Time-Constrained Big Data Transfer for SDN-Enabled Smart City

Transcription

1 Emeging Tends, Issues, and Challenges in Big Data and Its Implementation towad Futue Smat Cities Time-Constained Big Data Tansfe fo SDN-Enabled Smat City Yuanguo Bi, Chuan Lin, Haibo Zhou, Peng Yang, Xuemin (Sheman) Shen, and Hai Zhao The authos popose a novel achitectue to suppot smat city sevices based on SDN technology. They study the time-constained big data tansfe scheduling (TBTS) poblem unde the poposed achitectue, and pesent an intelligent stategy to addess the TBTS issue by utilizing the SDN contolle to conduct dynamic flow contol and multi-path tansfe scheduling. Abstact With advanced ICT, the eve-apid development of infomatization has become an integal pat of smat city sevices in healthcae, tanspotation, enegy, education, business, community life, and so on. A huge amount of data, called big data, is geneated fom vaious souces, and effective analysis and utilization of big data has become a ey facto in the success of smat city sevices. Howeve, in ode to achieve pecise big data analytics and mae eal-time decisions, one of the challenging issues is how to efficiently delive the huge amounts of collected data to the pocessing seves. In this aticle, we fist popose a novel achitectue to suppot smat city sevices based on SDN technology. Then we study the time-constained big data tansfe scheduling (TBTS) poblem unde the poposed achitectue, and pesent an intelligent stategy to addess the TBTS issue by utilizing the SDN contolle to conduct dynamic flow contol and multi-path tansfe scheduling. Simulation esults demonstate that the poposed stategy can efficiently suppot big data tansfe in tems of low tansfe delay and high bandwidth utilization. Intoduction With continuous ubanization, the unplanned and apid gowth of the uban population not only bings massive oppotunities to the economy, but also imposes additional budens on cities in the domains of unemployment, envionment, pollution, taffic congestion, enegy consumption, and so on. These undesiable issues could slow down the pace of ubanization. To addess the issues in apid ubanization, not only should the policies fomulated by the govenment be impoved, but also the quality of the sevices povided by the city should be pomoted, including the fundamental infastuctue, housing, public facilities, healthcae, education, and so on. Recently, an inceasing numbe of cities have tied to addess the sustainability poblem of ubanization by utilizing a lage scale of infomation and communication technologies (ICT) to impove the use expeience of city sevices []. As a esult, it gives ise to a novel paadigm, smat city, that integates all inds of uban ICT enabled sevices into a combined system, such that the city can be smat and is able to povide easy access to diffeent inds of sevices, infomation shaing, eal-time uban monitoing, and so on [2]. As an emeging paadigm, the smat city concept has been attacting consideable attention, and is consideed to be a pomising solution to addess the challenging issues in ubanization. A smat city utilizes ICT such as ubiquitous sensing (e.g., smatphone, camea, senso, RFID, Bluetooth) and heteogeneous netwo infastuctue (the Intenet, wieless senso netwos, cellula netwos, Wi-Fi, etc.), and manages diffeent inds of infomation by utilizing a vaiety of pocessing technologies, such as cloud computing [3], cybe-physical systems, and big data analysis. In a smat city, the sensing devices ae esponsible fo collecting/monitoing eal-time data elated to uban lives, including enegy, education, healthcae, taffic, building, weathe, pollution, and so on. Though netwo infastuctues, the collected data is tansfeed and stoed in distibuted data centes fo futhe analytics, data mining, and decision maing by some pogessive technologies such as cloud computing, which enables smat city sevices to ationally utilize natual esouces, enhance govenance efficiency, and impove the quality of life. Fo example, with the inceasing costs of esouces, it is