Minimize Congestion for Random-Walks in Networks via Local Adaptive Congestion Control

Transcription

1 Journal of Communatons Vol. 11, No. 6, June 2016 Mnmze Congeston for Random-Walks n Networks va Loal Adaptve Congeston Control Yang Lu, Y Shen, and Le Dng College of Informaton Sene and Tehnology, Nanjng Agrultural Unversty, Nanjng , Chna Emal: lu_y1991@yeah.net; {shen_y2000, dng_le_njau}@126.om Abstrat The routng method of random-walks s robust to the hange of network topology. However, ongeston ours easly for random-walks beause pakets usually take a long trp n the networks to arrve at ther destnatons. In ths paper, we propose a method to mnmze the ongeston for random-walks va loal adaptve ongeston ontrol. The method uses lnearpredton to estmate the queue length for nodes and loal adaptve evoluton equatons that enable nodes to selfoordnate ther aeptng probabltes to the nomng pakets. We apply the method to the BA sale-free networks. The results show that the ongeston s delayed remarkably and the phase transton from free-flow to ongeston ours n a smooth way wth the nrease of predton orders. We also nvestgate the dstrbuton of aeptng probablty of nodes as a funton of node degrees, and fnd a herarhal (degree-based) organzaton of the aeptng rates s strongly benefal to avodng the network ongeston. Furthermore, we ompare our method wth several exstng methods on the performane of avodng ongeston on the BA networks and a real example. The results show that our method performs best under the same paket generaton probablty n the networks. Index Terms Random-walks, self-oordnate, adaptve ongeston ontrol, networks I. INTRODUCTION Random walks s an effent tool for desgnng searhng and routng strateges and has attrated a lot of attentons n many real larger systems suh as the Internet [1], the power grd [2], the transportaton networks [3], and so on. Sne these real systems an be effently desrbed by networks and typally show a sale-free dstrbuton [4], we an nvestgate the dynamal propertes of them by usng varous methods nherted from the modern omplex network theores and statstal physs. Congeston an be onsdered as the ase n networks that the load of pakets nreases and auses the falure of nformaton flow. Reently, many lteratures on networks fous on the rtal propertes of the transtons from free-flow to ongeston [5]-[7]. These studes are manly Manusrpt reeved January 13, 2016; revsed June 21, Ths work was supported by the Fundamental Researh Funds for the Central Unverstes under Grant No. Y , the Jangsu Planned Projets for Postdotoral Researh Funds under Grant No B, and College Students Prate and Innovaton Tranng Program n Jangsu Provne under Grant No X. Correspondng author emal: shen_y2000@126.om. do: /jm desgnng effent routng strateges to provde short delvery tmes and avod the ongeston. Apart from the load of nformaton on networks, the network topologes also have great nfluene on the routng strateges [8], [9]. Many routng strateges have been nvestgated and show that the homogeneous networks an bear hgh network load due to the lak of hgh betweenness nodes [10]. However, t s mpossble and of hgh ost to hange the network topologes. Several feasble strateges have been proposed reently, suh as the mplementaton of uttng nformaton flow on the edges between the nodes wth large degrees [11] or the paket-droppng mehansms [12], for avodng ongeston. It has been observed n the applatons of these strateges that the effeny of network transmsson s dereased or the lose probablty of pakets s nreased. Other reasonable methods are to develop better routng strateges for avodng ongeston. These strateges nlude the based-shortest path [13], the effent path [14], the optmal path searh [15], the hybrd routng [16], and so on. Congeston-aware shemes have been employed n some of these strateges and allow the paths to be hanged to bypass the ongested nodes. However, the strateges proposed above are generally from the global and stat perspetve of node behavor that the nodes are unable to loally adjust ther own state to avod ongeston. Sne the optmal routng poly depends strongly on the state of ongeston of the systems, n order to avod ongeston, t s reasonable to enable the nodes to have a loal adaptve strategy to delver pakets. In ths paper, we propose a method to mnmze the ongeston for random-walks va loal adaptve ongeston ontrol. The method uses lnear-predton to estmate the queue length