Xu Gaochao, Dong Yunmeng, Fu Xaodog, Dng Yan, Lu Peng, Zhao Ja Abstract A heurstc task deployment approach for load balancng Gaochao Xu, Yunmeng Dong, Xaodong Fu, Yan Dng, Peng Lu, Ja Zhao * College of Computer Scence and Technology, Jln Unversty, Changchun 300, Chna College of Computer Scence and Engneerng, Changchun Unversty of Technology, Changchun 300, Chna Receved February 04, www.ts.lv The load balancng strategy, whch s based on the msson deployment, has become a hot topc of green cloud data centre. For the queston that currently the overloaded physcal hosts n the cloud data centre causes the load mbalance of the whole cloud data centre, the proposed makes an ntensve study whch s about the select locaton queston of the deployment tasks on the physcal host and then ths proposed a new heurstc method whch s called LBC. Its man dea conssts of two parts: Frst, based on the functon, whch denotes the performance ftness of physcal hosts, t conducts a constrant lmt to all physcal hosts n cloud data centre. So a task deployment strategy wth global search capablty s acheved. Secondly, usng clusterng methods can further optmze and mprove the fnal clusterng results. Thus, the whole way acheves the long-term load balancng of the cloud data centre. The results show that compared wth the conventonal approach, LBC sgnfcantly reduces the number of falure of the deployment tasks, mproves the throughput rate of the cloud data centre, optmzes the performance of external servces of the data centre, and performs well n terms of load balancng. Besdes, t makes the operaton of cloud data centres be more green and effcent. Keywords: load balancng strategy, cloud data centre, task deployment, LBC, clusterng Introducton Cloud computng [, ] s the focus of research topc currently whch s the most promsng and valuable research drecton followng the utlty computng, grd computng and dstrbuted computng. Cloud computng provdes users wth nfrastructure, platform and software servces accordng to user s needs through the Internet. Infrastructure as a Servce (IaaS) s the foundaton of cloud computng, whose key s to make the data centre cloud computng resources to be a resource pool through vrtualzaton technology. Besdes, t allocates accordng to the task specfcatons and resource requests, whch are submtted by users. In addton, t provdes elastc physcal or vrtual computngtorage and network resources. A large number of physcal hosts, whch are deployed by the cloud data centre, provde servces for users. However, each physcal host s resource remanng amount s changng all the tme. Therefore, t cannot guarantee to place every task on the physcal host, whch has the largest remanng amount of resources. Currently, load balancng s the hot ssue n the doman of the cloud data centre study. In order to further optmzng load balancng n the cloud data centre, ths proposed presents a heurstc dea [3], load balancng strategy, whch ams at fndng the physcal host whose deployment performance s optmal. And detals are as follows: Frst, gvng a constrant value whch s based on the resource amount of requested tasks. Then cluster the physcal hosts, whch are greater than the constrant value n the cloud data centre. Secondly, formng a set of physcal hosts by clusterng whose smlartes are wthn a certan threshold. The collecton of physcal hosts got after clusterng s the physcal hosts collecton havng the optmal deployment performance, whch we want to fnd. Fnally, place the tasks, whch need to be processed nto the physcal hosts whch are n the collecton to conduct deployment. Clusterng the physcal hosts n the data centre exactly s the process of fndng the physcal hosts, whch have optmal deployment performance. Therefore, through our deployment task strategy, t cannot only acheve load balancng n the cloud data centre, but also provde effcent external servce performance for users. Ths paper ams to acheve long-term load balancng n the cloud data centre and provde users wth effcent external servce performance. And achevng long-term load balancng n the cloud data centre must by means of deployng the task request to the resource pool n the cloud data centre effcently and ratonally. Therefore, t can acheve load balancng n the cloud data centre and mprove the effcency of the cloud data centre. Furthermore, t can show the excellent external servce performance of cloud data centre to users. The load balancng strategy proposed cannot only fnd the physcal hosts, whch have optmal deployment performance effcently, but acheve long-term load balancng n the cloud data centre. * Correspondng author e-mal: zhay049@sna.com 3 Mathematcal and Computer Modellng
