A virtual service placement approach based on improved quantum genetic algorithm *

Transcription

1 Xong et al. / Front Inform Technol Electron Eng (7): Fronters of Informaton Technology & Electronc Engneerng engneerng.cae.cn; ISSN (prnt); ISSN (onlne) E-mal: jzus@zju.edu.cn A vrtual servce placement approach based on mproved quantum genetc algorthm * Gang XIONG 1, Yu-xang HU 1, Le TIAN 2, Ju-long LAN 1, Jun-fe LI 1, Qao ZHOU 1 ( 1 Natonal Dgtal Swtchng System Engneerng & Technologcal Research Center, Zhengzhou , Chna) ( 2 Department of Mathematcs and Computer Scence, Unversty of Antwerp, Antwerp 2020, Belgum) E-mal: xg1226@126.com Receved Nov. 10, 2015; Revson accepted Feb. 16, 2016; Crosschecked June 9, 2016 Abstract: Despte the crtcal role that mddleboxes play n ntroducng new network functonalty, management and nnovaton of them are stll severe challenges for network operators, snce tradtonal mddleboxes based on hardware lack servce flexblty and scalablty. Recently, though new networkng technologes, such as network functon vrtualzaton (NFV) and softwaredefned networkng (SDN), are consdered as very promsng drvers to desgn cost-effcent mddlebox servce archtectures, how to guarantee transmsson effcency has drawn lttle attenton under the condton of addng vrtual servce process for traffc. Therefore, we focus on the servce deployment problem to reduce the transport delay n the network wth a combnaton of NFV and SDN. Frst, a framework s desgned for servce placement decson, and an nteger lnear programmng model s proposed to resolve the servce placement and mnmze the network transport delay. Then a heurstc soluton s desgned based on the mproved quantum genetc algorthm. Expermental results show that our proposed method can calculate automatcally the optmal placement schemes. Our scheme can acheve lower overall transport delay for a network compared wth other schemes and reduce 30% of the average traffc transport delay compared wth the random placement scheme. Key words: Software-defned networkng (SDN), Network functon vrtualzaton, Quantum genetc algorthm, Mddlebox CLC number: TP393 1 Introducton Current networks rely on rch functonaltes, such as mproved crtcal performance (e.g., proxes and load balancers), mproved securty (e.g., frewalls and the ntruson detecton system (IDS)), reduced bandwdth costs (e.g., wde area network (WAN) optmzers), and polcy complance capabltes (e.g., network address translaton (NAT) and content flters), whch are ntroduced by a wde spectrum of specalzed applances or mddleboxes (Carpenter and Brm, * Project supported by the Natonal Basc Research Program (973) of Chna (Nos. 2012CB and 2013CB329104), the Natonal Natural Scence Foundaton of Chna (Nos , , , and ), and the Natonal Hgh-Tech R&D Program (863) of Chna (Nos. 2015AA and 2013AA013505) ORCID: Gang XIONG, Zhejang Unversty and Sprnger-Verlag Berln Hedelberg ). Sherry et al. (2012) showed that the number of mddleboxes s on par wth the number of routers n a network (e.g., an average very-large network holds 2850 layer-3 routers and 1946 mddleboxes). In other words, mddleboxes are a crtcal part of today s networks and t s reasonable to expect that they wll reman so n the foreseeable future (Walfsh et al., 2004; Joseph and Stoca, 2008). Though mddleboxes are nevtably deployed n networks and are playng a crtcal role n ntroducng new network functonalty, t s troublng that current mddlebox archtectures suffer from barrers, such as hgh cost (Anderson et al., 2012; Anwer et al., 2013), lmted flexblty (Rajagopalan et al., 2013), and long development cycles (Sekar et al., 2012). The reason s that today s mddleboxes not only are closed and expensve systems wth few or no hooks and applcaton programmng nterfaces (APIs) for extenson