for nodes and loal adaptve evoluton equatons that enable nodes to self-oordnate ther aeptng probabltes to the nomng pakets. We apply our method to the BA sale-free networks. The results show that ongeston s delayed remarkably and the phase transton from free-flow to ongeston ours n a smooth way wth the nrease of predton orders. We also nvestgate the dstrbuton of aeptng probablty of nodes as a funton of node degrees to reveal the ntrns mehansm of our method for mnmzng ongeston, and fnd that a herarhal (degree-based) organzaton of the aeptng rates s strongly benefal to avodng the network ongeston. In the end, we ompare our method wth several exstng 2016 Journal of Communatons 579

2 Journal of Communatons Vol. 11, No. 6, June 2016 methods on the performane of avodng ongeston on the BA networks and a real example. The results show that our method performs best under the same paket generaton probablty n the networks. The rest of ths paper s organzed as follows: In Seton II, we present our method and desrbe n detal the mplementaton of t. In Seton III, we nvestgate the performane of our method on mnmzng ongeston and show the orrelaton between the aeptng probablty and the node degrees. Furthermore, omparsons between our method and other exstng methods on avodng ongeston are also presented. In Seton IV we gve the onlusons of ths paper. II. THE METHOD In ths seton, we frst present the traff model of our method. Then, we present the loal adaptve evoluton equatons for alulatng the aeptng probablty for nodes. Fnally, we show the method of lnear-predton of queue length used n our method for further avodng ongeston. A. The Traff Model In a network, the delvery of pakets between adjaent nodes an be onsdered as a probablst event. For random-walks, the probablty for a paket delvered from node to ts neghbor j s wrtten as p 1 k, where k s the degree of node. The paket wll be removed from the network when t arrves at ts destnaton. A set of dynam equatons an be used to desrbe the queue t length of the nodes q ( 1, 2,, N ) at tme t, where N s the network sze. A paket s generated on node wth probablty p at eah tme step and wth a destnaton hosen at random. In the tme step from t to, node selet one of ts neghbors at random and send a paket to t. The neghbor wll aept the paket wth probablty, alled aeptng probablty. If the paket s aepted, t wll be removed from the queue of node. We assume that a fnte number of pakets an be delvered by a node at eah tme step. For smplty, we set ths number to be1. We present the traff model as follows. At tme t, the number of pakets that arrves at node s t t N ( t n j ) j j j 1 q q A p (1) and the probablty that a paket s delvered from node to a neghbor s N q ( q ) A p (2) t t t out j j j j1 where ( x ) denotes the Heavsde step funton that ( x ) 1 f x 0 and ( x ) 0 otherwse. A j s the matrx element whh s defned as A 1 f nodes and j j j are onneted and A 0 otherwse. The queue of j nodes obeys FIFO (frst n and frst out) rule, and the traff model of node s wrtten as: q q p q q (3) t t t n out B. The Loal Adaptve Evoluton Equatons The ongeston ours f the nomng flow of new pakets s not balaned wth the suessful delvery of old ones. Therefore, n order to avod the ongeston, we should make t operate at the adverse ase of free-flow regme of a network. We allow eah node to be able to hoose ts own aeptng probablty t and attempt to reah an optmal queue length. Based on the dea that homogeneous traff an lead to the mprovement of network apaty, we set the optmal queue length to be 1 k p, where k s the average node degrees and p s the paket generaton probablty. The loal adaptve evoluton equatons t of node s t 1 t 1 exp q ˆ (4) where s the parameter that ontrols the speed of the varaton of the aeptng probablty. C. Lnear-predton of Queue Length for Nodes For further avodng ongeston, t s neessary to know the traff state of nodes n advane. Here we use lnear-predton to predt the traff state of nodes, and ˆ 1 t q s the L -order lnear-predton value of the real q. Aordng to (4), for node, q ˆ 0 wll tend to total aeptng 1, whereas the total rejeton 0. Here ˆ q wll tend to s set to be 0.5 whenever ˆ 1 network flow. Then, we obtan ln 2 / 1 q k p for ahevng homogeneous Based on lnear-predton, k p. q ˆ s wrtten as: L q a q (5) 1 ˆ t k k k 1 t1k where a are the predtng oeffents and q k are the prevous queue length of node. The error of the predton s L L t1 t1 t1 t1k t1k ˆ k k k1 k0 e() t q q q a q a q (6) a s usually set to be 0 1 for avodng trval soluton[14] to mnmze et (). The Yule-Walker equatons an be wrtten as are T Ra [1,0,,0]. The elements of R t tm rj R j, where R m E q q denotes the self-orrelaton of queue length of node. R Is a symmetral matrx and we an use fast Levnson reurson method [17] to alulate a Journal of Communatons 580