Other parts of ths paper are organzed as follows: In the second part, we brefly descrbe the current work that s related to the method, whch can acheve the load balancng of cloud data centre. In the thrd secton, we frst pont out the premse, whch proposed n our questons, and then ntroduce the desgn and mplementaton process of our algorthm n detals. In the fourth secton, we wll gve the experments and results, and prove that the algorthm we proposed has hgh effcency. The ffth part, we summarze the full paper and future work s put forward. Related works Load balancng has been a hot research topc of cloud data centre [4] and ts goal s to ensure that every computng resource can process tasks effcently and fast, mprove the utlzaton of resources ultmately. The queston s present n a cloud computng envronment. When there are some task requests n the cloud data centre, these tasks request wll be deployed to the optmal physcal host of the cloud data centreo that the computng performance of cloud computng centres can acheve optmal, whle cloud data centres can acheve the load balancng of entre network. Researchers have proposed a seres of statc, dynamc and mxed schedulng polces. In addton, there are also some studes usng lve mgraton technology of vrtual machne to meet the cloud data centres requested tasks whch nclude performance requrements and load lmtaton. In fact, most problems are ust deployng the requested tasks to the cloud data centre s physcal hosts. Exstng load balancng strateges are generally dvded nto two categores: statc load balancng and dynamc load balancng. Statc load balancng schedulng algorthm [5-8] are commonly used round robn, weghted round robn, least connecton method, weghted least connecton method and so on. These statc algorthms only use some statc nformaton, whch cannot solve dynamc load changes among servers n cluster effectvely and ther adaptve ablty s poor. Currentlyome of the most open-source IaaS platform most use statc algorthm to conduct resource schedulng. For example, Eucalyptus [9] platform uses round robn to assgn vrtual machnes to dfferent physcal hosts n sequence to acheve load balancng. In Lterature [0], We Q et al. used the weghted mnmum lnk algorthm, whch means that dfferent weghts ndcate the performance of the physcal host. Then, the vrtual machne wll be allocated to the physcal host, whch has the smallest rato of the number and weght. The advantage of statc schedulng algorthm s that t s smple to do. But facng the large-scale cloud data centre whose heterogeneous resources are strong and users consstent demand, load balancng effect s not deal. Dynamc load balancng [-3] s a NP-complete problem whch s a classc combnatoral optmzaton problem. It s manly used n the feld of dstrbuted Xu Gaochao, Dong Yunmeng, Fu Xaodog, Dng Yan, Lu Peng, Zhao Ja parallel computng, and ts man obectve s how to dstrbute the load more ratonally among multple computers to avod some phenomenon of calculaton node overload and lght load. Thus, overall system performance can be mproved. Addtonal communcaton overhead produced n the process of DLB wll reduce the dynamc load balancng system performance. And wth the ncrease of network latency among each node, the nfluence of the restrctng DLB performance of addtonal communcaton overhead wll further ncrease. Therefore, how to reduce communcaton overhead furthest among each node n the process of DLB becomes an mportant problem, whch wll nfluence the performances of DLB. Now amng at the problem of reducng addtonal communcaton overhead n the process of DLB, the soluton s manly usng greedy algorthm to process. The LRS algorthm, whch s put forward n Lterature 9 usng the lght load preferentally receved allocaton pattern. In Lterature [4], Lau et al. ntegrated two strateges whch are heavy load prorty and lght load prorty. They put forward an adaptve load dstrbuton algorthm, whch helps reduce the load balancng communcaton overhead effectvely. Usng the greedy algorthm can solve the problem of load dstrbuton. Howevereveral algorthms above cannot meet greedy choce performance and sub-optmal structural property at the same tme. Therefore, load dstrbuton program was often local optma. And the effect of solvng the problem of load dstrbuton under certan specal crcumstances s not deal. Cloud data centre cannot reach the entre network load balancng. Vrtual machne mgraton placement strategy s the most wdely used strategy to acheve cloud computng [5, 6] data centre load balancng currently. VMware load balancng soluton s DRS (Dstrbuted Resource Schedulng) [7]. When DRS select the physcal host for the vrtual machne, t wll check the load status of each physcal host and choose the placement method to reduce the overall load mbalance. And n the process of runnng a vrtual machne, DRS wll contnue to montor the load status of the cluster and use VMware VMoton technology to perform lve mgraton of vrtual machnes among dfferent physcal servers. Thus, t can ensure load balancng and effcent use of physcal resources of the entre cluster. 3 LBC algorthm desgn In IaaS cloud data centre, when users have requests, the system wll deploy the task request to the physcal hosts, whch are n the resource pool of a cloud data centre. In general, cloud data centres wll select physcal hosts randomly to deploy. When the requested resources are greater than the physcal hosts remanng resources, physcal hosts cannot deploy the task. When the requested resources are n proxmty to the physcal hosts remanng resources, t wll cause overload of physcal hosts. Thus, t wll cause load mbalance n the 3 Mathematcal and Computer Modellng
