Embedding Algorithm for Virtualizing Content-Centric Networks in a Shared Substrate

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1 Embedding Algorithm for Virtulizing Content-Centric Networks in Shred Substrte Shengqun Lio, Xioyn Hong, Chunming Wu, Ming Jing College of Computer Science nd Technology, Zhejing University, P.. Chin Deprtment of Computer Science, University of Albm, Tuscloos, AL Institute of Softwre nd Intelligent Technology, Hngzhou Dinzi University, P.. Chin Abstrct Network virtuliztion enbles diversified network rchitectures to coexist in substrte network. Content-centric network (CCN), s one of the mjor proposls for the future network, cn be deployed in the virtulized environment. However, the recent work tht hs ttempted in this direction hs not ddressed issues concerning the unique resource usge of CCN in resource lloctions during the embedding procedure when instntiting virtul CCN (VCCN). The unique problem is the cche component, i.e., the content store, in CCN. This pper will develop VCCN Mpping Algorithm (VCCNMA) tht optimizes the performnces of VCCN considering the content store request in ddition to CPU nd bndwidth. Since the resource lloction problem is NP-hrd, this pper thus proposes heuristic lgorithm. Severl sets of simultion experiments re performed to evlute the cceptnce rtio, the network cost, the storge utiliztion rtio nd lod blnce. esults show tht the lgorithm cn chieve better performnces. Moreover, the pper lso studies the trdeoffs between the network cost nd the lod blnce. Keywords - virtul network mpping; content-centric networking; network virtuliztion; distributed storge lloction; storge lod blnce I. INTODUCTION Content-Centric Networking (CCN) [1] is one of the min clen-slte rchitecture for the future Internet [2]. Under this communiction rchitecture, consumers brodcst interest pckets nd those who hve the content chunks serve the dt pckets. During the trnsmission, routers cn comprehend the content nme nd cche the content chunk in their content stores. Therefore, it is possible for ny routers to stisfy n interest pcket, if the content nme mtches cched chunk, without further forwrding the interest pcket to the dt source. The CCN rchitecture hs mny benefits, for exmple, improving performnces [3] nd lowering network costs [4]. On the other hnd, network virtuliztion llows different network services nd experiments to coexist in shred substrte network [5, 6 nd 7]. It is regrded s technology tht is ble to become long-term solution for reconciling the co-existence issue for Tody s Internet nd multiple future network innovtions [8]. As such, we believe tht CCN, populr future network proposl, cn be embedded s sliced instnce in the virtulized environment. In ddition, we lso believe tht embedding CCN is beneficil both for its rel production uses nd experimentl vlidtions. It cn be expected tht the lrge-scle network testbeds (e.g. Prof. Chunming Wu is the corresponding uthor. GENI [6] nd PlnetLb [9]) cn be fully utilized to vlidte performnces of innovtions in CCN. This pper is to ddress the resource lloction problem in the VCCN embedding procedure tht instntites VCCN onto the substrte network. The mjor problem is how to llocte resources of the substrte network to support the request of slicing VCCN request. Usully, CPU nd bndwidth re considered in generl virtul network embedding modules [1, 11 nd 12]. However, CCN hs its unique cche component, i.e., the content store [1]. No existing work hs considered the storge demnds in the embedding issue. Yet, s shown in this pper, such considertion cn led to efficient lgorithms tht improve the performnce of sliced CCN. In this pper, we will develop n efficient VCCN Mpping Algorithm (VCCNMA) to optimize the performnce of CCN when running in the network virtuliztion environment by considering its unique content store lloction needs. The key ide we propose in VCCNMA is to distribute the requested storges long the routing pths in the substrte network, whilst ssigning CPU nd bndwidth resources in greedy wy. However, the storge lloction optimiztion problem is proven to be Integer Liner Progrm (ILP) which is typicl NP-hrd problem [11]. As such, we relx the ILP to convex progrmming, nd introduce heuristic solution (SAA) for the distributed storge lloction issue. Severl simultion experiments re then performed to evlute VCCNMA in term of the cceptnce rtio, the network cost, the storge utiliztion rtio nd lod blnce. The results show our lgorithm cn chieve better performnces. In ddition, the embedding lgorithm needs to consider the trdeoff between the ccess cost nd the blnce of the storge lod. The ccess cost cn be lowered if more storge resources re ssigned closer to the destintion node. However, this strtegy cn threten the storge lod blnce needed by the Internet Service Providers (ISPs). To tckle the conflict, we introduce weight fctor in the proposed heuristic lgorithm. The desired trde-off between the lod blnce nd the ccess profit cn be chieved by djusting the weight. Simultions re performed to show how the weight influences the trdeoff. The rest of the pper is orgnized s follows: In Section II, we present the embedding model nd problem formultion. Section III describes VCCNMA in detil. Section IV gives simultion results vlidting our proposed lgorithm. Finlly, conclusion is presented in Section V.