3 Journal of Communatons Vol. 11, No. 6, June 2016 networks wth sze N 2000 and average degree k 6. In ths seton, we frst study the phase transton from free-flow to ongeston of our method, and nvestgate the nfluene of predton orders on the performane of our method. Then, we present the dstrbuton of aeptng probablty as a funton of node degrees. Fnally, we ompare our method wth two exstng methods on avodng ongeston on the BA networks and a real example. Fg. 1 shows the experment results. The onventonal random-walks wthout our loal adaptve sheme, whh means 1, s nvestgated for makng omparson. L are nvestgated. For larty, we here plot the value for L 1, 2, 4,8,16. All data ponts are obtaned by averagng over 400 networks and 105 ndependent runs. The order parameter H III. THE EXPERIMENTS A. The BA Testng Network Model The struture of many real networks s far from beng purely random. Qute on the ontrary, they typally show a Sale-Free (SF) dstrbuton [18]. We start the smulatons by adoptng the well-known BA (BarabásAlbert) sale-free network model [19]. Ths model s defned wth two steps as follows: 1) Growth: at the begnnng, there are m0 ntal nodes. At eah tme step, a new node s added nto the network, and ths new node wll onnet to m(m m0 ) dfferent nodes whh are already present n the network. 2) Preferental attahment: node wll lnk to the new node wth probablty P(k ) k / j 1 k j, where k s The paket generaton probablty p Fg. 1. The order parameter H versus p for our method. In our method, the aeptng probablty t an be adjusted dynamally aordng to the traff state of nodes. Ths leads to the re-dstrbuton of the flux of pakets and enhane the apaty of the whole network remarkably. From Fg. 1 we an see that the rtal pont p of our method s mproved greatly ompared wth the onventonal random-walks. We nrease L from 1 to 25. The range s large enough to nvestgate n detal the nfluene of L on the phase transton. Wth the nrease of L, we observe that the rtal pont p nreases and the degree of node. Ths model wll generate a salefree network wth t m0 nodes and mt edges n t steps, and the average degree of the BA network s approxmately 2m. B. The Phase Transton A new paket s generated on a node wth probablty p and wth a randomly hosen destnaton n eah tme step, and the paket wll be removed from the network when t arrves at ts destnaton. Wth the nrease of p, the ongeston wll our nevtablty. We are nterested n where a phase transton takes plaes from free flow to ongeston. For p p, the system s n free-flow state The rtal pont p the transton urve beomes smooth. Moreover, the lmts of p and the transton are ours qukly. and the sum of all the queue length Q(t ) 1 qt N flutuates around a fnte value. In other words, the system an balane the generated and delvered pakets. For p p, the ongeston appears beause Q (t ) nreases wth tme. It s obvously that the maxmal apaty of a system an be effetvely evaluated by the rtal value p. We use the followng order parameter Predton orders L Transton oeffent s (a) H to montor the phase transton and nvestgate p. H (t ) lm t 1 Q(t t ) Q(t ) Np t (7) We an see H s n the range from 0 to 1. When H 0, the system s n the free-flow phase, and s n the ongested phase when H 0. Here we use BA sale-free model [19] to nvestgate our method beause many real networks typally show a Sale-Free (SF) dstrbuton [4]. We generate the BA 2016 Journal of Communatons Predton orders L (b) Fg. 2. The nfluene of predton orders. (a) and (b) are the rtal pont p and s as a funton of L respetvely. 581

