An Approach to Optimized Resource Scheduling Algorithm for Open-source Cloud Systems

Transcription

1 The Ffth Annual ChnaGrd Conference An Approach to Optmzed Resource Schedulng Algorthm for Open-source Cloud Systems Ha Zhong 1, 2, Kun Tao 1, Xueje Zhang 1, 2 1 School of Informaton Scence and Engneerng, Yunnan Unversty, Kunmng, P. R. Chna, Hgh Performance Computng Center, Yunnan Unversty, Kunmng, P. R. Chna, 6591 ha.zhong@foxmal.com, kun.tao@foxmal.com, xjzhang@ynu.edu.cn Abstract Based on the deep research on Infrastructure as a Servce (IaaS) cloud systems of open-source, we propose an optmzed schedulng algorthm to acheve the optmzaton or sub-optmzaton for cloud schedulng problems. In ths paper, we nvestgate the possblty to allocate the Vrtual Machnes (VMs) n a flexble way to permt the maxmum usage of physcal resources. We use an Improved Genetc Algorthm (IGA) for the automated schedulng polcy. The IGA uses the shortest genes and ntroduces the dea of Dvdend Polcy n Economcs to select an optmal or suboptmal allocaton for the VMs requests. The smulaton experments ndcate that our dynamc schedulng polcy performs much better than that of the Eucalyptus, Open Nebula, Nmbus IaaS cloud, etc. The tests llustrate that the speed of the IGA almost twce the tradtonal GA schedulng method n Grd envronment and the utlzaton rate of resources always hgher than the open-source IaaS cloud systems. Keywords: cloud computng; resource schedulng; genetc algorthm; grd computng; IaaS I. INTRODUCTION Cloud computng has become another buzzword after web 2. [1]. Someone consders cloud computng as web 3. or Grd 2. [2]. Anyway, cloud computng typcally nvolves the provson of dynamcally scalable and vrtualzed resources as a servce over the Internet. Many types of cloud computng systems offer a new programmng target for scalable applcaton developers and have ganed popularty over the past few years. Clouds n general provde servces at several dfferent levels, Infrastructure as a Servce (IaaS), Platform as a Servce (PaaS), Software as a Servce (SaaS), etc [3]. However, most cloud computng systems n operaton today are propretary, rely upon nfrastructure that s nvsble to the research communty, or are not explctly desgned to be nstrumented and modfed by systems researchers [4]. Though, there are many opensource cloud systems for researchers emerges as the development of cloud computng. For IaaS, there are some popular open-source cloud systems, such as Eucalyptus [4], Open Nebula [5], Nmbus [6], etc. Resource schedulng s a key process for IaaS clouds. IaaS clouds commonly take vrtual machne (VM) as schedulng unt, be allocated to heterogeneous physcal resources. To decde the allocaton, Eucalyptus uses Greedy (Frst ft) and Round robn algorthm [4], Open Nebula uses queung system, advanced reservaton and preempton schedulng [5], and Nmbus uses some customzable tools lke PBS and SGE [6]. In the above schedulng approaches, Greedy and Round robn that provded by Eucalyptus s a random method to select adaptve physcal resources for the VM requests that not consderng maxmum usage of physcal resource. The queung system, advanced reservaton and preempton schedulng polces are not consderng the utlzaton rate of physcal resource. For customzable strateges, are basc queung systems that do not provde automated optmal resource schedulng and beng ndetermnate. Snce cloud computng s an on-demand computng paradgm, mmedate and automated leasng s a favorte schedulng strategy. And none of the above strateges s both beng an automated schedulng and consderng the maxmum usage of resources. To acheve an optmal or suboptmal VM allocaton for mmedate cloud servces, GA s a good choce whch has already beng used n the Grd envronment and many other schedulng problem n other felds [7], [8]. But the IaaS cloud envronment s somehow dfferent from the Grd s. Hence, we propose an Improved Genetc Algorthm (IGA) to schedule the resources. In our model, we use a superscheduler and follow the FCFS prncple [11]. We start the schedulng for three steps n general. Frst, we set a avalable resource lst and VM(s) request lst, update them at the ntate tme and each tme new VM(s) request come or VM(s) beng shutdown or new physcal resource change s beng detected; then, to determne a optmal allocaton we use the GA algorthm, and we use the shortest genes to mprove the evoluton speed and ntroduce a varable Dvdend Polcy n Economcs as the ftness functon. Fnally, the scheduler launches the specfed VM(s) on specfed physcal resources, and then renews the request lst and the avalable resource lst. However, not every VM(s) request wll be accepted by the scheduler snce there may be not enough computng resource avalable n some clouds. Therefore, f there s no /1 $ IEEE DOI 1.119/ChnaGrd

