The Network flow Motorng System based on Partcle Swarm Optmzed Neural Network Adult Educaton College, Hebe Unversty of Archtecture, Zhangjakou Hebe 075000, Chna Abstract The compatblty of the commercal network flow montorng software s not satsfactory whch can t meet the montorng requrements for some specfc local area network. The network managements are hard to control the flow drecton. The data flow stuaton of each nodes n the network and some abnormal states. The expense of such software s usually very expensve. Ths paper proposes the network flow predcton method based on partcle swarm RBF neural network. Because n RBF neural network, proper output weghts, hdden unt center and the selecton of the wdth affect the performance of RBF neural network greatly, ths paper optmzes the RBF neural network parameters by partcle swarm n order to attan the network flow predcton model based on the partcle swarm RBF neural network. The smulaton measurement verfes the generated flow chart s the same as that generated by MRTG and the system s easy to mplement, stable to work whch can be set as an example for local area network montorng software development. 1. Introducton Keywords: Network Management, Flow Montorng, Data Analyss The flow montorng and analyss are the key chan n the whole network management. The network flow montorng provdes a method to explore the network characterstcs n a practcal envronment. It contans data collecton from network devces, data decodng and data analyss. It collects some ndex data from the network and feedbacks to the montors. All of these data are mportant for network resource dstrbuton, volume plan, servce qualty analyss, error montorng and separaton, and securty management. The montorng system can detect securty threat n a short tme and analyss on tme. It can determne the attack by flow analyss and provde early warnng and quck responses [1]. The network flow montorng s a key part n network and system management whch provdes mportant nformaton for network operaton and mantenance. How to montor flow and restran the abnormal flow n the core devces s a crucal ssue whch needs more attentons. For the development of computer network flow montorng software, the tradtonal method s to use MRTG whch s a network flow balancng software to do further development. MRTG can support the dsplay of the network flow from multple routers [2]. However, there are some constrants to develop MRTG for some local area network flow montorng system wth specal requrements, and the system has a hysteress qualty, low effcency, hgh cost of defects, for montorng results contan a large error, through the montorng data cannot effectvely analyze network flow condtons. The current usually used n wavelet transform, assocaton cluster, the factorzaton method of network traffc montorng method, although wll be able to complete the network flow montor, but along wth the computer technology and network technology development, the network envronment has dversty and random characterstcs. At ths tme the tradtonal network traffc montorng method, can't adapt to the complcated network envronment, can't solve the network to nternal and external nterference factors, vulnerable to these nterference factors, eventually leadng to hnder access network traffc montorng results appears serous dstorton [3~6]. So n order to solve the dsadvantages of tradtonal methods, based on the comprehensve analyss of network flow characterstcs, was proposed based on partcle swarm based on RBF neural network traffc predcton method. Because of RBF neural network, the output of the rght weght, hdden unt center and wdth of the selecton on RBF neural network performance s affected by usng partcle swarm RBF neural network parameters are optmzed to get partcle swarm RBF neural network, network traffc predcton model. Through the smulaton test, ths system a flow chart and MRTG generated flow chart s bascally the same, and the system s smple, relable and can be used as a stable Journal of Convergence Informaton Technology(JCIT) Volume8, Number6,Mar 2013 do:10.4156/jct.vol8.ssue6.40
development of the local area network montorng system software paradgm. 2. The theoretc bass of the system desgn 2.1. data pre-process The network flow data n the expermental data s normalzed to between 0 and 1. Ths value wll be set as base and other value wll be dvded by ths maxmum value n order to ensure all of the values n the rang between 0 and 1. Ths can ncrease the generalzaton capablty of the predcton model and dsplay the model predcton capablty to the maxmum extent. Assumng the dmenson of the nput sample s 5, the tranng set s buld. The constructed RBF neural network model s descrbed as: y( ) x(5 ) f ( x( ), x( 1), x( 4)) (1) 2.2.RBF neural network optmzaton based on partcle swarm 2.2.1. RBF neural network theory Radal Bass Functon (RBF) neural network s a local approachng neural network whch has nput layer, hdden layer and output layer that s same as three-layer BP neural network. Specfcally, the nput layer s nonlnear, hdden layer has radal bass functons and the output layer s lnear. The output s sgnal source whch