2 II. CONSIDEATION OF STOAGE ALLOCATIONS A. Models for Virtul nd Substrte Networks Current reserches on embedding virtul network to substrte focus on improving the cceptnce rtio nd reducing the network cost, where constrints re usully the CPU cpcity nd bndwidth in virtul nodes nd links. Hence, corresponding solutions re mostly heuristic so s to reduce the computtionl complexity [1, 11 nd 12]. There re other works trying to implement CCNs in the virtulized environment [13, 14]. However, issues concerning the unique resource usge of CCNs during the embedding procedure, specificlly, the storge resource for content store, remin open. Here we present virtul network to physicl network (V2P) model tht cptures the VCCNs mpping. The model includes dding the storge ttribute for virtul/physicl nodes nd the direct routing constrint for virtul links in comprison to the generl model in ef. [1, 11 nd 12]. The proposed core strtegy is to distribute the requested storge to severl physicl nodes long the substrte pth which crries virtul link. Thus, we use directed grph to denote the virtul network, nd n undirected grph for the physicl network. Under this model, the trditionl undirected virtul link cn be viewed s two overlid directed ones. Further, we tret the forwrding behvior of interest pckets to be bi-directionl in the substrte network to ese the modeling of locting chunks in CCN. This tretment is resonble becuse the interest pckets re smll nd consume little bndwidth. Such model hs reduced complexities in the resource lloction, but it keeps CCN protocol behvior intct. B. Benefit nlysis of distributed storge lloction 3 1 A The CPU nd Storge request Fil b The vilble CPU nd Storge resources A B C D () Trditionl Virtul Network Embedding Succeed b f1 f2 B C D f1 f2 f3 f4 (b) Virtul Network Embedding with Distributed Storge Alloction Fig. 1: Mpping CCN using two virtul network models The cceptnce rtio is the min metric to guge the successfulness of mpping lgorithm. It cn be enhnced when the storge cpcities of ll nodes long the substrte pth re cultivted for distributed storge lloctions. In our model, the loctions nd the cpcities of the storge nodes f3 f4 re both considered. Fig.1 depicts the CCN mpping under the trditionl nd proposed virtul network embedding model, respectively. The virtul network (VN) hs only two nodes nd one link (b ), nd the physicl network (PN) hs four nodes on line (D C B A). The number in the squre box indictes the requested or vilble CPU resources on the virtul/physicl nodes, nd the number in round box indictes the requested or vilble storge resources. In this cse, we ssume the requested bndwidth of (b ) cn be stisfied by the substrte network. A trditionl forwrding mpping cretes tunnel for the virtul link onto the substrte pth. As shown in Fig.1., the virtul link (b ) is mpped onto the substrte pth (D C B A), where the bndwidth resources re reserved for dt pckets nd the virtul node nd b re embedded onto the substrte A nd D, respectively. If the requested storge of the virtul node b is 3, the embedding of b to D would fil becuse the substrte node D does not hve enough storge resources to host b s need in the trditionl wy. In contrst, the requested storge of 3 cn be stisfied in distributed storge lloctions. As shown in Fig.1.b, the requested storge 3 is distributed to nodes {B, C, D}, nd the virtul network embedding cn succeeds. Hence, the distributed storge lloction hs enhnced the VCCNs embedding cceptnce rtio. Another dvntge of distributed storge lloctions is tht the routing efficiency would not be reduced in CCNs, rther, be often incresed. Tke b s requested storges