essential to utilize smat equipment to monito system opeations and identify esouce waste, and by analyzing the collected data opeatos can do easonable esouce planning and distibute esouces. In a smat city, data is explosively geneated fom many souces since a lage numbe of diving technologies, infastuctues, and pocesses have been embedded into eveyday pactice. In paticula, the widespead mobile Intenet, ubiquitous computing, and embedded softwae have tansfomed things to be moe smat and ceated huge amounts of data [4]. In addition, some applications lie social media, pictues and videos, commecial tansactions, advetising, and games have acceleated data geneation. Fo example, some famous social netwo websites (e.g., Faceboo and Twitte) seve billions of page views evey month, stoe a lage numbe of new photos evey month, and manage billions of pieces of contents [5]. In [6], Ziopoulos et al. epoted that 2 50 bytes of data wee stoed in the wold in 2000; Manyia et al. in [7] estimated that entepises would globally stoe moe than 2 60 bytes of new data on dis dives by 200, and data is geneated at a gowth of 40 pecent globally pe yea; Ziopoulos et al. expected data volumes to each 2 70 bytes by The enomous scale of Digital Obect Identifie: 0.09/MCOM Yuanguo Bi, Chuan Lin (coesponding autho), and Hai Zhao ae with Notheasten Univesity; Haibo Zhou is with Naning Univesity; Peng Yang is with Huazhong Univesity of Science and Technology; Xuemin (Sheman) Shen is with the Univesity of Wateloo /7/$ IEEE

2 big data is geneated anywhee, as shown in Fig., and has become an essential pat of smat city. In ode to efficiently utilize the collected big data and conduct eal-time data analytics fo smat city sevices [8], the huge amount of data need to be deliveed to wied seves fo futhe pocessing, which intoduces a few challenging issues. On one hand, the tansmissions of the collected big data ae esouce-consuming, which bings additional budens to the communication systems. On the othe hand, some eal-time smat city sevices (e.g., tanspotation, healthcae, disaste ecovey) equie timely data collections and tansmissions, and outdated data may be useless. As an emeging technology to contol netwo behavios, softwae-defined netwoing (SDN) consists of two communication planes: the physical data plane and the abstacted contol plane. This decoupling of contol and fowading planes allows SDN to be dynamically econfigued [9, 0], and the flexibility of SDN, which is lacing in today s distibuted netwo substate, geatly simplifies esouce management, poviding a feasible solution to suppot quality of sevice (QoS)-awae data tansfe in smat city. In this aticle, we study the time-constained big data tansfe scheduling (TBTS) poblem unde the pesented SDN enabled achitectue, and the main contibutions of this wo ae summaized as follows: We popose a scalable SDN-enabled achitectue that integates vaious smat city components and povides eliable and timely scheduling fo big data tansfe to suppot smat city sevices. In ode to addess the TBTS poblem, we popose a heuistic scheme by solving the issue of maximum multi-commodity flow ove time. The poposed scheme can econfigue the multi-flow tansfe at the optimal time instant, taing the oveall tansfe efficiency into account. Pefomance evaluations of the poposed scheme ae conducted, which demonstate that it not only schedules the multi-flow tansfe efficiently, but also achieves high netwo utilization in addessing the TBTS poblem. The emainde of this aticle is oganized as follows. An SDN-enabled achitectue fo smat city is pesented. We study the TBTS poblem, and popose a heuistic algoithm with dynamic scheduling to addess the TBTS poblem. Simulation esults and evaluation of the poposed algoithm ae pesented. We conclude the aticle and pinpoint futue eseach diections Smat healthcae 2. Smat secuity 3. Smat weathe 4. Smat education 5. Smat gid 6. Smat tanspotation 7. Smat factoy 8. Smat paing 9. Smat suveillance 0. Smat community. Smat enegy 2. Smat business Figue. Smat city sevices. The SDN-Enabled