2 662 Xong et al. / Front Inform Technol Electron Eng (7): or expermentaton, but also are bult on a partcular chosen hardware platform that typcally supports a narrow range of specalzed functons (e.g., IDS). Worse stll, mddleboxes are acqured from ndependent vendors and deployed as standalone devces wth lttle unformty n ther management APIs or cohesveness n how the overall mddleboxes are managed (Greenberg et al., 2005). Gven the problems stated above, how to solve these ssues of mddleboxes has receved a sgnfcant amount of attenton (Hwang et al., 2015). Most recent strateges are bult on two knds of new networkng technologes, namely software-defned networkng (SDN) (McKeown et al., 2008; ONF, 2012; Nunes et al., 2014) and network functon vrtualzaton (NFV) (Chos et al., 2012; L and Chen, 2015). These technologes have emerged amng at cost reducton, network scalablty ncrease, and servce flexblty mprovement wth the strateges of enablng nnovaton n network nodes, e.g., standardzed APIs and software-centrc mplementatons. NFV proposes to run network functons as software nstances on commodty servers or datacenters, whle SDN supports a decomposton of the network nto controland data-plane functons. Therefore, these new concepts are consdered very promsng drvers to desgn cost-effcent mddlebox servce archtectures (de Turck et al., 2015; Matas et al., 2015). Although ntroducng SDN and NFV to the network functon has several advantages as mentoned prevously, t also brngs some challenges for network transmsson effcency (Shen et al., 2015). For example, n the network shown n Fg. 1, an addtonal traffc transport delay s expected, whch requres a thorough plannng of the mddlebox locaton wthn the network. In Fg. 1, two knds of vrtual mddleboxes (VMs),.e., IDS and frewall (FW), operatng on general servers 1 and 2, are placed at network nodes R2 and R4. We assume that traffc 1 (red sold curve), whch requres both the IDS and FW servces, enters the network from border router 1 and exts the network on border router 2, whle that traffc 2 (green dashed curve) needng the FW servce enters the network from border router 2 and exts the network on border router 3. However, wth server 1 supportng only the IDS functon and server 2 performng solely the FW functon, traffc 1 has to traverse the IDS box n R2 and then the FW box n R4, Fg. 1 Motvaton of the servce deployment problem (References to color refer to the onlne verson of ths fgure) and traffc 2 must be steered to the FW box n server 2. Apparently, the placement scheme concernng servce locatons {R2, R4} ncreases the traffc transport delay, compared wth the scheme wth locatons {R2, R5}. Whle routers/swtches process packets at every hop, mddleboxes process only packets of a subset of all the hops. Apparently, f the mddleboxes are deployed randomly or n some remote nodes, the network traffc may be sent on a detour for the mddlebox servces, leadng to a potental ncrease n packet latency and bandwdth consumpton. Therefore, there s stll an orthogonal problem at the network plannng stage,.e., where to place these mddlebox servces so that ths performance penalty s mnmzed. We denote ths problem as the servce placement problem. In ths paper, the mddlebox servce placement problem s dscussed, focusng on how to place servces n the network wth the objectve of mnmzng the average tme t takes for the subscrbers traffc to go through all requred servces. The followng two key contrbutons are made: 1. We formulate the servce placement problem n theory by the nteger lnear programmng model. 2. We propose a heurstc soluton based on the mproved quantum genetc algorthm (QGA), and evaluate the algorthm performance. 2 Related work Much current research focuses on the evoluton of the mddlebox servce model. Generally, two complementary approaches are followed. The frst

3 Xong et al. / Front Inform Technol Electron Eng (7): tackles the hgh buldng captal expendtures (CAPEX) and lmted extensblty by employng a combnaton of NFV and SDN. It allows operators to decouple the dependence from specalzed equpment and operate network functons as vrtualzed software nstances on a standardzed platform nstead. The second tackles the hgh operaton expendtures (OPEX) and lmted flexblty n the servce procedure by SDN controllng routng through the specfed functonal sequence. The man work related to these two approaches s summarzed here. On the one hand, Sherry et al. (2012) proposed a practcal servce framework for outsourcng enterprse mddlebox processng to the shared cloud computng platform (Q et al., 2014). Sekar et al. (2011) nnovated mddlebox deployment wth the software-centrc mddlebox mplementatons runnng on general-purpose hardware platforms managed va open and