being 2 nd its cched chunks being {f 1, f 2, f 3, f 4 } s n exmple. In the trditionl mpping, ll the requested storge of b would be mpped onto D, nd node A cn get f 1 from D in three hops (Fig.1.). By the distributed storge lloction, f 1 cn be stored in B. outing from D to B is in two hops, nd A gets f 1 from B in one hop. As result, the totl ccess cost is three hops which is the sme s tht in trditionl mpping, when the ccess frequency F is 1. More benefits cn be chieved when F is bigger (F 1). According to the current usge of CCN, it is resonlbe to ssume tht F is much bigger thn 1 becuse populr content chunks re cched in routers [13]. Thus, in Fig.1., if A requests content f 1 fromdf times, the totl network cost is 3F. However, in distributed storge lloction (Fig.1.b), the first visit hs cost of 3. For the rest F-1 requests, only 1*(F- 1) hops re needed becuse f 1 hs been cched in B fter the first request. Thus, the totl cost reduces to F+2. The cost reduction is 2(F-1). In generl, with distributed storge lloctions, the cost reduction is i length ic i(f 1) in verge, where length is the totl hops of the substrte pth nd c i is the size of cche which is llocted to the ith node wy from the source node on the substrte pth. C. Whose requested storge cn be divided? Here we nlyze how to distribute the requested storges of virtul nodes. Although distributing requested storges of source nodes hs potentil to chieve higher ccess profits. Not ll the virtul source nodes requested storges could be prtitioned. Further work of trnsformtion of the input virtul network grph is needed. Using the term of grph theories, virtul source nodes could be clssified ccording to their outdegrees. For instnce, the set {} nd {b,c,d} in Fig.3. contin

3 9 4 e d 7 5 9, G V c e G V 1 3 b () Originl Virtul Network equest Trnsfer requested storge 4 6 d b 1 5 (b) Modified Virtul Network equest Fig. 2: Preprocessing virtul network inputs nodes of out-degree of two nd one, respectively. Firness considertion speks ginst find dividing nd distributing the requested storge of virtul nodes with two or more outdegrees. As the exmple in Fig.2. shows, if we ssign the requested storge of on the substrte pth of the virtul link( b), the virtul node c will suffer unfir tretment, becuse more hops to ccess the cched contents in b would be needed. Hence, we suggest to distribute the requested storge of the source nodes whose out-degree is 1 in the virtul network. With the bove nlysis, ech input virtul network cn be trnsformed to new grph. The requested storge of the source nodes which meet the fore-mentioned requirements cn be trnsferred to its djcent virtul link nd so the virtul link hs the storge ttribute. Hence, the originl input (Fig.2.) cn be trnsformed to Fig.2.b. D. Problem Formultion It is noted tht the storge distribution long the substrte pth does not hve to be uniform. There re cses tht one wnting more cost reductions tends to ssign more storge to substrte nodes tht re closer to the destintion. For exmple, if we trnsfer C s llocted storges (1 units) to B in Fig.1.b, 1*(F-1) more benefits could be chieved under the verge F ccesses for ech unit. Doing this would, however, jeoprdize the storge lod blnce in the substrte network, becuse blnced storge lod distribution is needed to increse the cceptnce rtio [1]. There is obviously trde-off between the contrdictory requirements nd we found tht VCCN mpping with Blncing the Access Profit nd the storge Lod Blnce (VCCN-BAPLB) is equivlent to the trditionl virtul network embedding problem, where the storge lod nd the ccess benefit from distributed storge lloctions under ny mpping