Resource and Virtual Function Status Monitoring in Network Function Virtualization Environment

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1 Journal of Physcs: Conference Seres PAPER OPEN ACCESS Resource and Vrtual Functon Status Montorng n Network Functon Vrtualzaton Envronment To cte ths artcle: MS Ha et al 2018 J. Phys.: Conf. Ser Vew the artcle onlne for updates and enhancements. Ths content was downloaded from IP address on 26/01/2019 at 22:25

2 Resource and Vrtual Functon Status Montorng n Network Functon Vrtualzaton Envronment MS Ha, P Y, YM Jang Natonal Dgtal Swtchng System Engneerng & Technologcal Research Center, Zhengzhou, Chna Abstract: In Network Functon Vrtualzaton, n order to mprove the utlzaton of the underlyng resources and effectvely deploy the servce chan dynamcally, we wll use the management and orchestraton to montor the network resources and vrtual functon status n real tme, but real-tme montorng wll result n a certan communcaton overhead. In ths paper, a resource state agent montorng strategy s desgned. The mproved label propagaton algorthm dvdes network to subnet and selects the agent montorng node, whch realzes the real-tme montorng of resource status and mnmzes the montorng nformaton communcaton overhead. Smulaton results show that the montorng strategy proposed n ths paper reduces the overhead of montorng nformaton communcaton about 15%. 1. Introducton In tradtonal networks, operators use a large number of dedcated hardware devces to provde network servces for users,whch need huge nvestment costs and mantenance costs, and the tradtonal network confguraton s complex. In order to solve these problems, the ndustry proposed the concept of Network Functon Vrtualzaton (NFV) [1]. In a network functon vrtualzaton envronment, f there s a servce request n the network, the management and orchestraton deploys the servce chan[2] for the request, as servce requests n the network ncrease wth tme, the servce traffc of each servce chan wll also fluctuate constantly, and the status of vrtual functons wll also change contnuously[3]. In order to complete servce chan deployment accordng to user servce requests n a tmely manner and provde users wth hgh-performance servces, the management and orchestraton requres real-tme montorng of the underlyng physcal resources[4], vrtual resources, and network functon status. Gardks[5] proposed a montorng archtecture to montor the status of physcal resources, vrtual resources, and network functons n the network through an ntegraton framework. However, there s a large tme delay n the montorng nformaton of the ntegrated envronment. Pftscher[6] proposed a strategy for each node to montor the state of ts neghborng nodes. It can montor resource status nformaton n real tme, but t would generate a large amount of communcaton overhead. Cao[7] dentfed performance bottlenecks by montorng hardware resource utlzaton and other specfc ndcators, but only hardware resources are montored and network functon status montorng s not consdered; Nak[8] desgned the agent montorng strategy, but ts agent montorng node needs to be deployed n advance, and cannot be dynamcally adjusted accordng to the underlyng network changes after deployment. In the network functon vrtualzaton envronment, the management needs to montor the resources Content from ths work may be used under the terms of the Creatve Commons Attrbuton 3.0 lcence. Any further dstrbuton of ths work must mantan attrbuton to the author(s) and the ttle of the work, journal ctaton and DOI. Publshed under lcence by Ltd 1

3 and statuses n the network n real tme, and the real-tme montorng wll consume a large amount of lnk bandwdth resources. In order to mprove the real-tme montorng and reduce the communcaton overhead, ths paper proposes an agent node montorng strategy. 2. System model and problem descrpton The underlyng physcal network s constructed as an undrected graph G = (V, E), where V = ( v1, v2,, vn)s the set of physcal nodes n the network topology, n s the number of physcal nodes, and E represents the set of lnks n the network. Assume that the underlyng physcal network s dvded nto K sub-networks, denoted by S = ( S1, S2,, S k ), and there are no duplcate nodes between dfferent sub-networks. We select one agent montorng node n each subnet, and make other nodes n the subnet send the resource nformaton and vrtual functon status to the agent montorng node. The proxy node aggregates the data and reports t to the management and orchestraton. Consderng that the actual network topology has communty attrbutes[9], the network can be dvded nto sub-networks based on the correlaton between the nodes. Therefore, the network nodes n the same sub-network are closely connected, and the correlaton s large. The transmsson of montorng nformaton can reduce the delay and lnk bandwdth. The label propagaton algorthm can dvde the network nto multple sub-networks accordng to the network communty structure. However, the sub-networks wll have large gaps, the dfference n sub-network sze wll cause load unbalanced. Because the nfluence and mportance of each node n the network are dfferent, we can dvde the subnet accordng to the mportance of the node and the load of the montorng data n the subnet. Frst, we dvde network accordng to the degree of mportance of the nodes[10] and the load of the sub-networks so that the sub-networks are commensurate n sze. Second, the sub-network agent montorng nodes are searched for by mnmzng the communcaton overhead Dvdng Subnet The label propagaton algorthm updates the label nformaton between the target node and the neghbor nodes accordng to the relatonshp between the nodes n the network. After repeated teratons, the label area s stable, and fnally the sub-network s dvded accordng to the node labels. The mproved label propagaton algorthm proposed n ths paper constrans the subnet allocaton accordng to the mportance of the nodes and the subnet load to mnmze the network communcaton overhead. Defne the node densty functon ρ to represent the number of neghbor nodes. Defne node connectvty Connecton, f node v a and node v b belong to the same subnet then Connecton equals 1, otherwse 0. Defne the sub-network load-balancng ndex LoadBalance, whch s the maxmum dfference n the number of nodes between dfferent subnets. As shown n the formula (1), S represents the number of network nodes n sub-network S. LoadBalance = max( S S j ), j k (1) Frst, we ntalze the network and set dfferent ntal label propagaton numbers C ( V) = ( C ( v1), C ( v2), C ( v n )) for each node n the network, then accordng to the node densty functon, sort the nodes n descendng order, update the node labels and control the label propagaton capablty accordng to the mportance of the nodes. Meanwhle, We use load constrants between m m m m subnets to control subnet sze. Let C ( V) = ( C ( v1), C ( v2), C ( v n )) be the label of each node n the network after m tmes of label propagaton and update Set up the proxy montorng node In the process of montorng nformaton, the communcaton overhead manly ncludes bandwdth and delay. Assumng that each node has the same montorng nformaton bandwdth, the communcaton overhead s manly the lnk transmsson delay, and the communcaton overhead of transmsson 2

