A Dynamic Energy-Efficiency Mechanism for Data Center Networks

Transcription

1 A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan A Dynamc Energy-Effcency Mechansm for Data Center Networks 1 Sun Lang, 1 Zhang Jnfang, 1 Huang Daochao, 1 Yang Dong, 1 Qn Yajuan 1, School of Electroncs and Informaton Engneerng, Bejng Jaotong Unversty, Bejng @bjtu.edu.cn, @bjtu.edu.cn, @bjtu.edu.cn, dyang@bjtu.edu.cn, yjqn@bjtu.edu.cn,l Abstract Cloud computng s a large scale dstrbuted computng paradgm, whch can assgn a subset of the computng re-sources n the data center. In ths paper we present a novel energy-effcent ser-vce selecton algorthm for servce com-poston n date center wth consderaton of servce relevance. Ths approach s to buld more flexble and power-effcent vrtual server accordng to the dynamcally nterconnecton network n data center. Fnally, the performance of energy effcent servce composton selecton s evaluated. Keywords: cloud computng, data center, energy-effcency, servce orented archtecture, 1. Introducton As globalzaton evolve today, new requrements for servces computng and network archtecture emerge. Cloud computng s a large scale dstrbuted computng paradgm, whch can assgn a subset of the computng resources n the data center, e.g., processng power, storage, software, and network bandwdth, to customers on demand for a defned perod [1]. However, a typcal data center for cloud computng conssts of tens of thousands servers and comprses correspondng thousands of herarchcally connected swtches[2]. Data centers need enormous energy not only to operate ther servers but to cool and protect them. So, how to save cost of energy s a very mportant ssue. Energy effcency has become crucal for data centers [3]. Some recent research shows, about one-thrd of the total energy consumpton n data centers s communcaton cost, whle the remanng two-thrds are allocated to computng servers [4]. There are two popular technques for power savngs n data center. A major power conservaton technque n data center s energy-aware management whch adjusts hardware power consumpton at run tme accordng to computng load [5]. The am of the mechansm s to use the as lttle computng resources as possble to maxmze the number of unloaded servers that can be powered down (or put to sleep). Another exstng approach for energy savng s to buld specal network archtecture whch could reduce the communcaton cost n data center [6]. However, Most of the exstng approaches n data centers focus exclusvely on the devce status and network status. To the best of our knowledge, only a few methods have consdered servces relevance n data center [7]. One man feature of communcaton network n data center s the huge number of nterconnectons of the servers n the same data center. The multple servce nstances n the same workflow, often n practce, n the same data center. Moreover, the network n data center s the tree-based network, and the bandwdth of root node of the tree s the bottleneck. Thereby the network bandwdth s often a scarce resource n data center. It wll save more power f the tasks of the workflow can be completed at the as less route hops as possble. The motvaton of ths paper comes from a concse problem: whether t wll use less energy f ntegratng the servces relevance and network topology? In ths paper, we present a novel, smple, yet effectve servce selecton model n data center that could buld more flexble and power-effcent vrtual server accordng to the dynamcally nterconnecton network n data center. And ths model can provde a good QoS performance. Our specfc contrbutons are: 1) A framework for combnng servces relevance and network topology n a sngle data center. Internatonal Journal of Dgtal Content Technology and ts Applcatons(JDCTA) Volume6,Number7,Aprl 2012 do: /jdcta.vol6.ssue