cn be viewed s when the requested storge of ll virtul nodes is. It hs been proven tht the virtul network embedding problem cn be reduced to NP-hrd problem [15]. Therefore, VCCN-BAPLB is lso NP. III. ALGOITHM DESIGN In this section, we formulte our object which seeks for trde-off between the ccess benefit nd the storge lod blnce during the storge lloction. After the issue is proven to be NP, heuristic lgorithm (SAA) is proposed for the solution. VCCNMA strts with greedy node embedding, nd then uses the K-shortest lgorithm to find substrte pths for virtul links, followed by distributing requested storge of virtul links to nodes long the substrte pth with SAA. c 7 6 A. Trde-off between the ccess benefit nd the lod blnce We denote the substrte network by n undirected grph G S =(N S, E S, A S N, AS E ), where N S nd E S re the set of substrte nodes nd links, respectively. Substrte nodes nd links re ssocited with their ttributes A S N nd AS E, which ccounts for CPU, storge nd bndwidth. On the other hnd, the virtul network is described by directed grph G V =(N V, E V, CN V, CV E ), where N V nd E V refer to the set of virtul nodes nd links, respectively, nd CN V nd CV E re constrints for virtul nodes nd links in terms of CPU, storge nd bndwidth. The nodes in N V which stisfy the requirements for distributed storge lloctions re put into the set Φ. Thus, the VCCN embedding cn be defined s the following mpping: M: G V (N, P, N, L ), where N N S, P is loop-free pth in the substrte network. N nd L re the resources of substrte nodes nd links llocted to the virtul network G V. The mpping is fesible when the following conditions re stisfied: bw res (p ) bw(e v ), for e v p (1) cpu res (n ) cpu(n v ), for n v n (2) storge res (n ) storge(n v ), for n v n, iff n / Φ (3.1) storge res (n ) storge(n v ), for n v n, iff n Φ (3.2) n p n nd p re elements in N nd P, respectively. n v nd e v re virtul nodes nd links in G V. It mens the requested bndwidth should be fulfilled in the substrte pth. Tht lso requires the requested CPU nd storge resources of virtul nodes must be stisfied by the residul resources of its host nodes, if the virtul nodes requested storges cnnot be divisible. Otherwise, we distributed the requested storges of divisible nodes to the physicl nodes long the substrte pth. If the requested storges of v 1 is divisible nd the virtul link v 1 v 2 is mpped onto the substrte pth p =(n s, n s 2,..., n s t ), we should distribute the requested storges of v 1 to its physicl pth {n s, n s 2,..., n s t } for mximizing ccess profits nd minimizing lod differences in the storge mpping. n Define 1: We use L= mx{ v n s storge(nv ) storge(n v ) }, n s {n s, n s 1,..., n s t } to denote the storge s lod blnce on the substrte pth. n v n s storge(nv ) is the sum of requested storges of virtul nodes mpped onto n s. We cn see tht L is the lrgest storge utiliztion rtio mong nodes on the substrte pth. Define 2: If the size of content chunk is 1, the requested storge C totl of the virtul node v 1 is distributed to nodes {n s, n s 1, n s 2,..., n s t } s the vector {c s, c s 1, c s 2,..., c s t}, where t is the length of the substrte pth nd c i is the cche size llocted to the ith node wy from the source node in the pth. As is nlyzed in Section II, we cn define the ccess profits of distributed storge lloctions s: B = i t i c i (F 1) (4) The more storges re llocted for substrte nodes closer to the destintion node, the more benefits could be chieved. However, it would brek the lod blnce constrint. Hence, we introduce integer liner progrm to describe this problem. objective: minimize α K L - B (5) s.t.