extensble management APIs. Regardng VMs as frst-class enttes, Gember et al. (2012a) presented a framework for mmedate applcaton deployment over or under the cloud. Furthermore, Gember et al. (2012b) realzed a software-defned mddlebox networkng framework to smplfy the management of complex and dverse functonaltes. In the scenaros of NFV and SDN, Gember et al. (2014) desgned a control plane called OpenNF, whch could provde effcent, coordnated control of both nternal mddlebox state and network forwardng state. On the other hand, Qaz et al. (2013) presented the SIMPLE archtecture, an SDN-based polcy enforcement layer for effcent mddlebox-specfc traffc steerng. Bult upon SDN and the OpenFlow protocol, Zhang et al. (2013) proposed a scalable framework (called StEERING) for dynamc traffc routng through any sequence of mddleboxes. Fayazbakhsh et al. (2014) developed the FlowTags archtecture, whch conssts of SDN controllers and FlowTags-enhanced mddleboxes, to ntegrate mddleboxes nto SDN-capable networks. Gushchn et al. (2015) proposed a soluton for routng traffc n an SDN-enabled dynamc network envronment wth consoldated mddleboxes mplemented usng vrtual machnes. Cheng et al. (2015) used the smulatng annealng algorthm for the combnatonal problem of servce chans, whch could manage network servces n an effcent and scalable way. The above studes ether are based on the assumpton that the servce has been deployed or make only some prelmnary exploraton on the servce problem (Basta et al., 2014; Lange et al., 2015; Mohammadkhan et al., 2015). Few studes have desgned a specfc deployment strategy, whch s our focus n the next secton. 3 Proposed soluton Our goal n ths secton s to address the servce placement problem by combnng the theory of QGA wth the structure of SDN and NFV, whch ncludes today s SDN controller (Gude et al., 2008), Open- Flow swtches, and vrtual network functon components. Our soluton s an optmal mddlebox servce placement polcy maker that decdes a reasonable hgh-level deployment polcy for the network. 3.1 SDN/NFV-based archtecture Fg. 2 gves an overvew of our archtecture, where vrtual mplementatons of mddlebox applcatons are consoldated to run on a general-purpose shared hardware platform, managed n a logcally centralzed manner wth unform APIs for a networkwde vew. Ths SDN and NFV based soluton reduces the cost and development cycles to buld and deploy new mddlebox applcatons. Fg. 2 Overvew of mddlebox deployment usng SDN/NFV As shown n Fg. 2, the components of the archtecture can be classfed nto three knds: (1) the control plane components ncludng the network

4 664 Xong et al. / Front Inform Technol Electron Eng (7): operaton system, decson module, and databases; (2) the data plane components contanng the OpenFlow swtches and VM platforms; and (3) the nterfaces between the control plane and the data plane, such as the OpenFlow protocol. Next, we wll descrbe the roles of the man components and how the proposed soluton can be used n the context of SDN and NFV. The controller s the central admnstrator of the network and plays a core role n our proposed scheme. The controller perodcally collects network state nformaton, ncludng network topology, servce functon descrpton, network resources (e.g., bandwdth and network-wde traffc workload), and stores them n the databases. The substrate of SDN/NFV contans routers/swtches and NFV platforms, whch forward the traffc and provde mddlebox servce. In addton, the APIs are responsble manly for the communcaton tasks between the control- and dataplanes. The procedure of our placement scheme s as follows: Frst, wth the nput databases n the controller and the requrement of placement, the SDN controller runs the servce placement decson module, whch solves an optmzaton problem of mnmzng network-wde transport delay. Second, the results of the decson module are output of the confguraton polcy to gude servce placement operaton by the unform APIs. Fnally, NFV platforms are placed on network nodes wth optmal locatons, whch can provde mddlebox servces for the traffc from the nodes wthout the servce placed (as llustrated by dashed lnes n Fg. 2). 