2 A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan 2) The mechansm of an energy-effcency servce selecton algorthm for servce composton that focus drectly on mnmzng energy durng executon tmes of the workflow through takng hgh prorty of nterconnecton routes. 3) The smulaton results of an extensve evaluaton of our algorthm to provde a prelmnary analyss of the performance of these algorthms. The remander of the paper s organzed as follows: Secton 2 presents the man concepts and descrbes the energy-aware model and nterconnecton routes problem n data center. Secton 3 dscusses the soluton to the servce composton and descrbes energy-aware servce selecton algorthm. Secton 4 evaluates and compares the expermental results of dfferent solutons for the problem. Secton 5 concludes ths paper. 2. Problem Analyss and Requrements 2.1. Overvew of Servce Composton n Data Centers The archtecture of servce composton n data center ncludes network protocols, management system, applcatons, and nterfaces for nterconnecton for servces. As the space s lmted, we only dscuss the components nvolved ths paper, leavng out a large number of detals of servce composton [8][9]. Fgure 1. The Framework of Servce Composton n Data Center The basc framework of our mechansm for servce composton n data center s shown n Fgure1. Some servce templates (STs), whch consst of abstract servce types, have been wrtten n a meta-data level descrpton[10]. A characterstc of our framework s the mappng relaton between servce type and servce nstances has been stored n the servce regstry. The mappng table wll be llustrated at next secton. Servce composton engne (SCE), whch deployed n the data center, translates the user nqures to STs based on some certan rules. The framework wll choose the sutable servce nstances accordng to customer requrements and the status of the current network of the data center. The sequence of SIs wll be nvoked by the SCE through the web servce nterfaces. Then, the servce nstances wll actvate the servers n data center Challenges of Energy Savng for Servce Composton n Data Center In order to llustrate the servce selecton algorthm for servce composton, we pont to a smple example, consderng Fgure.2. Servce d 1 s a cloud storage servce whch store some AVI format 136

3 A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan move fle; servce s 1 s a vdeo transcodng servce, whch can convert av to mp4; servce s 2 s also a vdeo transcodng servce, whch can convert av to rmvb ; servce s 3 can convert rmvb to mp4; servce s 4 s a servce of Vdeo-On-Demand (VOD). There are two servce sequences to fulfll VOD: the frst one s ( s 4 d 1 s 1 ),and the second one s ( s 4 d 1 s 2 s 3 ). However, the bandwdth between s1 and d 1 s very low. How to select the sutable sequence of servce nstance s a problem once a customer wants to request s 4 for a move and play on hs/her the moble, whch just support for the format of mp4. Fgure 2. An Example for Servce Composton n Data Center Ths servce selecton process nvolves several challenges: 1) Choce of abstract workflow: The user requrements may satsfed by multple abstract workflows. The SCE needs to fnd the optmal abstract workflow whch wll leads to mnmum energy cost n data center wth QoS performance. 2) Choce of servces Instances and the routes: servce nstances are deployed n vrtual servers, whch can group a set of networks resource and computes resource as a logcal server. Snce there are typcally multple servce nstances, wth the same atomc servce functon, the SCE needs to select the servce nstances wth physcally adjacent locatons (cabnets, even same physcal server) so as to reduce the cost of network n data center Problem Defnton As TABLE1 shown, n ths model, a servce s defned by an ten factor group, ncludng servce dentfer, vrtual server dentfer, vrtual machne dentfer, nput, output, predcton, operaton, locaton and QoS. Defnton 1 A servce n a data center s a 10-tuple,.e., S=(SID,VsID,VmID, nput, output, precondton, postcondton, operaton, locaton, QoS) Table 1. Unts For Servce Descrpton n Mappng Table Symbol Meanng SID the servce dentfer VsID the vrtual server dentfer VmID the vrtual machne dentfer nput the data of the servce accepts output the data of the servce produces precondton the condton before servce executon postcondton the condton after servce executon locaton the functons of the servce operaton the deployment nformaton of servce QoS the QoS guarantee of servce In our model, the status of data center s a double layer of dgraph graph. As the Fgure.3 shown, the upper layer, called nterconnecton net, s a real map of the status of network and servers n the data 137

4 A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan center. And the other one, called servce net, s the map of the status of the concatenate vrtual servers for each servce executon flow, at least part of whch exctng n the data center. Fgure 3. Servce Selecton n Data Center Based on Two-layers So, there are two basc types of dgraph for data center: n n n n n Frst, the nterconnecton net G =( V, E ). V s the swtch or the computatonal node, E s the n drect connectvty. QV ( ) s the vector of the QoS requrement for V n, ncludng network delay, n bandwdth and so on. PV ( ) s power consumpton for V n n when power on. G presents current the resource allocaton of nternetwork n the data center. Second, the servce net G ser ser ser ser =( V, E ). V s the I th servce type of the work flow. E ser s the vector of the user expectaton for the servce type, ncludng computng velocty, memory capacty and ser so on. G presents the user expectatons of the resource allocaton(prmarly power consumpton) for each workflow. The power consumpton of each workflow n the data center can be expressed as formulaton (1). ABF s the abstract workflow, Pc( sj) s the energy consumpton for computng, and the Pt( s j) s the energy consumpton for communcaton traffc. P ( s ) P ( s ) P( s ), s ABF (1) j c j t j j j The problem of energy-effcency of servce selecton and executon for servce composton n data center can be attrbuted to the followng mathematcal model as formulaton (2). E t s the executon tme of user expectaton. mn{ Ps ( )} ABF DC sj ABF j subject to Qs ( ) Et, S ABF (2) over Q ( s ) 0, P( s ) 0, s ABF, ABF DC 3. Energy Effcent Framework and Servce Selecton for Servce Composton n Data Center Consderng Servce Relevance In ths secton, we approach energy-effcency of servce selecton and executon method whch mnmzes the max-mum energy consumpton n data center. We formulate the problem nto a mnmum cost flow problem (MCFP). Ths problem s to select a subgraph of the nterconnecton network to mnmze the total weght (prmarly energy) Servce Selecton of Servce Com-poston n Data Center wthout Consderaton of Energy Gven a set of possble servce temples, a user requrement, the SCE of can fnd out automated sequence of servce nstances through dgraph search. 138