4 montorng nformaton between nodes s the shortest delay between nodes, as shown n equaton (2). Overhead( va, vb) = mn PathDelay( va, vb) (2) Assumng that node v s the agent node n the subnet S, the communcaton overhead for transmttng montorng nformaton s shown n equaton (3). m Overheads = Overhead( v, vj) + Overhead Orchestraton (3) j= 1 For large-scale NFV networks, the resource montorng optmzaton objectve functon s shown n Equaton (4). k mn Overheads (4) = 1 The resource montorng n the NFV network also needs to satsfy the followng constrants: LoadBalance StandardLoad (5) Connecton ( v a, v else) = 1 v V a (6) Equaton (5) s a constrant condton for load balancng across subnets, and StandardLoad s the upper lmt of load balancng set by the network operator. Equaton (6) ensures that non-agent nodes n the network can transmt montorng nformaton to the agent node and can only transmt to one agent node. 3. Algorthm solvng The label propagated algorthm proposed n [11] can reasonably dvde the network nto multple subnets n a lnear tme wth less computaton and storage resources accordng to the communty structure characterstcs exstng wthn the network. However, the dfference n the sze of the subnets dvded by the LPA s large, resultng n unbalanced load. For the resource montorng large-scale NFV networks and reduce communcaton overhead, an mproved LPA Subnet Agent Montor (ILSAM) s desgned n ths paper. It s manly dvded nto two steps: the frst step s to use an mproved label propagaton algorthm to dvde large-scale network nto subnets showed n table 1. Table 1. Subnet dvdng algorthm Algorthm Functon Dvde-Subnets Set standard of load balance StandardLoad C ( V) = ( C ( v1), C ( v2), C ( v n )) 2 for each v do 3 calculate ρ ( v ) 4 end for; ' 5 V = Sort( ρ( v )) 6 Set t=0; 7 whle not stop 8 t++; 9 for each vm n ' V do 10 fnd v m adjacent nodes 11 for each adjacent node v a n 12 f LoadBalance StandardLoad 13 break; 14 end f; 15 end for; 16 update Cv ( m ); ' V 3

5 17 end for; 18 update S; 19 t f C t 1 = C 20 break; 21 end f; 22 end whle; 23 return S; In the sub-network dvson step, nodes are sorted n descendng order accordng to the mportance of each node n the network. In the process of updatng labels, the labels are updated n order accordng to ther mportance (lne 9). If the number of multple labels n the neghborng nodes s the same, the labels are selected accordng to the mportance of the nodes (lne 11), and the sze of each subnet network s controlled (lne 12). If the node label s no longer updated, the dvdng algorthm s completed (lne 19). The second step s to deploy the agent montorng node n each sub-network to mnmze the communcaton overhead of resource montorng n the network, the detaled algorthm s shown n table 2. Table 2. Agent node deployment algorthm Algorthm Functon Deploy Agent Node Create N node Input S = ( S1, S2,, S k ) 1 for each S do 2 for each v j do 3 Overhead( v, v ) + Overhead v S j else Orche else 4 end for; 5 fnd v agent make Overhead mnmze; 6 end for; 7 return v agent ; 4. Experments and results 4.1. Smulaton envronment In order to verfy the performance of the strategy, ths artcle uses the GT-ITM tool to generate the underlyng network topology and smulates t usng MATLAB software. The smulaton computer s Wndows 7 operatng system confgured wth 3.4GHz Intel Core processor, 8GB of memory, and 1 Gbps network nterface. In the experment, t s assumed that the amount of montorng nformaton transmtted by each underlyng node s the same, and the nodes do not affect each other. The dfference n the number of nodes n each sub-network does not exceed 3, and the delay of the underlyng lnk network s unformly dstrbuted n the range of [1, 30] Smulaton results and performance analyss In order to verfy the performance of the ILSAM algorthm, we compare t wth the dstrbuted resource montorng algorthms DRM, NFVPerf and LSAM. In the ILSAM algorthm, the proxy node s set accordng to the network communty structure and the mportance of the node. For dfferent network szes and topologes, the algorthm automatcally adjusts the number and locaton of the proxy nodes. The DRM and NFVPerf algorthm's proxy nodes are deployed n advance and cannot adjust the number of agent nodes accordng to the dynamc 4