4 Journal of Communatons Vol. 11, No. 6, June 2016 C. The Influene of Predton Orders on Congeston We defne a transton oeffent s that measures the range of p from p to the pont where H frstly arrves at 0.8. In ths range, we an see the transton urves show a lnear-lke nrease and lear dsrmnaton. s an be used to desrbe the transton of the urve of H. The BA network s presented n seton 3.1. Fg. 2(a) and 2(b) show the rtal pont p and s as a funton of L, respetvely. We an see from Fg. 2 that p and s nrease qukly wth L when L s small. However, for large L, the two parameters almost stop nreasng. The results show that usng small value of L has already allowed us to obtan obvous mprovement of p and smooth phase transton. D. The Dstrbuton of Aeptng Probablty To have a deeper nsght nto the mrosop onfguratons that allow delayng the onset of ongeston, we show n Fg. 3 the dstrbuton of average aeptng rates k of nodes wth degree k, as a funton of node degrees. We adopt L 8 here sne obvous mprovement of p and smooth phase transtons have been obtaned as shown n Fg. 1. From Fg. 3 we an see the orrelaton between and the node degrees k s lear sne all k the nodes wthn the same degree-lass dsplay smlar aeptng rates. For dfferent values of paket generatng probablty p, we observe that the nodes wth small degrees are self-organzed homogeneously. For the same p, wth the nrease of node degrees, the aeptng rates derease rapdly after the regon of the selforganzaton. Wth the nrease of p, the aeptng rates of the nodes wth small degrees nrease whle the nodes wth large degrees start to lose ther doors progressvely. We an see from Fg. 3 that a herarhal (degree-based) organzaton of the aeptng rates by the system s strongly benefal to avodng the network ongeston. k Average aeptng rates Node degrees k Fg. 3. The dstrbuton of k as a funton of node degrees. E. Compared wth Other Exstng Methods on Avodng Congeston In ths seton, we ompare our method wth three exstng routng strateges, the effent path strategy (EPS) [14], the Optmal Path Strategy (OPS) [15], and the hybrd routng [16], on avodng ongeston. Before omparsons, t s neessary to show the man dea of the three methods brefly. In the EPS [14], for any path from node to node j, as 0 1 n1 P( j) : x, x,, x, xn j, the L s denoted as [14], 1 L(( P j) : ) k( x ) (8) n 0 where denotes a tunable parameter, and k( x ) denotes the degree of node x. The effent routng path between nodes and j orresponds to the route that the sum of L s mnmzed. The EPS [14] depends on evendstrbuted load and fndng a short path between a par of nodes. To aheve ths, pakets should be delvered on the paths that avod the large-degree nodes beause the largedegree nodes are easy n ongested states. The results n referenes [14] show that the performane of mnmzng ongeston depends strongly on. Thus, before usng the EPS, one must determne the optmal at frst. However, t s a heurst proess and s more dffult than fndng the shortest path. In fat, when load on networks s zero or very small, fndng the effent path s equvalent to fndng the shortest path whh s 2 ON ( ) [14]. Wth the nrease of load, more omputaton ost based on the global traff nformaton on routes are requred for obtanng the effent path, and t s manly deded by the network traff and topologes. The OPS [15] s a heurst method whh depends on fndng the maxmum betweenness B for the effent (, ) paths. In a network, the betweenness b od of node s defned as the sum of the probabltes of all paths between soure o and destnaton d that pass through. The total betweenness B s found by summng up the ontrbutons from all par of soures and destnatons. B s the hghest betweenness of any node on the max network. To aheve optmal routng, the hghest betweenness B should be mnmzed. The problem of max fndng the exat optmal routng s mathematally ted to the problem of fndng the mnmal sparsty vertex separator [20], whh has been shown to be an NP hard problem [21]. Ths means that the omputaton of an exat soluton nreases wth the network sze faster than any polynomal. The OPS [15] use a heurst algorthm to fnd near-optmal solutons for the routng problem n polynomal tme. For networks wth N nodes, the 3 2 runnng tme s O( N log N ), where O( N log N ) for one max teraton and requrng ON ( ) teratons [15]. The hybrd routng mehansm [16] s omposed of the shortest path routng and a global routng strategy to mprove the network traff apaty. In the method, the pakets are sent along the shortest paths or by usng soure routng nformaton. Then, a global soure routng strategy s employed as a supplementary routng mehansm. In the routng strategy, the optmal path 2016 Journal of Communatons 582