Achitectue fo Smat City In ode to conduct eal-time big data tansfe and povide QoS-awae smat city sevices, the netwo infastuctue ought to possess centalized contol of netwo esouces, such that intelligent scheduling and QoS-awae outing can be achieved. Howeve, that cannot be ealized in taditional IP netwos since centalized management is not suppoted. The emeging SDN technology adopts a centalized seve named contolle to manage netwo esouces [], such that the netwo opeatos ae able to implement efficient algoithms and ules (e.g., QoS outing, taffic engineeing, and secuity). In this section, we popose an SDN-enabled achitectue fo smat city that consists of fou layes (i.e., data laye, netwo laye, contol laye, and application laye) as shown in Fig. 2. Data laye: The data laye is dedicated to collecting infomation fom the physical wold. With the advances of senso technologies (e.g., envionmental monitoing sensos and industial sensos) and smat devices (e.g., weaable devices, smatphones, cameas), the physical wold can be measued o monitoed such that the human is living in an infomation peception wold. Consequently, the data fom diffeent domains (e.g, healthcae, tanspotation, industy) can be collected by ICT equipment as well as the heteogeneous netwo achitectue including shot-ange netwos (body aea netwo, vehicula netwo, etc.) and long-ange cellula netwos. The local netwos may aggegate and pe-pocess the collected data befoe deliveing to the bacbone netwo. Netwo laye: The netwo laye is composed of SDN-enabled switches (e.g., Open vswitch) whee the SDN potocols (e.g., OpenFlow) ae implemented. The sepaated netwo laye is dedicated to tansfe the data collected fom the physical laye, and maes the netwo moe scalable. Fo instance, sensed data with diffeent categoies, pioities, o QoS equiements can be allocated diffeent flow tables, such that scalable management can be achieved. Moeove, the netwo laye povides the switches with vitual tunnels to the contol laye. Contol laye: The contol laye consists of two sub-components: the pocessing unit and the netwo contol unit. The pocessing unit eceives the collected data fom the netwo laye, stoes the data in data centes, and povides data analytics o decisions to smat city sevices in the application laye by utilizing cloud computing technologies. In the netwo contol unit,

3 In the netwo contol unit, the SDN contolle is dedicated to contolling big data tansfe centally including flow contol and device contol. The contolle is able to suppot QoSawae outing, taffic engineeing, esouce allocation, and so on by contolling the SDN-enabled switches in the netwo laye, which maes big data tansfe contollable in a smat city. Smat city applications Netwo applications Application laye Smat enegy Smat healthcae Smat secuity Smat tanspotation Scheduling Recovey QoS-awae Monitoing Secuity Routing Contol laye Data pocessing Cloud seve Cloud stoage Computing cente Netwo contol SDN potocol SDN contolle Flow contol Device contol Data laye Netwo laye SDN-enabled switch Flow table Smat devices Healthcae Cellula phones Tanspotation Enegy and evionment Figue 2. The SDN-enabled achitectue fo smat city. the SDN contolle is dedicated to contolling big data tansfe centally, including flow contol and device contol. The contolle is able to suppot QoS-awae outing, taffic engineeing, esouce allocation, and so on by contolling the SDN-enabled switches in the netwo laye, which maes big data tansfe contollable in a smat city. Application laye: The application laye can povide vaious smat city sevices and netwoing sevices by the pogamming intefaces between the application laye and the contol laye. In paticula, the application laye povides smat components (e.g., smat enegy, smat healthcae, and smat tanspotation) with vaious sevices by equesting the infomation fom the pocessing unit in the contol laye. In addition, the netwo application manages data tansfe with diffeent equiements (QoS, monitoing, outing, etc.) by utilizing the sevice intefaces povided by the SDN contolle. In the pesented