3.2 Integer programmng model for servce placement We formulate the servce deployment problem as an optmzaton problem that ams at mnmzng the transport delay or dstance to be traversed by all subscrbers traffc (Lu et al., 2013). Assume that the network topology s defned as an undrected graph G=(V, E), wth node set V representng swtches and edge set E representng the lnks. For example, n the topology of Fg. 2, graph G s a symmetrc graph wth weghted edges, and each edge s assocated wth a transport delay value d(l) (le). The objectve s to fnd a subset V S of the locatons among all canddates V (1 V S V =N, s the cardnalty of a set), and place the servces n these selected locatons so that the total delay for all the users s mnmzed. The optmzaton problem can be consdered as an nteger lnear programmng (ILP) model, whose feasble soluton defnes a scheme that satsfes our objectves. The problem s formulated as follows: Frst, gven two nodes v and v j (v, v j V, v v j ), the mnmum transport delay between v and v j s calculated by dv (, v) arg mn dl ( ), (1) j pkp( v, vj) lpk where P(v, v j ) denotes the set of paths from node v to node v j, and p k s one path element of set P(v, v j ). Let D=[d(v, v j )] N N (, j=1, 2,, N) denote the shortest path matrx of graph G, and V E (V E V) the egress node set. The lnear optmzaton model s shown as N N E S mn xdv (, v ) (1 x) dv (, v ) 1 1 (2) S s.t. vv, v { vn vnv, xn 1, dvv (, ) argmn dvv (, )}, n j j1,2,..., N, xj 1 v V v v v V E E, { k k, dvv (, ) arg mn dvv (, )}, k j E vjv (3) (4) x {0, 1}, 1,2,..., N, (5) where x {0, 1} (=1, 2,, N) are the varables of the optmzaton (x =0 means that node v s not selected for servce placement; otherwse, v s the locaton of the network servce), v S s the servce node correspondng to node v, and v E s the egress pont correspondng to node v. Expresson (2) defnes the total transport delay, whch s calculated as the sum of the transport delay between the ngress ponts and the servce ponts and the transport delay between the servce ponts and the egress ponts. Constrant (3) means assgnng the servce node wth the mnmum transport delay to v as v S. Constrant (4) ndcates that the egress node wth the mnmum transport delay to v s selected as v E.

5 Xong et al. / Front Inform Technol Electron Eng (7): Soluton based on the mproved quantum genetc algorthm Malossn et al. (2008) and Mohammed et al. (2012) have shown that the quantum genetc algorthm (QGA) has a good performance n dealng wth nteger programmng. In ths study, we extend the basc QGA wth some mprovement methods, such as dynamc rotaton angle mechansm, quantum mutaton, and populaton catastrophe. Then we propose a algorthm (called SP-IQGA) based on the mproved quantum genetc algorthm (IQGA) for the ILP model to obtan the optmal servce placement (SP) Introducton to QGA QGA s based on the concepts of quantum bt and quantum superposton state. The basc unt of nformaton n quantum computaton s the qubt. A qubt s a two-level quantum system wth bass states 0 and 1, whch can be represented by a superposton of the bass states: , (6) where α and β are complex numbers, denotng the probablty ampltudes of the bass states. QGA operates on a populaton composed of multple feasble solutons. Each feasble soluton of the QGA, whch s made up of multple qubts, s the element chromosome of the populaton. If the probablty ampltudes of a qubt are [α, β] T, a chromosome contanng N qubts s descrbed by q1 q2 qn 1 2 N (7) C, 1 2 N where each qubt q (=1, 2,, N) of the chromosome C can be one of the state 0, state 1, and superposton of states 0 and 1, and wll collapse nto a certan state ( 0 or 1 ) n the observaton of chromosome. So, ths operaton endows the QGA wth better populaton dversty than the basc genetc algorthm. In our SP-IQGA algorthm, the th qubt state of chromosome C represents the servce nformaton of node v ;.e., q wth state 1 means that node v s chosen as the servce placement locaton; otherwse, q wth state 0 means that v s not placed n the servce Formulaton of the SP-IQGA algorthm The man steps of the SP-IQGA algorthm can be descrbed as follows: Step 1: acquston of the shortest path matrx D. D s an mportant nput parameter, and contans all mnmum transport delays between any two nodes n graph G. For example, element d(v, v j ) of the th row and jth column n D s the mnmum transport delay value between v and v j, whch can be calculated by usng the Bellman-Ford algorthm. Step 2: ntalzaton of the QGA. At the ntal stage of SP-IQGA, we set the chromosome populaton sze as M and the qubt length of each ndvdual chromosome as N. Denote the tth generaton populaton as Pt () { C1, C2,, C M () t () t () t () }, where C t m (m=1, 2,, M) s as descrbed n Eq. (7). In the ntal search of the algorthm, all states appear wth the same probablty, so we set (0) (0) 1 m m, 1,2,, N, m 1,2, M, 2 and obtan C 1/ 2 1/ 2 1/ 2. 