4 c i storge res (n s i ), i t (6) c i = C totl (7) i t c i N, i t (8) The objective (Eq.5) is to gurntee the storge s lod blnce (Define 1) of substrte nodes, whilst mximizing the ccess benefits (Define 2) in distributed storge lloctions. Constrint (6) sys tht the substrte nodes must hve enough cpbility to ccept those llocted storges; while constrint (7) gurntees tht the sum of distributed storge resources is equl to the totl requested storge of the virtul source node. In Eq.8, the llocted storges for ll nodes long the pth should be multiple of the content chunk size, ssuming the size of content chunk is 1. The distributed storge lloction is integer liner progrm which is well-known NP-hrd problem [11]. However, the constrint (8) could be relxed s the following constrint: c i (9) After the relxtion, we nme the new liner progrm s Storge LP ELAX. It is bsed on convex optimiztion s formulted in the following: We suppose tht the current storge lod vector of ll nodes long the substrte pth is S=(s,s 1,s 2,...,s t ) nd the solution vector is C=(c,c 1,c 2,...,c t ). If the storge cpcity vector for these nodes is =(r,r 1,r 2,...,r t ), our objective (Eq.5) could be rewritten s follows: F(C)=α K mx{ si+ci r i }-P C T (F-1), i {, 1, 2,..., t} where P=(,1,2,...,t) is coefficient vector to clculte ccess benefits. Then we prove tht our objective is convex. To simplify our description, we replce mx{ si+ci r i }(i {,1,2,..., t}) with mx { S+C }. Also, we ssume the solution spce of Storge LP ELAX is Θ. X nd Y re the two solution vectors in Θ. Ifθ (, 1) nd (1-θ) X+θ Y Θ: F((1-θ) X+θ Y) = α K mx{ S+(1 θ) X+θ Y } +P ((1-θ) X T +θ Y T ) (F-1) α K mx{ 2S+(1 θ) X+θ Y } +P ((1-θ) X T ) (F-1) +P (θ Y T ) (F-1) α K mx{ S+(1 θ) X } +P ((1-θ) X T ) (F-1) + α K mx{ S+θ Y } +P (θ YT ) (F-1) F((1-θ) X) + F(θ Y) Therefore, the objective function F(C) is convex, nd this convex optimiztion (liner progrm) cn be solved in polynomil time with the interior-point polynomil lgorithm or ellipsoid lgorithm [16]. B. VCCN Mpping Algorithm When the requested storge of ll virtul nodes is set s, the VCCN mpping issue is equl to the trditionl virtul network embedding which is NP. Hence, we cn conclude the VCCN mpping is NP problem, nd heuristic lgorithm (VCCNMA) is then proposed for the problem. Initilly, we preprocess the input virtul network with discussions in Sec.II.C. After tht, we give the following formul: rnk(n s ) = storge(n s ) cpu(n s ) bw(l) (1) l Neighbor(n s ) to compute the weight for virtul/substrte nodes. Then, virtul nodes re mpped onto substrte nodes one to one ccording to their rnks in vlues clculted with formul (1), followed with the link mpping. In the link mpping phse, K-shortestpth lgorithm is utilized to find substrte pths for those virtul links. Finlly, we cll SAA lgorithm to distribute the requested storges of virtul links to ll nodes long the pth. If the link/node mpping nd SAA succeed, we ccept this virtul network. In SAA (Algorithm 1), for ech virtul link in the modified virtul network G V, we choose the shortest pth which met the requested storges from its cndidte pth set to support the virtul link for the reson tht it consumes lest bndwidth resources. Then, we solve the relxed liner progrm (Storge LP ELAX) so s to distribute requested storges to ll nodes long the substrte pth. Since the solution vector is rel, we then pply the brnch-nd-cut lgorithm to get the finl integer ssignment. TABLE I: P-code of SAA Algorithm 1: Storge Alloction Algorithm (SAA) Input: the virtul network G V, nd the pth set P S = 1 i E V P s i.