s hstorcal network flow nput vector; hdden unt amount s determned by command; the output layer s response on the nput modes whch s the hstorcal network flow correspondng output. The RBF neural network structuraton descrbed n fgure 1. Fgure1. RBF neural network structure RBF neural network uses the Radal Bass Functon as the foundaton of the hdden unt to buld hdden layer space [7~10]. The low-dmenson data can be transferred to hgh-dmenson space by hdden layer nput vector transformaton n order to make the nonlnear nseparable problems n low-dmenson separable n hgh-dmenson [11]. Once the center of the functon s determned, the mappng relatonshp between nput and output wll be determned. The RBF neural network n ths paper contans 5 nput nodes, 8 hdden nodes, and 1 output node. The hdden layer s conssted of RBF functons whch can be expressed as: 2 r exp x e 2d 1,2,, 8 (2) and In the equaton, x s nput vector, w s the output weght. e s RBF hdden node center, d s RBF hdden node wdth,
2.2.2. Partcle swarm optmzaton algorthm The partcle swarm optmzaton algorthm s a new optmzaton algorthm based on group ntellgence, whch s derved from the smulaton of the brd predaton behavor. In partcle swarm optmzaton algorthm, each optmal soluton s defned as a partcle, and each partcle corresponds a soluton whch s evaluated by adaptablty [12~13].Each teraton, the partcle wll follow ts own current optmal soluton and the optmal soluton for the swarm, then searchng generaton by generaton untl fnd the optmal solutons. A group of partcles are randomly ntalzed n whch th s defned as X1= (x 1,,x m ), then teraton s used to fnd the optmal solutons. For each tme of teraton, the partcle wll update tself by local extremum and global extremum. The partcle wll update ts speed and poston accordng to the equaton (2) and (3). vj ( t 1) w vj ( t) c1 r1 ( pj ( t) X j ( t)) (3) c2 r2 ( pgj( t) X j ( t)) X j ( t 1) X j ( t) vj ( t 1) (4) In the equaton, w s nerta weght; r 1, r 1 are random numbers between 0 and 1; c 1, c 2 are acceleraton factors; t s evolutonal teraton tmes; p j (t) s local extremum, p gj (t) s global extremum. Table 1.tranng data constructon Input data Correspondng output data x (1), x (2), x (3), x (4), x (5) x (6) x (2), x (3), x (4), x (5), x (6) x (6) x (3), x (4), x (5), x (6), x (7) x (7) x (4), x (5), x (6), x (7), x (8) x (8)... 2.2.3. RBF neural network optmzaton based on partcle swarm In RBF neural network, the proper output weghts, hdden unt center and the selecton of the wdth affect the performance of the RBF neural network greatly. Ths paper apples partcle swarm to optmze the parameters of the RBF neural network n order to attan the PSO-RBF neural network model. The detaled optmzaton procedure s shown n fgure 2.
Fgure2. RBF neural network optmzaton process Step 1: partcle ntalzaton. The output weghts, hdden unt center and wdth can consst of a new partcle. Each partcle represents each combnaton of output weghts, hdden unt center and wdth and randomly generates a new partcle. Step 2: The components of each partcle n the partcle swarm are mapped to the output weghts, hdden unt center and wdth n the neural network n order to buld a neural network. Step 3: evaluate the adaptablty of the partcle. All the partcles n the swarm are evaluated n order to fnd the optmal one. Step 4: compute and update the local extremum and globe extremum. Step 5: the searchng poston and speed of each partcle s updated accordng to the equaton (3) and (4). The speed of the components n each partcle s updated to generate new partcle. Step 6: When the target functon value s less than the gven value, the algorthm wll end and turn to step 2. Step 7: The components of the optmal partcle are mapped to the output weghts, hdden unt center and wdth of the neural network. Step 8: attan PSO-RBF neural network model. 3.The overall desgn and mplement of the computer network flow montorng system 3.1.System development envronment and Software realzaton The system contans devce scan module, data samplng module, data real-tme dsplay module and data analyss module. The software wll scan the specfed devces to retreve the correspondng nformaton of all the physcal ports n the devces and dsplay for selecton. It wll measure the data flow of the selected port. The avalable OID s shown n table 2. Table 2 OID used n the devce scannng module OID Descrpton 1.3.6.1.2.1.2.2.1.1 Port ndex 1.3.6.1.2.1.2.2.1.2 Port number 1.3.6.1.2.1.2.2.1.8 Port operaton states 1.3.6.1.2.1.2.2.1.5 Port max rate 1.3.6.1.2.1.4.20.1.2 Port IP address 1.3.6.1.2.1.31.1.1.1.18 remark