1/ 2 1/ 2 1/ 2 (0) (0) (0) (0) m1 m2 mn m (0) (0) (0) m 1 m2 mn Step 3: measurement of the observaton value of chromosome C. The chromosome observaton s to make each qubt of the chromosome collapse nto a certan state. The measurement method s to generate a random number n range [0, 1] for each qubt. If the random number s less than α 2, the measurement value of the qubt s 0; otherwse, t s 1. After the measurement operaton, C s transformed to the observaton value X C ={x 1, x 2,, x N }, where x (=1, 2,, N) s a bnary varable (0 or 1). Step 4: calculaton of the ftness. The ftness s the metrc ndcatng the qualty of the ndvdual. The hgher the ftness value, the closer the ndvdual to the optmal soluton. For ndvdual X C ={x 1, x 2,, x N }, the ftness functon can be obtaned by

6 666 Xong et al. / Front Inform Technol Electron Eng (7): N N 1 E S Ft( XC) xdv (, v ) (1 x) dv (, v ). 1 1 (8) Step 5: adaptve adjustment strategy for the quantum rotaton gate. In QGA, the populaton can be updated by quantum rotaton gate U(θ). Based on the quantum rotaton gate, the adjustment operaton of the () th qubt n C t s as follows: m m m cos sn m ( ), U m m sn cos (9) m where m and m represent the probablty ampltudes of the th qubt after adjustment. θ denotes the rotaton angle of quantum rotaton gate, defned by θ =s(α, β ) Δθ, (10) where s(α, β ) determnes the drecton of quantum rotaton and Δθ determnes the sze of quantum rotaton. To reduce the nfluence of the rotaton angle on the algorthm convergence rate, an adaptve method s used to adjust θ n ths study. Specfc adjustment polces are shown n Table 1, where δ s a coeffcent related to the convergence rate of the algorthm, and we set t as a varable changng wth the number of teratons: t 0.04π1, T 1 (11) where σ[0, 1] s a constant, t s the current evoluton teraton number, and T s the total number of evoluton teratons. Step 6: quantum varaton and quantum crossover. The performance of QGA can be mproved by quantum varaton and quantum crossover. Quantum varaton can generate new ndvduals to prevent QGA from evolvng nto a local optmal soluton. Durng the varaton, we choose a small proporton of ndvduals from the populaton, appont randomly a varable qubt of chromosomes, and swap the probablty ampltudes α and β of the apponted qubt. On the other hand, quantum crossover can produce more new models to mprove the searchng performance of the algorthm. Our specfc mplementaton process s that all ndvduals n the populaton are ordered Table 1 Adjustment polces for the rotaton angle b s(α, β ) x x Ft(X) Ft(X b ) Δθ α β >0 α β <0 α =0 β =0 0 1 False True δ 1 ±1 ± False δ 1 ±1 ± True δ ±1 1 1 False δ ±1 1 1 True δ ±1 X b s the current optmal soluton, and randomly and then a new populaton s obtaned by cyclcally shftng the th qubt for 1 tmes n all ordered ndvduals. Based on the above descrpton, we nput all algorthm parameters and call the SP-IQGA algorthm to obtan the servce placement scheme. The process flow s shown n Algorthm 1. Upon the controller, the decson module runs the SP-IQGA algorthm and acheves the result X b ={x 1, x 2,, x,, x N }, where x represents the node v beng the servce locaton v S. 4 Performance evaluaton 4.1 Expermental envronment To evaluate the performance, we set up the expermental envronment on a computer wth a 2.67 GHz two-core Intel Core TM 7 CPU and 4 GB RAM. The GT-ITM tool (Zegura et al., 1996) s used for generatng dfferent network topologes, and we mplement the proposed algorthm wth MATLAB. We evaluate the SP-IQGA algorthm for the servce locaton descrbed n Secton 3 usng the test topology from the GT-ITM and the smulated real network traffc from the data center network traffc record. The method for generatng lnks s Waxman, wth parameters α=0.3 and β=0.2. The lnk transport delay n the test topology s measured n mllsecond, and the transport delay of each lnk s randomly dstrbuted n range [1, 100]. In SP-IQGA, we set the populaton sze M=20 and the varaton probablty r= Evaluaton results b x s the th qubt of X b Effectveness of dfferent parameters Wth the ntellgent decson of QGA, SP-IQGA can calculate automatcally the servce node number