(p s i is the K-pth set including k cndidte substrte pths for the virtul link e v i in GV ) Begin: 1. For ech e v i G v 2. Sort elements in P s i ccording to their lengths. 3. BOOL Flg =. 4. For ech p s i P s i // ps i =(ns, ns 1, ns 2,..., ns t ) 5. if storge res(n s i ) storge(ev i ) i t 6. Flg = 1, p = p s i //Sve the pth 7. Brek 8. end if 9. End for 1. if Flg =, then reject the request. 11. Solve Storge LP ELAX(storge(e v i ), p) 12. Apply the brnch-nd-bound serch for the finl integer ssignment. 13. End for End C. Complexity Anlysis In VCCNMA, the time complexity of the node mpping is O(N 2 ) for the greedy lgorithm. The time complexity of the link mpping is O(logN N 3 ) owe to the k-shortest-pth lgorithm s (O(logN N+k N)) (k logn) nd the mximl number of links O(N 2 ). Also, the Storge LP ELAX could be solved in polynomil time by the ellipsoid lgorithm or Krmrkrs interior point lgorithm [16]. Although the complexity of the brnch-nd-cut lgorithm is exponentil, we cn limit the itertion number for pproximte solution in polynomil time. IV. PEFOMANCE EVALUATION The purpose of our evlution is to vlidte tht our heuristic lgorithm cn chieve better performnces in the cceptnce rtio, the ccess cost, the storge utiliztion rtio nd lod blnce. The comprisons re mde to bseline lgorithm nd rndom lgorithm s no direct relted work exists. Further, the simultions lso show tht the trdeoff between the lod blnce nd the ccess profit cn be controlled by weight fctor. A. Simultion settings In this pper, the simultion environment is the sme s tht in ef.[12]. We use the brite tool [17] to generte substrte

5 network with 1 nodes. Ech pir of nodes in the network is connected with link with probbility.5. The vilble CPU nd storge resources for physicl node follow n uniform distribution between 5 nd 1. Also, the bndwidth resource for link is uniformly distributed in the rnge from 5 to 1. In contrst, the number of VN nodes follows unique distribution between 2 nd 1. Also, ech pir of virtul nodes is rndomly connected with probbility.5. The CPU, Storge nd bndwidth for virtul node/link re ll uniquely distributed from 1 to 2. The virtul network requests (VNs) rrive following Poisson process with n verge rte of 4 VNs per 1 time units. Ech VN s lifetime is 5 units. We run for bout 5 time units, which corresponds to 2 VNs on verge in one instnce of simultion. We compre our lgorithm with bseline lgorithm (BL- VNE) [12] nd rndom lgorithm (VNE) [18]. BL-VNE nd VNE both do not distribute the requested storges long the substrte pth. The difference between them is tht BL-VNE choses the host nodes ccording to their rnks in resources in the node mpping phse, while VNE rndomly select the mpping nodes. B. Metrics 1) Acceptnce rtio: We define our cceptnce rtio s formul (11), which is the division of the number of ccepted virtul networks nd the totl VNs. T i= cceptnce rtio = M(GV i ) (11) T i= GV i 2) Averge network cost: The totl network cost is the sum of expenses to ccess those requested storges vi the virtul link e v with n verge frequency F ev. We cn get the Averge Network Cost (ANC) by dividing the totl cost with the number of virtul links. ANC 1 = ANC Fe v = v i length e v c e e v E v i length e v E v e v E V i length E v (F ev 1)(length i)c ev i iff: F e v =1 (12) + AVC 1 iff: F e v > 1 (13) c ev i is the requested storge of the virtul link e v llocted for the ith node wy from the source node on the pth. length is the totl hops of the substrte pth of e v, nd F ev is the verge ccess frequency for e v s requested storges. 