cloud data centre and result n decreased effcency and ncreased energy consumpton. Obvously, wth regard to the cloud data centre, dfferent deployment task strateges wll cause dfferent load allocaton n entre system. There s no doubt that the optmal deployment task strategy can make the entre cloud computng system produce the effect of load balancng. Therefore, t s necessary to desgn and mplement an effcent and loadbalancng deployment task strategy n the cloud data centre. 3. IMPLEMENTATION OF LBC ALGORITHM The mplementaton of the LBC algorthm: Step : Assumng the number of physcal hosts n the data centre s n. We need to do a constrant to all physcal hosts n the data centre. And n order to meet the physcal hosts performance constrants. We treat the physcal hosts remanng amount of resources L as a metrc. It s defned as follows: Xu Gaochao, Dong Yunmeng, Fu Xaodog, Dng Yan, Lu Peng, Zhao Ja S, whch s got after comparng n physcal hosts wth the constraned value s S = { s 3. m }, m n. Step : We can get the performance values of each physcal host based on the ftness functon of physcal hosts performance. By restrctng the constraned value, we put the physcal hosts n the data centre whose performances are relatvely good nto the set S. Regard the remanng of the physcal host s CPU as the physcal host s property. Suppose S = { s 3, m } as the set whch contans m physcal hosts. We arrange CPU remanng of physcal hosts n the set S n descendng order. Large CPU remanng of physcal hosts s arranged n the front. Supposng that s s the physcal host, whch has the largest CPU remanng, we regard s as the class-centre. The equatons that calculate the smlarty are as follows: d k k, (4) k d(s,s ) (s s ) L, () Lc Lmem. () s(s,s ), (5) d(s,s ) L shows the remanng computng resources of physcal host node I, whch mean the usage of CPU and memory usage. L c s the remanng of CPU. L mem s the remanng of memory. s the weght of CPU. s the weght of memory. The value of and are obtaned through BP neural network study. Accordng to the ftness functon () and () of the physcal hosts performance, t generally obtans the montorng data of the physcal hosts varous performance n the entre data centre through SNMP (smple network management protocol), ncludng CPU and memory data. The remanng resources of n physcal hosts n the cloud data centre can be calculated. The constrant value s defned as: The total amount of resources receved of task request collecton wthn tme t, namely: L req n L. (3) tk In ths equaton, L req s the total amount of resources of task request collecton, L tk s the resource of task n the task request collecton. There defnes an empty set S = {}. Accordng to the equaton (3), L req can be calculated. When there s an nequaton, L Lreq, a host wll be put nto set S, otherwse, we wll contnue to fnd. The set s s a property of the physcal host. It can represent the physcal host s CPU remanng. So accordng to the equatons (4) and (5), the smlarty of the physcal host and the physcal host can be calculated. s (s,s ) s the smlarty of s and s. s(s,s ) Step 3: Regardng (L L ) c c. (6) s as the class-centre, gvng a threshold Smlarty value U threshold, accordng to the smlarty, we calculate the smlarty of s and each element of the set S. If the Smlarty smlarty s greater than the threshold value U threshold, we wll add ths element nto the new set S and not put the class-centre nto S. Then the set S selects class-centre accordng to the remanng of physcal host CPU n descendng order and calculate apart the smlarty wth the elements of S. Next put the threshold whch s greater than of the set Smlarty U threshold nto the set S. When the elements S does not change, the teraton ends. The fnal clusterng result s the set q m n. S. S {s,s...s }, q 33 Mathematcal and Computer Modellng
Step 4: It puts the task request receved from the data centre nto the collecton S of physcal hosts, then physcal hosts n the collecton S process the task set n a collecton of physcal hosts to process the requested task collecton. After processng, the results wll be returned to users. From the physcal hosts n the collecton S startng processng the task untl the processng s completed, the perod of tme s recorded as t. The task requrements that the data centre receves wthn tme t wll be the next task to be processed. Step 5: Repeat the above process. 