2 suffcent resource for the request, the scheduler wll reject t. We have developed a smulator to compare our approach wth the Greedy and Round robn algorthm whch are provded by Eucalyptus, and an average level of confgurable schedulng methods lke Open Nebular and Nmbus on the utlzaton rate. And we compared the performance wth the tradtonal GA (TGA) algorthm of the Grd envronment. The schedulng model we developed performs much better than the current schedulng model whle fndng an optmal resource allocaton. And the GA method we developed n cloud envronment s half the evoluton tme of TGA n the Grd envronment. II. SCHEDULING POLICY Accordng to the popular IaaS cloud systems, the computatonal resources are usually connected by LAN. Hence, n ths paper we don t dscuss the topology of networks. The cloud s somehow centralzed and we just need to consder the superscheduler. Fg.1 llustrates the standard-based open-source cloud archtecture, and the super scheduler s always at the Fgure 1. Standard-based open-source cloud vrtual nfrastructure manager n reservor top lawyer [14]. Let us consder a set of VM requests, a set of nterconnected computng nodes connected by LANs. The computng nodes are dfferent knds of ordnary PCs, servers, and even hgh performance clusters. And cloud provdes all knds of machnes t possesses n forms of vrtual machne that clents can vst t through Internet as a servce. In ths paper, we take the CPU speed (usually the number of cores), Memory capacty and Hard Drve capacty n consderaton, whch s most of the exstng IaaS cloud systems do. For Eucalyptus, t uses Greedy (Frst ft) and Round robn schedulng strateges. Greedy query all the computatonal resources from the frst to the last node untl fndng a sutable node every tme new request comes and deal wth them one by one for multple requests. Round robn records the last poston of the scheduler vsted. And the scheduler starts from the last vsted poston next tme new request(s) come(s) meanwhle the resources are consdered as a crcular lnked lst. Open Nebular uses Hazea [18], an opensource VM-based lease management archtecture as the scheduler and provdes the queung system, advanced reservaton, preempton, mmedate lease strateges, etc. All these polces pay more attenton to when but neglect how, the utlzaton of resources. Nmbus can be confgured to use famlar schedulers lke PBS (Portable Batch System) or SGE (Sun Grd Engne) to schedule vrtual machnes [15], [16], [17]. PBS s a queung system and SGE uses Job Schedulng Herarchcally (JOSH), both do not have a good utlzaton of resources. Snce the exstng schedulng algorthms don t have good consderaton of utlzaton. We propose an optmal schedulng polcy for the open-source IaaS cloud envronments. The optmal scheduler reallocates resources for queung or nstant VM requests each tme ether the resource status change or new VM requests come. Hence, t s also an automated and nstant scheduler. We have desgned a schedulng strategy usng an IGA to acheve an optmal or suboptmal resources dstrbuton. Usng the IGA algorthm, the nstances wll be scheduled to run on proper physcal machnes so that t wll have a hgher performance and better occupaton coeffcent. In our smulatons we have smulated a FCFS super scheduler for the cloud. The goal of the superscheduler s to fnd out the allocaton sequence to each computng node n a cloud so that nstances run on proper physcal computers. Multple nstance requests wll be allocated to the partly or entre resources. III. THE SCHEDULING ALGORITHM The automated schedulng model s beng dvded nto three steps. Frst, the scheduler updates the avalable resource lst when allocaton or de-allocaton happens and update the VM request lst when each tme new VM requests come. Then, the scheduler uses an IGA algorthm to fnd out a ftness and economcal allocaton. Last, the cloud launches the correspondng VMs at the physcal resource and suspends the VMs when the leasng tme s up. If there s not enough resource for the VM request, the scheduler wll reject the request automatcally. And the user needs to resubmt hs (her) VM request. But t s not frequent for publc clouds. Therefore, the most mportant step s to fnd the ftness allocaton usng IGA. To run a GA n ths combnatoral optmzaton problem, the two most mportant factors are Chromosome representaton and 125