3) Averge storge utiliztion rtio nd stndrd devition of the storge resources in the substrte network: The verge storge utiliztion rtio is quotient of the sum of current substrte nodes s storge utiliztion rtio divided by the number of nodes. It is presented in formul (14): storge(n v ) U storge = n s N S n v N V n v n s storge(n s ) N S (14) As for the stndrd devition of the storge resources, we defined s formul (15): U σ = storge(n v ) n ( v N V n v n s storge(n s U ) storge) 2 n s N S N S (15) C. Simultion results nd nlysis Initilly, α is ssigned s 1, while K is set s 1 to blnce the mgnitude of the two vlues. After tht, we solve the integer liner progrm with ILOG CPLEX [19] nd get the sttisticl results in the cceptnce rtio, the network cost, the verge storge utiliztion nd lod blnce (stndrd devition). Then, we observe the reltion between the ccess frequency nd the ccess benefits. Finlly, we discuss how α influences the storge lod blnce nd the network cost. Fig.3 shows VCCNMA outperforms BL-VNE (1%) nd VNE (2%) in term of the cceptnce rtio. Without the distributed storge lloction, virtul nodes in BL-VNE nd VNE should find physicl nodes tht concurrently meet CPU nd storge requests s their hosts. In contrst, virtul nodes only need to find those with enough CPU s their hosts in VCCNMA. Hence, the node mpping of VCCNMA is esier to succeed thn tht of BL-VNE nd VNE, which leds to higher cceptnce rtio. Correspondingly, this mens more virtul network requests could be successfully mpped onto the substrte network with VCCNMA. Therefore, ech substrte node would hve higher storge lod in verge, s hs been confirmed in Fig.4. Fig.5 describes comprisons of three lgorithms on the stndrd devition of the storge utiliztion. Fig.5 shows tht VCCNMA cn keep higher performnce in the storge lod blnce in substrte nodes. Tht is becuse VCCNMA breks big storge requests into multiple cches. Thus, the differences in the storge utiliztion mong substrte nodes re smller thn those of the other two schemes. Hence, the storge lod is much more blnced in substrte nodes. As is shown in Fig.6, the higher the verge ccess frequency is, the more benefits the distributed storge lloction cn gin. More specificlly, the network cost is lmost the sme when the verge ccess frequency (F) is1.however, we cn obtin more benefits when F chnges from 2 to 6. If the requested storges re llocted closer to the clients, clients cn ccess these content chunks vi fewer hops. Obviously, the ccess benefits is proportionl to the ccess frequency. Fig.7 nd Fig.8 show tht α cn djust the trdeoff between the storge lod blnce nd the network cost. When α becomes lrger, optimizing the storge lod blnce becomes more importnt in our objective function. Hence, the stndrd devition of the storge utiliztion would be smller nd smller. As shown in Fig.7, when α chnges from.1 to 5, the storge lod is more nd more blnced in the substrte nodes becuse its stndrd devition is becoming smller. In contrst, the benefit would decrese nd the verge network cost would increse, which hs been confirmed in Fig.8 where the network cost incresingly becomes lrger when α becomes bigger. V. CONCLUSION In this pper, we introduced the problem concerning the storge need when embedding CCNs onto the substrte network, nd proved tht the problem is NP. As such, we