5 Journal of Communatons Vol. 11, No. 6, June 2016 between any soure node and destnaton s defned as the path wth mnmum sum of queue length of nodes. The ost of fndng the optmal path s smlar to the shortest 2 path whh s ON ( )[16] for a network wth N nodes. We frst make omparson on the BA networks. The BA networks have the average degrees k 6 and the network sze N We show n Fg. 4 the urves of the parameter H for our method (squares), for the EPS[14] (rulartes) the OPS[15] (trangles), and hybrd routng[16], as a funton of the paket generaton rate p. All data ponts are averaged over 400 networks and 10 5 ndependent runs. The order parameter H EPS OPS Hybrd Routng Our Method provdes the marosop Internet topology data kt (ITDK). The orrespondng date fles that desrbe the Internet router-level graph by the adjaeny matrx are avalable on the web of CAIDA. Here, we use the undreted verson of the CAIDA network due to the bdretonal flow of pakets n our method. The CAIDA network has a total of nodes and lnks. For llustraton purpose, we frst show the degree dstrbuton of the network n Fg. 5 where k denotes the node degrees. We an see from Fg. 5 that the CAIDA network has lear sale-free propertes. Most nodes have low degrees and few nodes have very large degrees. We apply our method to the CAIDA network and ompare t wth the EPS [14], the OPS [15], and the hybrd routng [16], on avodng ongeston. In Fg. 6, we show the transton from free-flow to ongeston of the EPS, the OPS, the hybrd routng, and our method. All data ponts are averaged over 400 networks and 10 5 ndependent runs The paket generaton probablty p Fg. 4. Plots of order parameter H versus p for the EPS, the OPS, the hybrd routng, and our method. From Fg. 4, we observe that our method has the smallest value of H among the four methods under the same paket generaton probablty p, whh ndates that our method performs best on avodng ongeston. The reason s that our method s dynam and the optmal paths are self-organzed by nodes wth loal traff nformaton. Furthermore, the loal adaptve sheme used n our method allows the nodes to be able to redstrbute the traff for avodng ongeston effently. Ths s dfferent from the EPS[14], the OPS[15], and the hybrd routng[16], that ther optmal paths are obtaned from the perspetve of stat vew and based on the global traff nformaton The order parameter H 0.1 EPS OPS Hybrd Routng Our Method The paket generaton probablty p Fg. 6. Plots of order parameter H versus p for the EPS, the OPS, the hybrd routng, and our method. We an see from Fg. 6 that the rtal pont p of our method s large that that of the EPS, the OPS, and the hybrd routng. Furthermore, wth the nrease of paket generaton probablty p, we an see that the phase transton of our method s smoother than that the other three exstng methods, whh means that our method s more robust for reoverng from ongested state. Number of nodes k Node degrees k Fg. 5. The degree dstrbuton of the CAIDA network. Next, we make omparsons between our method and the three exstng methods on a large real example wth large network sze and hgh lusterng oeffent. Center for Appled Internet Data Analyss (CAIDA) [22] IV. CONCLUSIONS The routng method of Random-walks has many advantages. For example, t works wthout the knowledge of network topologes n advane and s robust to the hange of network. Therefore, the routng method of Random-walks has been wdely used as an effent tool for desgnng searhng and routng strateges. However, ongeston always appears easly for random-walks beause pakets may take a long trp n the networks to arrve at the destnatons. In ths paper, we propose a method to mnmze the ongeston for random-walks va loal adaptve ongeston ontrol n networks. In the method, we use lnear-predton of queue length to predt the traff state of nodes, and propose a loal adaptve evoluton equatons of aeptng probablty for 2016 Journal of Communatons 583

6 Journal of Communatons Vol. 11, No. 6, June 2016 nodes to dede whether aept or rejet the nomng pakets. In the experments, we frst analyze the rtal value p where a phase transton from free-flow to ongeston takes plaes. The results show that the p s nreased remarkably by our method. Then, we nvestgate the nfluene of predton orders on network ongeston. We observe that, wth the nrease of predton orders L, the value of rtal pont p nreases and the phase transton urve beomes smooth. To show a deeper nsght nto the mrosop onfguratons that allow delayng the onset of ongeston, we study the dstrbuton of the average aeptng probablty as a funton of node degrees. The results show that a herarhal (degree-based) organzaton of the aeptng rates s strongly benefal to avodng the network