5 A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan The edge drected from the vertex of servce to the vertex of servce s created f and only f the output of and nput of ntersect n some felds and all the precondtons were met. A formal defnton of servce net s gven below: Defnton 2 A servce net (SN) s a 3-tuple,.e., N ( V, E, C). 1) V { s1, s2, s3... sm}, m 0, s a fnte set of vertex, and each vertex s a servce. 2) E V V, s the ncdence relaton, whch can derve the nput and output operaton. 3) C: ej {0, }, s a functon for weght of edges. The servce selected algorthm s based on graph search as Algorthm 1 shown; the process s llustrated as Fgure.4. s 11 s 21 s 41 s 0 s 12 s 22 s 42 s 6 s 13 s 31 s 51 s 14 s 32 s 52 Fgure 4. The Selecton Process of Servce Composton 3.2. Servce Selecton wth the Consderaton of Energy based on Servce Relevance In ths secton, we assumed that servce nstances whch wll be used are deployed at the same data center. Our smple start pont s that: once several SIs need to be connected and exchanged a huge number of data, they could be deployed as close as possble wth each other, even at the same host. Energy consumpton of traffc can be reduced, so as to save energy of the data center. The dfferences of servce selecton between consder servce relevance or not are as followng: 1) The process of servce selecton wth servce relevance process of dynamc system decson. Ths process s need to collect the real network and physcal nformaton and plan how to buld and deploy the servce nstance of the data center, 2) Ths mechansm consders energy effcency as one of the most mportant bass for servce selecton, couple wth support of the QoS of servce composton on a certan extent. The SCE collect the status of network and physcal servers. Then the nterconnecton net wll be buld by SCE. We now present the energy-effcency servce selecton algorthm process based on graphsearchng as Fgure 3 shown: Then, we can model the servce selecton problem n the followng way: 1) Create a two-layer graph. The upper layer s a nterconnect net, and the other one s a servce net n whch each vertex s a servce type. 2) The SCE bulds a servce net for each customer requrement whch once SCE receved. 3) Search the servce temples and fnd out the sequences of servce types whch can fulfll the am of customer wth consderaton of servce relevance. 4) Servce net wll send the selected sequences of servce nstances to nterconnecton net. And the nterconnecton net wll confrm the resource of servce nstances s enough. 5) SCE composes and actvates the servces nstance accordng to the workflow. The servce selecton algorthm base on two layer graph search s desgned as Algorthm 2. Algorthm 2 Energy effcent servce selecton n data center Input : nterconnecton net wth network topology of the data center. Servce net wth servce templates and orgn and destnaton. Servce mappng table store the mappng of servce types and servce nstances. Output: specfc sequences of servce types n whch the energy consumpton of data center network s mnmum Functon fnd_sc(orgn, destnaton, nterconnecton_net, servce_net, templates,mappng_table) Begn graph _or= orgn graph _des= destnaton 139