7 Xong et al. / Front Inform Technol Electron Eng (7): Algorthm 1 SP-IQGA Input: network topology graph G, populaton sze M, chromosome length N, varaton probablty r, number of evoluton teratons T Output: optmal placement locaton scheme X b 1 Run the Bellman-Ford algorthm for calculatng the shortest path matrx D of G (0) (0) (0) 2 Generate orgnal populaton P(0) { C1, C2,, C M }, and ntalze each chromosome ndvdual 3 Measure P(t) and obtan observaton value O(t)={X 1, X 2,, X M } 4 for all X O(t) do 5 for all x j X do 6 f x j =0 then 7 Query matrx D, and assgn a mnmum transport S delay node v to x j 8 end f 9 end for 10 Calculate the ftness value Ft(X ) 11 end for 12 X b ={X Ft(X )=argmax XO(t) Ft(X)} 13 whle (t<t) 14 Evolve t to t+1 by quantum rotaton gate: t=t+1 15 Perform steps 3 to B(t)={X Ft(X )=argmax XO(t) Ft(X)} 17 f Ft(X b )<Ft(B(t)) then 18 X b =B(t) 19 end f 20 f X b has not changed after N/2 evoluton generatons then 21 Do quantum crossover for P(t) 22 Do quantum varaton wth probablty r for P(t) 23 end f 24 end whle (N S ) under dfferent network topologes. For dfferent network szes (N V ), the optmal servce node ratos (N S /N V ) wth dfferent numbers of egress nodes (N E ) are obtaned by SP-IQGA (Eqs. (2) (5)) (Fg. 3). It s ntutve to fnd that the servce node rato s n range [0.05, 0.25] and ncreases wth N E. The reason s that wth more egress nodes, more network nodes become sutable for the placng servce. Then, under dfferent values of parameter N E and set N V =100, we show the overall transport delay (Eq. (2)) of network traffc varyng wth the teraton number n Fg. 4. SP-IQGA reaches rapdly the convergence state after about 100 teratons and searches toward the optmal soluton by quantum varaton and NE=1 NE=3 NE= Number of network nodes (N V) Fg. 3 Servce node rato wth dfferent network szes Overall delay (ms) NE=1 NE=3 NE= Number of teratons Fg. 4 Overall transport delay wth dfferent number of teratons quantum crossover (.e., sharp changes n the overall delays). Fnally, we test the convergence performance of SP-IQGA by comparson wth the genetc algorthm (GA) and the basc quantum genetc algorthm (QGA). Under the condtons N E =5 and N V =200 or 500, we acqure the smulaton results of three algorthms. Fg. 5 llustrates that our SP-IQGA converges to the stable states faster. Furthermore, the larger the network sze, the more obvous the advantage. Overall delay (ms) Fg. 5 Convergence comparson of dfferent strateges

8 668 Xong et al. / Front Inform Technol Electron Eng (7): Furthermore, we show the tme consumptons of operatng 200 teratons for the three algorthms n Fg. 6. As shown, wth the ncrease n network sze, the computaton tme of the three methods also ncreases rapdly. The consumed tme of SP-IQGA s more than that of QGA, and both are much hgher than that of GA. The reason s that SP-IQGA and QGA execute quantum operaton, quantum varaton, and quantum crossover, whch spend more calculaton tme. In partcular, the dynamc quantum rotaton operaton n the SP-IQGA algorthm further ncreases ts tme consumpton. Table 2 Comparson of algorthms Algorthm Descrpton SP-Random Servce placement based on a random number generator SP-Greedy Servce placement based on a greedy algorthm (Zhang et al., 2013) SP-B+COR Servce placement based on a heurstc method of parttonng flows (Mohammadkhan et al., 2015) SP-Anneal Servce placement based on a smulatng annealng algorthm (Cheng et al., 2015) SP-IQGA Servce placement based on an mproved quantum genetc algorthm proposed n ths study Fg. 6 Comparson of tme cost of dfferent strateges Comparson of dfferent algorthms To verfy the performance of the proposed algorthm, we compare our SP-IQGA wth four other placement strateges. Strategy 1 s based on random placement, denoted as SP-Random; strategy 2 s based on the greedy algorthm, denoted