ongeston. Fnally, we ompare our method wth the EPS [14], the OPS [15], and the hybrd routng [16], on avodng ongeston on the BA networks and a real example. The results show that the rtal pont where ongeston ours for our method s large that that of the three exstng methods. Furthermore, the phase transton of our method s smoother than that of the three exstng methods, whh means that our method s more robust for reoverng from ongested state. REFERENCES [1] P. Franeso, Perolaton-based routng n the Internet, Journal of Systems and Software, vol. 85, no. 11, pp , [2] J. Wang, X. X. Xong, and X. W. Zheng, Determnst random walk: A new preondtoner for power grd analyss, IEEE Transatons on Very Large Sale Integraton (VLSI) Systems, vol. 23, no. 11, pp , [3] X. Zhang, E. Mller-Hooks, and K. Denny, Assessng the role of network topology n transportaton network reslene, Journal of Transport Geography, vol. 46, pp , [4] S. Boalett, V. Latorab, Y. Moreno, M. Chavez, and D. U. Hwang, Complex networks: Struture and dynams, Physs Reports, vol. 424, no. 4, pp , [5] H. W. Wang and C. Yu, Desgn of ongeston ontrol sheme for unertan dsrete network systems, INT J. Comput. Commun, vol. 8, no. 6, pp , [6] Z. Y. Jang, J. F. Ma, and X. Jng, Enhanng traff apaty of sale-free networks by employng hybrd routng strategy, Physa A, vol. 422, no. 8, pp , [7] C. Jao, W. Yang, Y. Xa, and M. Zhu, A fast heurst algorthm for mnmzng ongeston n the MPLS networks, Internatonal Journal of Communatons, Network & System Senes, vol. 7, no. 8, pp , [8] P. Poter and L. J. Qan, Management of ogntve rado ad ho networks usng a ongeston-based metr, Internatonal Journal of Network Management, vol. 23, no. 5, pp , [9] Z. X. Gao, Y. Sh, and S. Z. Chen, Identfyng nfluental nodes for effent routng n opportunst networks, Journal of Communatons, vol. 10, no. 1, pp , [10] R. Gumerà, A. Arenas, A. D. Gulera, and F. Gralt, Dynamal propertes of model ommunaton networks, Phys. Rev. E, vol. 66, no. 2, pp. 1-8, [11] D. D. Martno, L. D. Asta, G. Banon, and M. Marsl, Congeston phenomena on omplex networks, Phys. Rev. E, vol. 79, no. 1, pp. 1-4, [12] W. Huang and T. W. S. Chow, Investgaton of both loal and global topologal ngredents on transport effeny n sale-free networks, Chaos: An Interdsplnary Journal of Nonlnear Sene, vol. 19, no. 4, pp. 1-8, [13] W. B. Du, Z. X. Wu, and K. Q. Ca, Effetve usage of shortest paths promotes transportaton effeny on salefree networks, Physa A, vol. 392, no. 17, pp , [14] G. Yan, T. Zhou, B. Hu, Z. Q. Fu, and B. H. Wang, Effent routng on omplex networks, Phys. Rev. E, vol. 73, no. 4, pp. 1-5, [15] B. Danla, Y. Yu, J. A. Marsh, and K. E. Bassler, Transport optmzaton on omplex networks, Chaos: An Interdsplnary Journal of Nonlnear Sene, vol. 17, no. 2, pp. 1-10, [16] Z. Y. Jang, J. F. Ma, and X. Jng, Enhanng traff apaty of sale-free networks by employng hybrd routng strategy, Physa A, vol. 422, no.5, pp , [17] B. R. Musus, Levnson and Fast Cholesk Algorthms for Toepltz and Almost Toepltz Matres, Boston: MIT RLE, 1988, no [18] M. Small, Y. L, T. Stemler, and K. Judd, Growng optmal sale-free networks va lkelhood, Physal revew. E, vol. 91, no. 4, [19] A. Barabás and R. Albert, Emergene of salng n random networks, Sene, vol. 286, no. 5439, pp , [20] E. Zhang and L. Gao, A novel vertex separator based rut parttonng algorthm, Chnese Journal of Computers, vol. 37, no. 7, pp , [21] T. N. Bu and C. Jones, Fndng good approxmate vertex and edge parttons s NP-hard, Inf. Pro. Lett., vol. 42, no. 153, [22] CAIDA: Center for appled Internet data analyss. [Onlne]. Avalable: Yang Lu was born n Shandong Provne, Chna. He reeved the B.S. degree from Nanjng Agrultural Unversty (NJAU). He s urrently pursung the M.S. degree wth the College of Informaton Sene and Tehnology, NJAU. Hs major researh s omputer network. Y Shen was born n Jangsu Provne, Chna. He s an assoate professor at Nanjng Agrultural Unversty (NJAU). He obtaned the Ph.D. degree n Southeast Unversty. Hs major researh s omputer network. He has publshed more than 30 papers and 10 of them are ndexed by SCI Journal of Communatons 584

7 Journal of Communatons Vol. 11, No. 6, June 2016 Le Dng was born n Anhu Provne, Chna. He reeved the B.S. Degree from Shenyang Unversty of Chemal Tehnology. He s urrently pursung the M.S. degree wth the College of Informaton Sene and Tehnology, NJAU. Hs major researh s omputer network Journal of Communatons 585