SDN-enabled achitectue fo smat city, the QoS-awae big data tansfe can be achieved by centalized scheduling. Specifically, in the following section, we study time-constained big data tansfe scheduling and popose an efficient appoach to addess this issue. Time-Constained Big Data Tansfe Scheduling In a smat city, big data is tansfeed in the fom of flows, and the flows scheduled by the SDN contolle ae tansfeed in the netwo composed of SDN-enabled switches. Unde the SDN-enabled smat city achitectue as shown in Fig. 2, we study a challenging issue: the time-constained big data tansfe scheduling (TBTS) poblem. The TBTS poblem is defined as follows: Assuming thee ae m flows, and flow ( =, 2,, m) with size s is tansfeed fom s to t in a netwo whee multi-path tansfe is allowed duing flow tansfe. The TBTS poblem is to find path set P with feasible aggegate bandwidth and taffic distibution fo flow, such that all the m flows can be tansfeed within the time inteval t. Netwo Model Nomally, a netwo can be modeled as a weighted diected gaph G(V,E), whee V is the set of nodes and e is the set of lins. Fo a lin e E, let b(e) > 0 be the bandwidth, and d(e) be the lin delay, which compises queueing delay, pocessing delay, and popagation delay. Let s be a souce node, t be a destination node, and p = (v 0, v 2,, v l ) be a path with l lins, whee e i = (v i, v i ) p, i [, l] epesents a lin connecting nodes v i and v i. The path delay of p is d(p) = S i [,l] d(e i ), and the bandwidth of p is equal to b(p) = min i [,l] b(e i ). Fo tansfeing a flow with fixed size, the tansfe delay consists of the path delay and the delay inheent to the available bandwidth (a lowe bandwidth esults in a longe tansfe delay). In paticula, the delay inheent to the available bandwidth cannot be omitted in big data tansfe. Thus, the delay fo tansfeing flow with size s ove path p is equal to 46

4 Use Use 2 Use 3 Figue 3. Dynamic scheduling. d(p)+ σ b(p). Remote contolle max{th +th 2 +th 3 } Use 2 Dynamic Scheduling Assuming the path set fo tansfeing each flow is obtained, we popose a dynamic scheduling appoach fo scheduling the multi-flow tansfe, which pefoms as follows: Keep the thoughput of the whole multi-flow tansfe maximum, and flow is uploaded to the tansfeing tunnels until the amount of the flow is equal to s. When flow s tansfe is complete, the multiflow tansfe will be econfigued by feeing up the allocated bandwidth to flow and ecomputing the feasible thoughput fo each uncompleted flow If thee ae m flows to be tansfeed in a netwo, each with thoughput th, =, 2,, m, the optimization poblem of thoughput maximization can be expessed as max {th + th 2 +,, + th m }. () Remote contolle max{th 2 +th 3 } Use 2 Use Use 's flow Use 2's flow Use 3's flow Remote contolle max{th 2 } As mentioned above, the ey to dynamic scheduling is to compute the time peiods t x (s) by addessing the thoughput maximization poblems duing t x such that one flow s tansfe is completed afte t x. Assuming the multiflow tansfe is scheduled based on solving the thoughput maximization poblem, each flow equies minimum time peiod g to compute its tansfe. Thus, t x = g, whee is the uncompleted flow afte S z [, x ]t z. In dynamic scheduling, t can be completed diectly by solving the thoughput maximization poblem. Howeve, the computing of t x (x > ) is complicated since flow s tansfe is associated with a diffeent bandwidth. This means that if n flows ae completed afte t x, the othe m n flows will shae the netwo esouces. This will esult in a new thoughput optimization poblem fo computing the next t x+. In addition, the esidual flow in the tansfe tunnels needs to be computed fo the uncompleted flows afte t x, since the esidual flow is pat of the flow and affects the minimum peiod computation. We addess the esidual flow issues in the following subsection. The application laye can povide vaious smat city sevices and netwoing sevices by the pogamming intefaces between the application laye and the contol laye. In paticula, the application laye povides