3. MODEL OF LBC ALGORITHM Xu Gaochao, Dong Yunmeng, Fu Xaodog, Dng Yan, Lu Peng, Zhao Ja ncrease of two deployment methods wth the ncreasng number of requests tasks and the Make-Span of the collecton of requested tasks requests wll also ncrease. As can be seen from Fgure, comparng wth random deployment, the LBC algorthm we proposed has a smaller Make-Span under the same condton. Ths experment llustrates that the LBC algorthm not only has good load balancng effect, but has relatvely good Make-Span. Overall, the target of LBC algorthm s also n lne wth the dea of heurstc algorthms. It manly because the soluton that heurstc dea fnds every tme s not always the optmal soluton. But by constant fndng and revsng, t can get closer to the optmal soluton untl nfntely close to the optmal soluton. And hs process meets the goal of algorthm. From the vew of the goal of algorthm, we are commtted to acheve the load balancng of data centre. An teraton of the algorthm can acheve load balancng. So t needs repeated teratve algorthm, and fnd the optmal physcal host after each teratve. Therefore, users requested tasks can be dsposed by the optmal physcal host n the data centre. In ths way, after repeated teratve algorthm, each set of physcal hosts we found can get close to the best performance. Data centres can quckly process tasks requested by the user. Thus, the data centre tends to be load-balancng and fnally t acheves load balancng. Ths s a long process, and LBC algorthm ensures that the process can get good results wthn a reasonable tme. 4 Evaluaton Ths paper uses CloudSm smulator to smulate a dynamc cloud data centre. It supports for dynamc creaton of dfferent types of enttes at run-tme and t can add and delete data centre. In CloudSm platform, t creates a resource pool wth 00 physcal hosts. These hosts have dfferent computng resources and 50 dfferent tasks request resources. They need dfferent CPU and memory of physcal host. LBC model we proposed calls and gets resource nformaton and status of physcal hosts n the cloud resource pool regularly. In ths secton, through load balancng degree, make-span, and external servce performance, we compared the LBC deployment strateges we proposed wth random deployment strateges and do some experments. The results shown below: In ths scenaro of experments, we compared the Make-Span of LBC and random methods. Make-Span s the completon tme of computng tasks. The results shown n Fgure, t can be seen from the fgure that the FIGURE Comparson of Makespan In ths set of experments, we compare the changes of load balancng n the cloud data centre, whch nfluenced by LBC method and stochastc methods over tme. As can be seen from Fgure, wth the tme ncreasng durng deployments, the load balance degree of stochastc methods and LBC method decrease gradually. The load balance degree of tradtonal random deployment strateges s always greater than LBC method. It s because the LBC method can quckly fnd the optmal physcal host based on the requred CPU resource amount of a requested task. To a certan extent, t ensures the CPU utlzaton of physcal host s much good. From the expermental results, the LBC method we proposed has better load balancng effect. Thus, the resource utlzaton of the cloud data centre s more effectvely mproved. And t ndrectly saves the power consumpton for the cloud data centre. FIGURE Comparson of load balancng degree 34 Mathematcal and Computer Modellng
The thrd set of experments verfes the LBC method from the eternal servce performance of the data centre after the deployment of two methods. They selected the throughput as the evaluaton crtera of the external servce performance of the data centre, because the throughput s usually the overall evaluaton of a system and the ablty of ts assembly unts requested ablty to process transmsson data. Expermental results shown n Fgure 3, t can be seen from the fgure that usng two dfferent deployment methods, external servce performances of the data centre are dfferent. FIGURE 3 Comparson of external servce performance After the random deployment, computng performance of the data centre s much good. Wth the ncrease of response tme, eternal servce performance has a waved trend, and no stablty. But usng the LBC method to deploy tasks, the ntal external servce performance of the data centre s not as good as that of the data centre by usng the random methods. Wth the ncrease of response tme, the external servce performance of the data centre gradually stablzes. By comparng the performance of external servce performance of the data centre, t can be concluded that usng LBC method can be more stable and effcent than random deployment. Xu Gaochao, Dong Yunmeng, Fu Xaodog, Dng Yan, Lu Peng, Zhao Ja 5 Concluson and future work Based on summarzng the related work, ths task proposes a new load balancng strategy, LBC, whch s based on task deployment and gves the man dea ncludng process mplementaton and evaluaton. It uses heurstc deas, whch are based on clusterng. In these heurstc deas, frst t calculates the total amount of resources of requested tasks accordng to the ftness functon of physcal hosts performance. Then, t compares the amount of tasks wth the remanng resources of the physcal host n the cloud data centre. And t makes the physcal host