 Probability(CDP%) 2m 60 50 4m Changing Connection Dropping Probability with 40 6m 30 8m different number of real-time users in a cell 20 10 10 m 100 0 Connection Dropping Probability (CDP%) 5 users 1 3 5 7 9 13 19 11 15 17 80 6 users Generation num ber 60 7 users 40 8 users 20 9 users Figure 9. Effect of session’s time on CDP 0 6) Comparison between GA based and PSO based 1 4 7 10 13 16 19 Generation num be r model. The proposed model has been compared with the Figure 6. Effect of number of real-time users in the cell on CDP PSO based model for the following parameters and for the same number of generations/iterations which is 20. 42© 2010 ACEEEDOI: 01.ijns.01.02.08
  6. 6. ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010i. Available bandwidth: 10, 20, 30, 40, 50 Mbps. Number of users in the cell: 4 real-time, 4 non- v. Session times: 2, 4, 6, 8, 10 minutes, Available real-time, Packet generation rate: 30 packet/sec. bandwidth: 40 Mbps, Packet generation rate: 30 Time of each session has been randomly packet/sec, Number of real-time users: 5 (same asgenerated. in PSO based model). Comparison be twe en PSO and GA for Comparison betwee n PSO and GA in sense of available bandwidth session times 30 50 25 40 (CDP%) 20 (CDP%) 30 G.A PSO 15 G.A 20 PSO 10 10 5 0 0 10 20 30 40 50 2 4 6 8 10 available bandw idth (Mb/sec) s e s sion tim e (m inute s ) Figure 10. CDP with changing the bandwidth Figure 14. CDP with changing the time of the sessionii. Number of real-time users in the cell: 5, 6, 7, 8, 9. V. OBSERVATIONS AND CONCLUSIONS Packet generation rate: 30 packets/sec. Available bandwidth: 40 Mbps. All experiments have been conducted in Cellular IP Time of the session has been generated randomly. network that has many parameters affecting QoS such as: number of real-time users in the network, available Comparison betwe en PSO and GA for number bandwidth, packet size, packet generation rate, time of real-time users taken for a complete session. The effect of each of the 30 25 mentioned parameters has been studied in the proposed work. In all the experiments, packets of random sizes 20 (CDP%) G.A 15 PSO 10 5 are generated with maximum size being 100 bytes and 0 5 6 7 8 9 the available bandwidth is taken care of as limited number of real-time users resource. The model is trying to optimize Connection Dropping Probability (CDP) for real-time users using Figure 11. CDP with changing number of real-time users Genetic Algorithm. The model also compares the results obtained with a similar model using Particleiii. Number of real-time packets 5, 10, 15, 20, 25 Swarm Optimization (PSO) algorithm. packets, Available bandwidth: 40 Mbps, Number of users in the cell: 4 real-time, 4 non- It is observed that increasing the available bandwidth real-time, Packet generation rate: 30 packets/sec. leads to decrease in CDP for real-time traffic as is clear Time of the session has been generated randomly. from Fig. 5. Comparing GA based model with PSO based model, in terms of this parameter, shows that Comparison between PSO and GA in sense of number of real-time packets both of them reduces the CDP when the available 30 25 bandwidth is more with the notice that GA is performing better in reducing CDP as obvious from Fig (CDP%) 20 G.A 15 10. PSO 10 5 0 5 10 15 20 25 num ber of real-tim e packe ts When number of real-time users increases, the demand on bandwidth increases; therefore, CDP is Figure 12. CDP with changing the number of real-time packets bigger every time there are more real-time users in a cell as shown in Fig 6. From comparison with PSO iv. Packet generation rate 10, 20, 30, 40, 50, based model, it is clear that CDP is going up when real- Available bandwidth: 40 Mbps, Number of real- time users are increasing in number; though both GA time users: 5 (same as in PSO based model). and PSO are controlling CDP below (0.5) with better Time of the session has been generated randomly. values for CDP in case of GA. This observation is derived from Fig 11. Comparison between PSO and GA in sense of Pa cke t Genera tion Rate 60 When real-time users generate bigger number of packets, the consumed bandwidth is more and it results 50 40 (CDP%) G.A in bigger CDP with fixed amount of bandwidth. Both 30 PSO 20 10 0 10 20 30 40 50 the models (PSO-based and GA-based) are able to Pack et Generation Rate (packe t/s ec) handle this problem easily but the GA based model is performing better as shown in Fig 12. Figure 13. CDP with changing the packet generation rate 43© 2010 ACEEEDOI: 01.ijns.01.02.08