smat components with vaious sevices by equesting fo the infomation fom the pocessing unit in the contol laye. In this aticle, we solve the multi-flow scheduling poblem by tansfoming it into a maximum multi-commodity flow ove tme (MMFT) poblem [2]. The linea pogamming (LP) fomulation of the MMFT poblem fo m flows (denoted by MMFT m ) can be expessed as max τ f (P ) (d( u,v ) f (u,v )) [,m] (u,v ) P (2) whee f(u, v ) is the bandwidth fo a lin e = (u, v ), e p i, p i P, and f(p ) is the aggegate bandwidth fo flow s tansfe. Equation 2 is subect to flow consevation and capacity limit, which can be found in [2]. The optimal fomulation in Eq. 2 is dedicated to maximizing the total amount of data that can be eceived at all destinations in time peiod t. As shown in Fig. 3, afte computing the maximum thoughput fo the multi-flow tansfe (use, use 2, use 3, etc.), use s flow is completed afte time peiod t. By ecomputing the maximum thoughput fo uses 2 and 3, only use 2 is left afte anothe time peiod t 2. Afte anothe time peiod t 3, the multi-flow tansfe is completed. Hence, the multi-flow tansfe is completed afte T total = t + t 2 + t 3. Computation of Residual Flow A esidual flow tx is a sub-flow of flow that is in tansit on the paths fom the souce to the destination duing time peiod t x. Fo instance, thee ae x inds of esidual flow fo flow as shown on the left of Fig. 4, whee t is the esidual flow uploaded by the souce node duing t, t2 is the esidual flow uploaded by the souce node duing t 2,, and t x is the esidual flow uploaded by the souce node duing t x. Fo each flow, each ind of esidual flow is associated with diffeent aggegate bandwidth. Moeove, ta is befoe tb tempoally fo any a < b. Computing the amount of tz and the esidual amount of tx (z [, x ]) afte t x will affect the switching peiod computation, namely t x, x =, 2,, m. Fo instance, in the left pat of Fig. 4, the destination eceives no flow fom the souce befoe S z [,x ] t z. This also implies thee ae x completed flows. Assume flow s tansfe is not completed afte t x since the (x ) th flow is completed. Thee ae thee geneal cases fo computing flow s esidual flow: Case fo computing esidual flow: In the fist case, all the esidual flows geneated befoe t x each the destination duing t x. This implies 47

5 s x 2 Residual flows Case Case 3 Case 2 Figue 4. The computation of esidual flow. t s s s (esidual) x New flow duing x x t Pat of that the destination has aleady eceived the newly uploaded flow. The amount of tx can be computed as follows: Compute the aggegate bandwidth fo scheduling the new flow duing t x by addessing the thoughput maximization poblem. The amount of tx is then equal to the amount of the new uploaded flow, subtacting the amount eceived at the destination. Case 2 fo computing esidual flow: Fo the second case shown in Fig. 4, afte anothe t x, the destination eceives pat of t ( < < x ) (we efe to it as the intemediate flow). This implies that the esidual flows befoe the intemediate flow tempoally each the destination, and the esidual flows afte the intemediate flow tempoally still stay in the tunnels. Refeing to the fist case, the amount of tx is equal to the amount of the flow uploaded to the tunnels duing t x, and the esidual amount of t is then equal to the x (esidual) x x 2 t None : Compute the available bandwidth and delay of each lin in the netwo. 2: Let. 3: while < max do 4: P Compute the th-shotest path fo flow, =, 2,, m. 5: Execute dynamic scheduling by addessing MMFT poblem, to compute time Ttotal. 6: if T total t then 7: Output the path sets with feasible multi-flow scheduling. 