6 A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan graph_servce_net= servce_net graph_ nterconnecton _net = nterconnecton _net graph_ mappng _table = mappng _table Servce_Sequences[] =Fnd_Workflow(graph _or, graph _des,templates) m = length(servce_sequences[]) for = 1:m Send_In_Net(Servce_Sequences[]) n = length(servce_sequences[]) for j = 1:n Workflow_Energy[]+= Compute_Energy(Servce_Sequences[j]) end for Energy(Servce_Sequences[]) = Workflow_Energy[] Workflow _selected = selected_mn (Energy(Servce_Sequences[])) end for return workflow= Workflow _selected End-Functon 4. Expermental Evaluaton In ths secton, we evaluate the performance of energy effcent servce composton selecton method usng large-scale smulatons. In our smulaton experments, we use cloudsm to smulate the mechansm n a data center, whch contans 100 servers. We use a topology generator to generate graphs to represent the topology of data center. For comparson, we also mplement three mechansms wth random deployment, and our energy effcency method. We generate dynamcally the network latency, bandwdth, servce tme, servce cost and some other dynamc QoS values of custom requrements. Fgure.5 shows the energy value of the data center dfferent algorthms wth the same servce requrement. The results llustrate that energy-effcent servce selecton for servce composton n data center can consstently acheve better performance. 5. Conclusons Fgure5. Utlty Value Comparson for Data Center Ths paper presents a novel energy-effcent servce selecton algorthm for servce composton n date center wth consderaton of servce relevance. The purpose of ths algorthm s to buld more 140

7 A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan flexble and power-effcent vrtual server accordng to the dynamcally nterconnecton network n data center. Moreover, ths algorthm provdes a framework of mappng system n data center. Ths algorthm expand mnmum cost flow algorthm. Our smulaton demonstrated the feasblty and effcency of the source-awareness algorthm. In the future, we ntend to mprove the ablty to mutual percepton between nterconnecton network and servce composton engne n data center, wth more reasonable negotaton, resource utlzaton. Acknowledgements Ths paper s supported by Major Program of Natonal Natural Scence Foundaton of Chna (No ), Natural Scence Foundaton of Bejng (No ), and Foundaton Scences Bejng Jaotong Unversty (No. 2011YJS014). 10. References [1] Balga, J.; Ayre, R.W.A.; Hnton, K.; Tucker, R.S.;, "Green Cloud Computng: Balancng Energy n Processng, Storage, and Transport," Proceedngs of the IEEE, vol.99, no.1, pp , Jan [2] Rakpong Kaewpuang, Juta Pchtlamken, "Buldng a Servce Orented Cloud Computng Infrastructure Usng Mcrosoft CCR/DSS System", JNIT, Vol. 2, no. 1, pp , 2011 [3] Masanet, E.R.; Brown, R.E.; Shehab, A.; Koomey, J.G.; Nordman, B.;, "Estmatng the Energy Use and Effcency Potental of U.S. Data Centers," Proceedngs of the IEEE, vol.99, no.8, pp , Aug [4] P. Mahadevan, P. Sharma, S. Banerjee, and P. Ranganathan, A PowerBenchmarkng Framework for Network Devces, n Proceedngs of IFIPNetworkng, May [5] Qnghu Tang; Gupta, S.K.S.; Varsamopoulos, G.;, "Energy-Effcent Thermal-Aware Task Schedulng for Homogeneous Hgh-Performance Computng Data Centers: A Cyber-Physcal Approach," Parallel and Dstrbuted Systems, IEEE Transactons on, vol.19, no.11, pp , Nov [6] Lysne, O.; Renemo, S.-A.; Skee, T.; Solhem, A.G.; Sodrng, T.; Huse, L.P.; Johnsen, B.D.;, "Interconnecton Networks: Archtectural Challenges for Utlty Computng Data Centers," Computer, vol.41, no.9, pp.62-69, Sept [7] Ka Chen; Chengchen Hu; Xn Zhang; Ka Zheng; Yan Chen; Vaslakos, A.V.;, "Survey on routng n data centers: nsghts and future drectons," Network, IEEE, vol.25, no.4, pp.6-10, July- August [8] Shanbhag, S.; Wolf, T.;, "Automated composton of data-path functonalty n the future nternet," Network, IEEE, vol.25, no.6, pp.8-14, Nov [9] Zhu jngy, "A Semantcs Constraned Net based on CPN for Cloud Workflow Modelng", IJACT, Vol. 3, no. 7, pp , 2011 [10] Kajun Ren; Nong Xao; Jnjun Chen;, "Buldng Quck Servce Query Lst Usng WordNet and Multple Heterogeneous Ontologes toward More Realstc Servce Composton," Servces Computng, IEEE Transactons on, vol.4, no.3, pp , July-Sept