  7. 7. ACEEE International Journal on Network Security, Vol 1, No. 2, July 2010 It is observed that when the rate of packet generation using particle swarm optimization”, Int. J. of Bus. Datais bigger the required amount of bandwidth to complete Comm. and Netw. (IJBDCN), vol. 5, pp 53 - 65, 2009.a call is also bigger as is clear from Fig 8. It has been [4] C.J. Huang, Y.T. Chuang, W. K. Lai, Y.H. Sun, and C.T. Guan, “Adaptive resource reservation schemes forobserved that when real-time users generate more proportional DiffServ enabled fourth-generation mobilepackets, PSO and GA both tolerate the increment in communications system”, Comp. Comm. J., vol. 30, pp.packet generation rate. Though CDP increases in both 1613-1623, 2007.the models, but it is still less than the value that drops a [5] G.S. Kuo, P.C. KO, and M. L. Kuo, “A probabilisticconnection. CDP values obtained from GA model are resource estimation and semi-reservation scheme forless than those which have been obtained from PSO flow-oriented multimedia wireless networks”, IEEEmodel as shown in Fig 13. Wire. Comm. and Netw. Conf., (WCNC 2000), vol. 3, pp. 1046 – 1051, 2000. The effect of session’s time is not less important [6] J.Y. Chang, and H.L. Chen, “Dynamic-Grouping bandwidth reservation scheme for multimedia wirelessthan the effect of the other factors on CDP according to networks”, IEEE J. of Selected Areas in Comm., vol. 21,(5). When the time of a real-time session increases pp. 1566 – 1574, 2003.there is a possibility for generating more packets and [7] Z. Dziong, M. Juda, and L.G. Mason, “A framework forconsuming more bandwidth. GA is performing better bandwidth management in ATM networks – aggregatethan PSO in controlling CDP with the increment in equivalent bandwidth estimation approach”, IEEE/ACMsession’s time as obvious from Fig. 14. Trans. on Netw. vol. 5, pp. 134 – 147, 1997. [8] I.S. Hwang, B.J. Hwang, L.F. Ku, and P. M. Chang, The discussion is concluded stating the reason “Adaptive bandwidth management and reservationbehind the better performance of the GA based model. scheme in heterogeneous wireless networks”, IEEE Int. Conf. on Sensor Netw. Ubiquitous and TrustworthyPSO model does not use crossover operation (i.e. there Computing, 2008. SUTC 08, pp. 338-342, 2008.is no material exchange between particles) that makes [9] A. Hac, “Bandwidth management in the switch withthe particles same without change but they are various traffic Burstiness”, Third IEEE Conf. oninfluenced by their own previous best positions and Telecomm. , pp. 343-347, 1991.best positions in the neighborhood in the global [10] B. Al-Manthari, N. Nasser, N. A. Ali, and H. Hassanein,population. In GA, there is a crossover operation (i.e. “Efficient bandwidth management in broadband wirelessthere is exchange in the material between the access systems using CAC-based dynamic pricing”,individuals in the population) that means there is a 33rd IEEE Conf. on Local Computer Netw., pp. 484- 491, 2008.chance to generate new offspring with better [11] N. Nedjah, and L. de M. Mourelle, Swarm Intelligentspecifications than the parents. GA model is better in Systems, Springer-Verlag Berlin Heidelberg, 2006, pp 3-sense of values obtained in every generation but PSO 57.model is better in sense of time taken for the [12] J.H. Seo, C.H. Im, C.G. Heo, J.K. Kim, H.K. Jung, C.G.convergence. Lee, “Multimodal function optimization based on particle swarm optimization”, IEEE Trans. on Magn. , REFERENCES vol 42, pp. 1095 – 1098, 2006 [13] D.E Goldberg, Genetic Algorithms in search,[1] X. Yang, and G. Feng, “Optimizing admission control optimization, and Machine Learning, Upper Saddle for multi-service wireless networks with bandwidth River, NJ: Pearson, 2005, pp. 1-25. asymmetry between uplink and downlink”, IEEE Trans. [14] L.M.O. Khanbary, D.P. Vidyarthi, “A GA-based Veh Tech., vol. 56, pp. 907 – 917, 2007. effective fault-tolerant model for channel allocation in[2] L. C. Jain, V. Palade, and D. Srinivasan, Advances in mobile computing”, IEEE Trans. on Veh. Tech., vol. 57, Evolutionary Computing for System Design, Springer- pp. 1823-1833, 2008. Verlag Berlin Heidelberg, 2007, pp 1- 139. [15] Z. Michalewicz, Genetic Algorithms + Data Structures[3] M.Anbar, and D.P.Vidyarthi, “On demand bandwidth = Evolutionary Programs, 3rd revised and extended Ed., reservation for real-time users in cellular IP network Springer, Charlotte, 1995, pp. 45-88. 44© 2010 ACEEEDOI: 01.ijns.01.02.08

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