as SP-Greedy (Zhang et al., 2013); strategy 3 s based on a heurstcs, denoted as SP-B+COR (Mohammadkhan et al., 2015); strategy 4 s based on another heurstcs, denoted as SP-Anneal (Cheng et al., 2015). The notatons and descrptons of dfferent algorthms are lsted n Table 2. Fg. 7 shows the smulaton results of the fve strateges when N E =5. The overall transport delay of the optmal deployment scheme obtaned by dfferent polces ncreases wth the ncrease of the network sze. It s easly seen that the transport delay of SP-IQGA s smlar to that of SP-B+COR, and they are both lower than that of the three other strateges under the same network sze. Under the same condton as n Fg. 7, Fg. 8 shows the computatonal tme overhead of the fve algorthms. Because the random strategy does not need to solve the optmzaton problem, ts tme Fg. 7 Comparson of overall transport delay under dfferent network szes overhead s set to zero. The smulaton results demonstrate that the calculaton tme of SP-B+COR s the hghest among the algorthms compared and ts tme consumpton ncreases rapdly wth the ncrease of the network sze. Relatvely speakng, the calculaton tme overhead of our proposed SP-IQGA algorthm s sgnfcantly lower than that of the three other heurstc algorthms, and t s less affected by the network sze. For the network topology wth N V =100 and N E =5, we execute servce placement usng the fve algorthms to obtan ther respectve optmal deployment schemes: X random, X greedy, X anneal, X B+COR, and X IQGA. We smulate 10 types of applcaton traffcs and each applcaton contans 5 data flows. Therefore, a total number of 50 flows are nvestgated. Suppose that every data flow needs to be processed by mddlebox servces, and these servce requests can be satsfed by a servce node. Under the fve servce

9 Xong et al. / Front Inform Technol Electron Eng (7): flow s dstrbuted n [0, 200]. For 50% of the flows, SP-B+COR and SP-IQGA can acheve about 51 and 55 ms transport delays, respectvely, whle SP-Greedy, SP-Anneal, and SP-Random need 63, 67, and 80 ms transport delays, respectvely. Compared wth SP-Random, SP-IQGA can decrease the transport delay by about 30%. Thus, the advantage of SP-IQGA s sgnfcant, and t can be used to effcently solve the servce placement problem. Fg. 8 Comparson of tme cost of dfferent algorthms under dfferent network szes placement schemes, we let all flows transport the network topology and compute the average transport delay of each traffc for dfferent deployment schemes. Fg. 9 shows the average transport delay of data flows n each network applcaton under dfferent placement algorthms. The results show that the average transport delay of SP-Random s the hghest among all schemes. The delays of SP-Greedy and SP-Anneal are smlar and le n the mddle of the algorthms compared. SP-B+COR and SP-IQGA can acheve less transport delay. Fg. 10. Dstrbuton of transport delay of dfferent servce placement strateges 5 Conclusons To solve the servce deployment problem n the NFV and SDN network envronment, we presented a placement approach based on the mproved quantum genetc algorthm. Bult on top of SDN and the ntellgence of QGA, our placement strategy can automatcally obtan the optmal schemes for dfferent network topologes. Smulaton experments showed that sgnfcant latency reducton can be obtaned by our algorthm for placng servces n dfferent network topologes. In future work, we wll use the proposed approach to help construct network functon servce chans. Fg. 9 Comparson of average transport delay under dfferent traffc flows Fg. 10 shows the transport delay cumulatve dstrbuton functon (CDF) for all the flows. CDF can show clearly the transmsson delay dstrbuton of all applcatons. The greater the curvature of a CDF curve, the more concentrated the delay value dstrbuton. As shown, the transport delay of each data Acknowledgements The authors would lke to thank the revewers of Chna Future Network Development and Innovaton Forum 2015 (5th FNF). Ther careful examnaton of the manuscrpt and valuable comments helped us consderably mprove the paper. References Anderson, J.W., Braud, R., Kapoor, R., et al., xomb: extensble open mddleboxes wth commodty servers.

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