8: Bea. 9: else 0: + : : end if 2: end while Algoithm. TBTS-H. t amount of the flow uploaded duing t, subtacting the amount eceived at the destination. Case 3 fo computing esidual flow: In the thid case, since the destination still eceives no flow afte t x, each esidual flow is delayed in the tunnels, and the size of each esidual flow is equal to the amount uploaded duing each time peiod. Thus, the details of the esidual flow can be computed accoding to the case of the esidual flow computation. To addess the intelligent computing issue, the details of the esidual flow can be ecoded by utilizing an auxiliay matix such as that in [3]. Computation of the Minimum Time Peiod Futhemoe, the time fo deliveing esidual flow should be taen into account when computing the minimum time peiod g fo an uncompleted flow in ode to compute t x (x > ). Assuming thee ae at most x inds of esidual flows geneated duing diffeent t z (z =, 2,, x ), the computation of g can be divided into two geneal cases, as shown in Fig. 5. Case fo computing g : In the fist case, the entie amount of flow is uploaded to the tunnels. Namely, thee is t such that the tansfe can be completed afte t eaches the destination. If the tansfe stats at time 0, the minimum equied time fo t to each the destination is equal to g * which could be computed by addessing a new thoughput maximization poblem. Theefoe, the minimum time peiod is equal to g subtacting the time that has passed. Case 2 fo computing g : Fo the second case, thee is emaining flow that has not been uploaded at the souce. By addessing a new thoughput maximization poblem, the minimum time peiod could be computed by computing the minimum equied time fo the emaining flow to each the destination. Since the dynamic scheduling assumes that the feasible path set P is given ( =, 2,, m), in the following subsection, a heuistic algoithm named TBTS-H is pesented fo finding such path sets. The Poposed TBTS-H Algoithm We popose a heuistic appoach named TBTS-H as shown in Algoithm, to addess the TBTS poblem. Fistly, TBTS-H computes the states of the netwo including the available bandwidth, lin-delay, etc., by utilizing the monitoing ability of the SDN-enabled achitectue. TBTS-H adds the shotest paths (fo each flow tansfe) into the path sets, and conducts the dynamic scheduling to compute T total iteatively until the time limit of the big data tansfe is met. Simulation Results In ode to demonstate that ou poposed TBTS-H can achieve high netwo utilization and lowe tansfe delay, we compae T total among TBTS-H, x-tbts (x-tbts, x =, 2,, m ), and equal TBTS (E-TBTS). Specifically, x-tbts is the scheduling that only allows x times of econfiguation. E-TBTS allows econfiguation, but the bandwidth of each lin e will be shaed equally if thee ae flows a and b competing fo lin e. We conduct seveal simulations on the Edős- Rényi (ER) netwo (30 nodes with the lin-existence pobability of log ) [4], the 48

6 GÉANT netwo in [5], and the USANet netwo (40 nodes with 58 lins). Fo each netwo, the lin delay d(e) is unifomly distibuted between 5 and 0 units, and the lin bandwidth b(e) is unifomly distibuted between 0 and 5 units. Fo ER topology, we epeat geneating 00 diffeent topologies, and epeat geneating the same 00 GÉANT and USANet topologies with diffeent (bandwidth, delay) weights. Fo each topology, we conduct multi-flow tansfe with five and six flows, espectively, within time t = 300 units. Fo the flows we select, thee ae at least two flows a, b and a shaed lin e; e P a and e P b (P a and P b ae obtained by executing TBTS-H). Othewise, the flows ae eselected. The size of each flow is distibuted andomly between 000 and 500 units. The simulations ae conducted as follows: Fist, we use TBTS-H to compute the path set and T total (TBTS H). Based on the path set obtained in step, we use E TBTS and TBTS, 2 TBTS,, (m ) TBTS, espectively, to compute T total (E TBTS) and T total ( TBTS), T total (2 TBTS),, T total ((m ) TBTS) if thee ae m flow tansfes. We use scoe(y) to epesent the scoe fo algoithm Y. Fo each topology, scoe(y) scoe(y) + if Y meets T total (Y) = min{ttotal(tbts H), T total (E TBTS), T total ( TBTS), T total (2 TBTS),, T total ((m ) TBTS)}. As shown in Fig. 6, we can obseve that TBTS-H achieves the highest scoe. This is due to the fact that TBTS-H adusts the multi-flow taffic dynamically and is able to mae full use of each potential path and the capacity povided by the netwo. The x-tbts algoithms can