3.2.Flow data samplng module The kernel functon of the software s to read flow data from the network devces and compute. The read data s receved from some port at some tme, whch s accumulatve amount of bytes. Thus, the dfference between the twce data should be computed and convert the byte unt to bt unt. The value s dvded by tme nterval (n seconds) to attan the average flow n the perod of tme. The detaled software mplement should pay attenton on 4 ssues. (1) Determne sample ntervals The less the samplng tme nterval, the more the data can get and the results wll be more accurate. However, t doesn t mean the more less the nterval, the better the results are. Frst, the frequent SNMP read operaton wll affect the network devce performance and generate more network flow. The samplng nterval should depend on actual stuaton, whch s usually 5 mnutes. (2) Select dvsors In order to avod the delay when the network s busy affectng the accuracy of the data, the network devce tme wll be read as well the flow data. The dfference of the flow data twce wll be dvded by the tme dfference. The OID of the devce system tme s 1.3.6.1.2.1.1.3.0. (3) Select proper data types From the defnton of MIB, the type of the counter s Counter whch s four-byte unsgned nteger. The range s 0~4 294 967 295. In C++, the nt type s sgned four-byte nteger. Thus t can t be used to store retreved data, but use unsgned nt type. The length of the nt type data depends on the operaton system and compler. In VC++, unsgned nt or unsgned long type varables should be appled. (4) Clear counter If the counter reaches the maxmum value, t wll recount from 0. If t happened n the montorng, the computed flow data must be negatve, whch should be avoded. When the bandwdth of the lne s small even t s rare, the software should consder and take care of t n hgh-bandwdth stuaton. Table3 s the approxmate tme of the counter countng from 0 to the maxmum value under dfferent bandwdths. Table 3. the relatonshp between bandwdth and counter clearng tme Bandwdth Counter clearng tme (mnutes) 1Mbps 546.1 10Mbps 54.61 100 Mbps 5.46 155 Mbps 3.52 1Gbps 0.55 The soluton s to compare the new data wth pervous data. If the new data s less than the prevous data, t llustrates the counter s cleared and the data should add 4294967295. 3.3. Flow data real tme dsplay module The tme nterval s 12 mnutes and the 35 network flow data s the expermental data n the paper n whch the prevous 27 data s the tranng data and the last 8 data s for test. Frst, all of the data are normalzed; then the partcle s ntalzed and the adaptablty of the partcle s evaluated to select output weghts, hdden unt center and wdth of the optmal neural network; fnally, PSO-RBF neural network model s establshed and the RBF neural network s conssted of 5 nput nodes, 8 hdden nodes and 1 output node. 4.Expermental results Fgure 3s the collected 35 network flow data n ths paper. RBF neural network s appled to compare the proposed algorthm. Fgure 4 s the network predcton results comparson of the proposed algorthm and RBF neural network. From these results, the predcton results of the proposed algorthm s more close to the real results than RBF neural network whch proves the proposed algorthm has better predcton performance. Table 4 and fgure 5 are the predcton error comparson of the proposed
algorthm and RBF neural network. The value 28 ~ 35 n the proposed algorthm s equvalent 0.0170~0.0017; the value 28 ~ 35 n RBF neural network s 0.0178~0.0334. The average predcton precson of the proposed algorthm s 0.0194, whle the precson n RBF neural network s 0.0315. Fgure3. Network flow data Table 4. the predcton errors of proposed algorthm and RBF neural network No. Proposed algorthm RBF neural network 28 0.0170 0.0178 29 0.0234 0.0316 30 0.0311 0.0465 31 0.0239 0.0360 32 0.0043 0.0056 33 0.0332 0.0564 34 0.0205 0.0249 35 0.0017 0.0334 Average 0.0194 0.0315 Fgure4. The flow predcton results of proposed method and RBF neural network
Fgure5. Network flow predcton error comparson of proposed method and RBF neural network Statstcal experment process, wll be able to get ths paper method and RBF neural network traffc predcton method of related performance, descrbed n table5: Table5. the performance of the two methods ndcators proposed method RBF neural network average run tme 203s 620s Montorng accuracy 0.08 0.25 Leakage rate of prson 6% 32% Operaton effcency 92.30% 74.20% The experment results llustrate the network flow predcton performance of the proposed algorthm s better than that of RBF neural network. The average predcton precsons of the model are less than 0.02 whch meets the network flow predcton requrements. Thus, the proposed algorthm has qute good applcaton value n network flow predcton. 5.Conclusons Ths paper proposes the network flow predcton method based on partcle swarm RBF neural network. Because n RBF neural network, proper output weghts, hdden unt center and the selecton of the wdth affect the performance of RBF neural network greatly, ths paper optmzes the RBF neural network parameters by partcle swarm n order to attan the network flow predcton model based on the partcle swarm RBF neural network. The smulaton measurement verfes the generated flow chart s the same as that generated by MRTG and the system s easy to mplement, stable to work whch can be set as an example for local area network montorng software development. 6.Acknowledgment 1.Remote Openng Physcs Vrtual Experment System Development (20121828) 2.Ultra hgh densty magnetc granular flm studes(20101829) 7. References [1] Rongx Zhou, Xn Ma, Shourong L, Jan L, "The Green Suppler Selecton Method For Chemcal
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