6 Acceptnce tio = 1, K = 1 BL VNE VNE VCCNMA Fig. 3. Comprison on Acceptnce tio Averge Utiliztion of Storge BL VNE.5 = 1, K = 1 VNE VCCNMA Fig. 4. Comprison on Averge Storge Utiliztion Stndrd Devition of Storge Utiliztion BL VNE = 1, K = 1 VNE VCCNMA Fig. 5. Comprison on Stndrd Devition of Storge esources Averge Cost for Accessing the Storge = 1, K = 1 Benefits of Distributed Storge (F = 6) Benefits of Distributed Storge (F = 2) BL VNE (F=1) BL VNE (F=2) BL VNE (F=6) VCCNMA (F=1) VCCNMA (F=2) VCCNMA (F=6) Stndrd Devition of Storge Utiliztion VCCNMA (=.1) VCCNMA (=1) VCCNMA (=2) VCCNMA (=5) K = 1 Averge Cost for Accessing the Storge K = 1, F = 6 VCCNMA (=.1) VCCNMA (=1) VCCNMA (=2) VCCNMA (=5) Fig. 6. Comprison on the verge network cost with different F Fig. 7. Comprison on storge lod blnce with different α Fig. 8. Comprison on the verge network cost with different α developed heuristic lgorithm (VCCNMA) to solve for efficient embedding tht yields better VCCN performnces. Severl simultion experiments hve vlidted tht VCCNMA cn chieve higher performnces in the cceptnce rtio, the verge storge utiliztion, the storge lod blnce nd network cost. The results lso show tht we cn control the trdeoff between the lod blnce nd the ccess benefits with weight fctor. ACKNOWLEDGMENT This work is supported by the Ntionl Bsic eserch Progrm of Chin (212CB31593), the Progrm for Zhejing Leding Tem of Science nd Technology Innovtion ( , 213TD2), the Ntionl Science nd Technology Support Progrm (214BAH24F1), 863 Progrm of Chin (215AA1613), nd the Ntionl Nturl Science Foundtion of Chin ( ). EFEENCES [1] Vn Jcobson, Din K. Smetters, et l. Networking Nmed Content, Proc. of ACM CoNext9, pp. 1-12, 29. [2] Pedro Henrique, V. Guimres, et l. Experimenting Content-Centric Networks in the Future Internet Testbed Environment, pp [3] Psrs I, Clegg G, et l. Modeling nd evlution of CCN-cching trees, in Networking 211, Springer Berlin Heidelberg, pp , 211. [4] Tyson G, Kune S, et l. A trce-driven nlysis of cching in contentcentric networks, Proc. of IEEE ICCCN 212, pp. 17, 212. [5] Anderson T, Peterson L, et l. Overcoming the Internet impsse through virtuliztion, Computer, 25(4): [6] GENI: [7] Bvier A, Femster N, Hung M, et l. In VINI verits: relistic nd controlled network experimenttion, Proc. of ACM SIGCOMM Computer Communiction eview, 36(4): 3-14, 26. [8] Nick Femster, Lixin Go, et l. How to Lese the Internet in Your Spre Time, in ACM SIGCOMM Computer Communiction eview, 37(1): 61-64, 27 [9] PlnetLb: [1] Zhu Y., Ammr M. H., Algorithms for Assigning Substrte Network esources to Virtul Network Components, Proc. of IEEE INFOCOM 26, pp. 1-12, 26. [11] Chowdhury N. M. M. K., hmn M.., et l. Vritul Network embedding with coordinted node nd link mpping, Proc. of INFOCOM 29, pp , 29. [12] Minln Yu, Yung Yi, et l., ethinking virtul network embedding: substrte support for pth splitting nd migrtion, in ACM SIGCOMM Computer Communiction eview, 38(2): 17-29, 28. [13] Msto Ohtni, Keiichiro Tsukmoto, et l. VCCN: Virtul Content- Centric Networking for elizing Group-Bsed Communiction. InProc. of ICC 213. [14] S. Slsno, et l. Informtion centric networking over SDN nd Open- Flow: Architecture spects nd experiments on the OFELIA testbed. Vol. 57, No. 16, pp , Nov [15] D. G. Andersen. Theoreticl pproches to node ssignment, Unpublished Mnuscript, 22. [16] A. Schrijver, Theory of liner nd integer progrmming, John Wiley & Scons, [17] BITE : Topology Genertor, [Online] Avilble: [18] S. Zhng, et l. Virtul Network Embedding with Substrte Support for Prlleliztion, in Proc. of Globecom 212, pp , 212. [19] ILOG IBM., Cplex optimiztion studio, [online:]