6 changes of the underlyng network. Fgure 1 shows the change of the proxy node of ILSAM algorthm under dfferent network scales. 30 Number of montorng agents Number of network nodes Fgure 1. Number of montorng agents under dfferent network szes Then, the number of montorng agents under dfferent algorthms s smulated. Fgure 2 shows the varance maps of the number of the underlyng nodes montored by each agent node n dfferent algorthms. From the smulaton results, we can see that the NFVperf algorthm has the smallest dfference n the number of nodes n each subnet, followed by the ILSAM algorthm, and the dfference n the number of subnet nodes n the LSAM s the largest. Snce the NFVperf algorthm sets the number of agent nodes n advance for the statc underlyng network, the number of agent node montorng s relatvely balanced. LSAM sets up the agent montorng node accordng to the network communty structure completely. Therefore, the dfference between the number of sub-network nodes s large. The ILSAM algorthm balances the montorng of the agent node overhead and updates the label accordng to the mportance of the node, so ts subnet sze more balanced. Node varance value of each subnet LSAM ILSAM NFVperf Number of network nodes Fgure 2. The varance of the montorng number of agent nodes under dfferent algorthms Fgure 3 shows the communcaton overhead of montorng nformaton. We can see that the communcaton overhead of the ILSAM algorthm s the smallest, followed by the LSAM and NFVperf algorthms. NFVperf does not dynamcally adjust the proxy node mechansm, resultng n a large communcaton overhead; LSAM Consderng only the communty structure of the nodes, the subnet sze vares greatly, leadng to a large communcaton overhead. ILSAM mproves the LSAM, combnes the mportance of the nodes, and restrcts the sze of the subnet so that communcaton overhead s mnmzed. ILSAM communcaton overhead s reduced by approxmately 15% compared to sub-optmal strateges. 5

7 Montorng nformaton communcaton overhead LSAM NFVperf ILSAM DRM Number of network nodes Fgure 3. Resource montorng communcaton overhead under dfferent algorthms 5. Concluson and future work Ths paper ams at the problem of real-tme montorng of resource status n network functon Vrtualzaton envronment, desgns the agent montorng strategy. Montorng strategy proposed n ths paper reduces the communcaton overhead of montorng nformaton on the bass of real-tme montorng, and expermental results also show that the performance s better than other strateges. However, when consderng the communcaton overhead, ths paper assumes that the montorng nformaton of each node s the same and does not consder the dfferences between nodes. The next major work wll further consder the mpact of node nformaton on the deployment of agent montorng nodes. References [1] Gray K, Nadeau T D. Network Functon Vrtualzaton[M] [2] Lu J, Lu W, Zhou F, et al. On Dynamc Servce Functon Chan Deployment and Readjustment[J]. IEEE Transactons on Network & Servce Management, 2017, PP(99):1-1. [3] Racheg W, Ghrada N, Zhan M F. Proft-drven resource provsonng n NFV-based envronments[c]// IEEE Internatonal Conference on Communcatons. IEEE, [4] Xu Z, Lang W, Gals A, et al. Throughput maxmzaton and resource optmzaton n NFV-enabled networks[c]// IEEE Internatonal Conference on Communcatons. IEEE, 2017:1-7. [5] Gardks G, Koutras I, Mavrouds G, et al. An ntegratng framework for effcent NFV montorng[c]// Netsoft Conference and Workshops. IEEE, [6] Pftscher R J, Sched E J, Santos R L D, et al. DReAM - a dstrbuted result-aware montor for Network Functons Vrtualzaton[C]// Computers and Communcaton. IEEE, 2016: [7] Cao L, Sharma P, Fahmy S, et al. NFV-VITAL: A framework for characterzng the performance of vrtual network functons[c]// Network Functon Vrtualzaton and Software Defned Network. IEEE, 2016: [8] Nak P, Shaw D K, Vutukuru M. NFVPerf: Onlne performance montorng and bottleneck detecton for NFV[C]// Network Functon Vrtualzaton and Software Defned Networks. IEEE, 2017: [9] Jguang, WANG, Yuqng, et al. REMARKS ON NETWORK COMMUNITY PROPERTIES[J]. Journal of Systems Scence & Complexty, 2008, 21(4): [10] Shve M S, Anderson J M. Bodegradaton and bocompatblty of PLA and PLGA mcrospheres.[j]. Advanced Drug Delvery Revews, 2012, 64(1):

8 [11] Raghavan U N, Albert R, Kumara S. Near lnear tme algorthm to detect communty structures n large-scale networks[j]. Physcal Revew E Statstcal Nonlnear & Soft Matter Physcs, 2007, 76(3 Pt 2):

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