whch s greater than the total amount of resources to be the clusterng obect. Therefore, LBC has better search capablty and adaptve capacty at the begnnng. To assess LBC method, there are several experments done at CloudSm platform. Through four experments, the result shows that LBC method can deploys the real-tme task requests to the resource pool of cloud data centre faster and effcently. LBC acheves the long-tme load balancng and hgheffcency computng capacty of the cloud data centre. It mnmzes the number of falures of deployment tasks n the cloud data centre. And to some extent, t mproves the throughput of the cloud data centre. In those LBC methods proposed, there are some open questons, whch need further research, and some expermental questons, whch need a lot of experments to get a much good soluton. The value of CPU weght,, and memory weght, an emprcal queston. It needs several experments to obtan the optmal values so that we can get the equaton:.therefore, LBC method can be more effcent and feasble. In ths context, all the parameters are set to the approprate value. To further mprove the performance of LBC, We plan to study the robustness of LB - C n the next step. LBC method should be able to choose one to more physcal hosts to deploy tasks n the collecton after clusterng. Thus, the cloud data centre and users can get the maxmum beneft. 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[] You T, L W, Fang Z, et al 04 Performance Evaluaton of Dynamc Load Balancng Algorthms TELKOMNIKA Indonesan Journal of Electrcal Engneerng (4) [3] Bah J M, Contassot-Vver S, Couturer R 005 Dynamc load balancng and effcent load estmators for asynchronous teratve algorthms IEEE Transactons on Parallel and Dstrbuted Systems, 6 (4) 89-99 [4] Lau S M, Lu Q, Leung K S 006 Adaptve load dstrbuton algorthms for heterogeneous dstrbuted systems wth multple task Xu Gaochao, Dong Yunmeng, Fu Xaodog, Dng Yan, Lu Peng, Zhao Ja classes IEEE Transactons Parallel and Dstrbuted Computng 66() 63-80 [5] Armbrust M, Fox A, Grffth R, Joseph A D, Katz R, Konwnsk A, Lee G, Patterson D, Rabkn A, Stoca I, Zaharaa A 00 A vew of cloud computng Communcatons of the ACM 53(4) 50-58 [6] Moreno-Vozmedano R, Montero R S, Llorente I M 03 Key Challenges n Cloud Computng: Enablng the Future Internet of Servces Internet Computng IEEE 7(4) 8-5 [7] Mur S, Tavlla R, Verghese B 0 Vmware Technca Journal [EB/OL] () Authors Gaochao Xu, Wuhan, born n 966 Current poston, grades: Changchun, Professor and PhD supervsor of College of Computer Scence and Technology, Jln Unversty, Chna. Unversty studes: BS, MS and PhD on College of Computer scence and Technology of Jln Unversty n 988, 99 and 995 Scentfc nterest: Cloud Computng, Moble Cloud Computng Publcatons: SCI 0 Experence: more than 0 natonal, provncal and mnsteral level research proects of Chna Gaochao Xu was. Research nterests: dstrbuted system, grd computng, cloud computng, Internet of thngs, nformaton securtyoftware testng and software relablty assessment, etc. Yunmeng Dong, born n 989, n Yushu of Jln provnce of Chna Current poston, grades: Changchun, Master Unversty studes: bachelor degree n computer scence at Changchun Unversty of Technology (0), a postgraduate canddate of the college of computer scence and technology of Jln Unversty Scentfc nterest: Vrtualzaton, Cloud Computng, Moble Cloud Computng Publcatons: EI Research nterests: dstrbuted system, cloud computng and vrtualzaton technology Xaodong Fu, ChangChun Current poston, grades: Senor engneer n the College of Computer Scence and Technology, Jln Unversty of Chna. Unversty studes: BSc degree from Jln Unversty. Research nterests: Dstrbuted System, Grd Computng, Cloud Computng, Internet Thngs Publcatons: 4 research artcles Yan Dng, born n 988, n Ychun of Helongang provnce of Chna Current poston, grades: Changchun, Master, a postgraduate canddate of the college of computer scence and technology of Jln Unversty Unversty studes: bachelor degree at Jln Unversty n 0 Scentfc nterest: Vrtualzaton, Cloud Computng, Moble Cloud Computng Publcatons: SCI Research nterests: dstrbuted system, cloud computng and vrtualzaton technology Peng Lu, born n 990, n Jx of Helongang provnce of Chna Current poston, grades: Changchun, Master a postgraduate canddate of the college of computer scence and technology of Jln Unversty Unversty studes: bachelor degree at Daqng Normal Unversty n 03 Research nterest: Vrtualzaton, dstrbuted system Cloud Computng, Moble Cloud Computng, SDN Ja Zhao, born n 98, n Changchun of Jln provnce of Chna Current poston, grades: Changchun, Doctor, PhD canddate of the college of computer scence and technology of Jln Unversty Unversty studes: Scentfc nterest: Vrtualzaton, Cloud Computng, Moble Cloud Computng Publcatons: SCI 4 Research nterests Vrtualzaton, Cloud Computng, Moble Cloud Computng nclude dstrbuted system, cloud computng, network technology Experence: partcpated n several proects 36 Mathematcal and Computer Modellng