scoe since sometimes the uncompleted flows cannot obtain lage aggegate bandwidth even if the esouce (e.g., available path) of the completed flow is feed. Moeove, x-tbts can achieve highe scoe as x inceases, which demonstates that the highe the numbe of the econfiguation is, the highe the netwo utilization achieved. Although the E-TBTS can sometimes scoe, ou TBTS-H can get 80 pecent of scoes. This means that ou poposed algoithm TBTS-H is efficient when the multi-flow tansfes ae conducted. Conclusions and Futue Reseach Diections In this aticle, we have investigated the ealtime big data tansfe issue in smat city sevices. We fist popose a scalable SDN-enabled achitectue that integates vaious smat city components and povides eliable and timely scheduling fo big data tansfe to enable smat city sevices. We then study the TBTS poblem unde the pesented achitectue, and popose a heuistic with an intelligent scheme that can maximize the thoughput and schedule the multiflow tansfe dynamically. Simulation esults have demonstated that ou algoithm can schedule the multi-flow tansfe dynamically and can achieve high netwo utilization. Possible futue eseach diections deived fom this aticle ae summaized as follows. QoS Suppot: In a smat city, vaious inds of data may be geneated fom diffeent souces, s s Case afte Figue 5. The computation of g. and ae tansfeed with divese QoS equiements. In this aticle, we study the data tansfeing scenaio whee only the delay constaint is taen into account. Thus, one open issue extended fom ou wo could be consideing the multiflow tansfeing scenaio whee data tansfes ae conducted with multiple QoS equiements o diffeent pioities. Howeve, to guaantee efficient big data tansfe scheduling, designing a scalable southbound application pogamming inteface to povide vaious QoS suppots to big data flows is a challenging issue unde the SDN enabled smat city achitectue. Contol Intelligence: Fequent data analysis and tansfe scheduling ae indispensable to suppot smat city sevices, binging additional buden to computing components. Compaed to the emegent appoaches that could be utilized in contol laye (e.g., distibuted computation and data mining), it is moe efficient and flexible if some lightweight actions could be detemined at the data laye. Fo instance, some flows without QoS equiements, such as, best effot flow, could be scheduled diectly at the fowading table. The challenges ae designing available aning stategies and an intelligent fowading table. Netwo Heteogeneity: Inteconnecting heteogeneous netwos in a smat city, especially when the netwos ae within a centalized contol laye, bings seveal issues and challenges (e.g., netwo compatibility and netwo contollability). Designing efficient gateway potocols that integate SDN featues and bidge SDN with othe types of netwos is indispensable. Futhemoe, developing a feasible hieachy management model fo contolling the netwos may lead to a bette contol laye fo smat cities. Fo instance, each ind of netwo is within a sub-contol laye, while all the sub-contol layes can be managed by a centalized contol laye. Acnowledgment This wo was suppoted in pat by the Natual Sciences and Engineeing Reseach Council (NSERC), Canada, and by the National Natual Science Foundation of China (66742 and ). t t s s x afte New uploaded flow duing x Case 2 t t 49

7 Refeences [] E. Al Nuaimi et al., Applications of Big Data to Smat Cities, J. Intenet Sevices and Applications, vol. 6, no., pp. 5, 205. [2] H. Zhou et al., Towad Multi-Radio Vehicula Data Piping fo Dynamic DSRC/TVWS Spectum Shaing, IEEE JSAC, vol. 34, no. 0, 206, pp [3] G. Han et al., An Efficient Vitual Machine Consolidation Scheme fo Multimedia Cloud Computing, Sensos, vol. 6, no. 2, 206, pp. 7. [4] H. Zhou et al., A Coopeative Matching Appoach fo Resouce Management in Dynamic Spectum Access Netwos, IEEE Tans. Wieless Commun., vol. 3, no. 2, 204, pp [5] S. Sagioglu and D. Sinanc, Big Data: A Review, IEEE Int l. Conf. Collaboation Technologies and Systems, 203, pp [6] P. C. Ziopoulos et al., Undestanding Big Data, McGaw- Hill, 202. [7] J. Manyia et al., Big data: The Next Fontie fo Innovation, Competition, and Poductivity, McKinsey Global Institute, 20. [8] R. Kitchin, The Real-Time City? Big Data and Smat Ubanism, Geo-Jounal, vol. 79, no., 204, pp. 4. [9] Q. Zhou et al., Netwo Vitualization and Resouce Desciption in Softwae-Defined Wieless Netwos, IEEE Commun. Mag., vol. 53, no., Nov. 205, pp [0] T. Chen et al., Softwae Defined Mobile Netwos: Concept, Suvey, and Reseach Diections, IEEE Commun. Mag., vol. 53, no., Nov. 205, pp [] N. Zhang et al., Softwae Defined Space-Ai-Gound Integated Vehicula Netwos: Challenges and Solutions, IEEE Commun. Mag., vol. 55, no. 7, July 207, pp [2] A. Hall, S. Hipple, and M. Sutella, Multicommodity Flows ove Time: Efficient Algoithms and Complexity, Theoetical Compute Science, vol. 379, no. 3, 2007, pp [3] C. Silva, R. Duate, and A. Tofino, Featue Extaction Impovements Using an LMI Appoach and Riemannian Geomety Tools: An Application to BCI, IEEE Conf. Contol Applications, 206, pp [4] P. Edös and A. Rényi, On Random Gaphs I, Publicationes Mathematicae (Debecen), vol. 6, 959, pp [5] GÉANT, Pan-Euopean Reseach and Education Netwo; geant.net/about/pages/home.aspx, accessed Dec. 8, 206. Biogaphies Yuanguo Bi (biyuanguo@mail.neu.edu.cn) eceived his Ph.D. degee fom Notheasten Univesity, Shenyang, China, in 200. He oined the School of Compute Science and Engineeing, Notheasten Univesity, China, as an associate pofesso in 200. His cuent eseach inteests focus on medium access contol, QoS outing, multihop boadcast, mobility management in vehicula netwos, as well as SDN enabled vehicula netwos. Chuan Lin (chuanlin988@gmail.com) is cuently a Ph.D. candidate with the School of Compute Science and Engineeing, Notheasten Univesity. He eceived the B.S. degee in compute science and technology fom Liaoning Univesity, Shenyang, China in 20 and his M.S. degee in compute science and Technology fom Notheasten Univesity in 203. His eseach inteests include netwo pefomance analysis, QoS outing, softwae defined netwoing, and netwo modeling. Haibo Zhou (haibozhouuw@gmail.com) eceived his Ph.D. degee in infomation and communication engineeing fom Shanghai Jiao Tong Univesity, China, in 204. Fom 204 to 206, he was a postdoctoal eseach fellow with the Boadband Communications Reseach (BBCR) Goup, Univesity of Wateloo, Canada. He is cuently a eseach associate in the BBCR Goup. His cuent eseach inteests include esouce management and potocol design in cognitive adio netwos and vehicula netwos. Peng Yang (yangpeng@hust.edu.cn) eceived his B.E. degee fom the Depatment of Electonics and Infomation Engineeing, Huazhong Univesity of Science and Technology (HUST), China, in 203. Cuently, he is pusuing his Ph.D. degee in the School of Electonic Infomation and Communications, HUST. Since Septembe 205, he has been a visiting Ph.D. student in the Depatment of Electical and Compute Engineeing, Univesity of Wateloo. His cuent eseach inteests include softwae defined netwoing and fog computing. Xuemin (Sheman) Shen [F] (sshen@uwateloo.ca) is a univesity pofesso, Depatment of Electical and Compute Engineeing, Univesity of Wateloo. His eseach focuses on inteconnected wieless/wied netwos, netwo secuity, and so on. He seved as the Technical Pogam Committee Chai/ Co-Chai fo IEEE INFOCOM 4, IEEE VTC-Fall 0, and IEEE GLOBECOM 07, and Symposia Chai fo IEEE ICC 0. He seves/has seved as Edito-in-Chief of IEEE Netwo and the IEEE Intenet of Things Jounal. He is an EIC Fellow, a CAE Fellow, and an RSC Fellow. Hai Zhao (zhaohai@mail.neu.edu.cn) is a pofesso with the School of Compute Science and Engineeing, Notheasten Univesity. He seves as the diecto of the Liaoning Povincial Key Laboatoy of Embedded Technology. His eseach focuses on embedded Intenet technology, wieless senso netwos, and moe. He has successfully applied fo 0 patents, and eceived six awads fo science and technology fom Liaoning Povince and the Ministy of China. He eceived an allowance fom the State Council of China. 50