3 the ftness functon. Snce there s some common sense of crossover, mutaton rate, etc. We wll llustrate the chromosome and ftness functon n detal and the crossover, mutaton on brefly. A. Chromosome representaton Sometmes VM request s also called nstance request (IRs). Hence, we wll take IRs for short n the followng sectons. We chose a drect representaton of solutons, wth a chromosome encodng a scheduler. So for N IRs to be allocated on M computng nodes, the chromosome wll hold N genes representng the IRs whch wll be scheduled n sequence. Thus, contrast to the 2 * N genes n grd computng [5], t s half the length. See Fg. 2 and Fg. 3. Fgure 2. Chromosome representaton n grd computng Fgure 3. Chromosome representaton n cloud computng Let s consder N IRs need to be allocated. And suppose there are M dle or partly dle computng nodes avalable n a cloud. Frst, we need to number the computng nodes wth ntegers. For M computng nodes, we wll gve each of them a unque nteger arrange from to M-1, and mark each of the computng node s CPU cores, memory sze and hard dsc sze, e.g. (: 2 cores, 2M, 4G; 1:1 core, 1M, 2G). Second, the GA algorthm holds the IRs sequentally n a lst and records ther content, e.g. (1 core, 512M, 1G; 2 cores, 1M, 2G). Then, the IGA wll produce a chromosome usng N nteger genes. And the value of the genes s lmted from to M-1. And we set an approprate populaton sze and max evolutons for the evoluton. The algorthm wll generate all possble sequences of these nodes. For nstance, 1, 3, 7 and 3, 3, 7 are possble. Thus, the decodng s very smple. The decoded nteger s the sequence number of the computng node. And the scheduler wll allocate certan physcal resources for the IRs from left to rght by value of the nteger genes. Snce we don t advocate launch multple nstances on one node. A vald sequence must have exactly one occurrence of each node. And each job wll be allocated only once. B. Operators In general, the GA ncludes three operators, replcaton, crossover and mutaton. Snce the encodng schema s set properly. We just need to use the standard genetc operators. And the rates of crossover and mutaton are standard ones whch are approprate for ths model too. C. Constrans As we mentoned above, we consderng one computng node just launch one nstance at a schedulng tme. And one job wll be allocated for one tme. But t s not means one physcal machne can only run on one VM. We wll gather all the dle resources of each computng node, and each of them wll be appearng at the schedulng tme except that the node do not have any free resource to hand out. For example, a 4-core computng node that half of ts CPUs s n occupyng. We wll take the rest part of ts CPUs n consderng at schedulng tme. D. Ftness functon The ftness algorthm s a key process for GA. It wll decde the tendency of the evoluton. In our model, we take the Dvdend Polcy [1] n Economcs as a reference. There are two parts n a Dvdend Polcy, the captal gans and the dvdend yeld. The captal gans represent the ftness value of each gene whle the dvdend yeld s an overall condton of the gene duplcaton and so on. See formula (1). K = captal gans + dvdend yeld (1) Formula (1) s a general dea of Dvdend Polcy n Economcs. Whle n our stuaton, we brng n a max ftness value and take a deducton method. Hence, t wll keep none negatve ftness value. Formula (2) s a modfed Dvdend Polcy. The man dea s that gven a total value of the whole genes. And dvde the value to each part of the allele dynamcally. We gve a sensble max value as the most ftness one. For not calculate a negatve value, we usng a subtracton method to deduct the napproprate parts. And the deducton part wll not exceed the max value n reasonable stuatons. F = Max_ Ftness K (2) n 1 2 j = j= K = C + D (3) Max _ Ftness IRj IRj + 1 ( >= 1) 3* IRCount NodeIR j NodeIR j C j = IR j IR j (4) 1 ( < 1 ) Node Node IR j IR j Max _ Ftness D = * Dups (5) IRCount In formula (2), F s the ftness value of a chromosome after Max_ Ftness mnus K, K s a certan value of the resources that current chromosome wasted usng the Dvdend Polcy. 126

4 n For K n formula (3), t s composte by 1 2 C j = j= (captal gans) and D (dvdend yeld). From formula IRj (4), the value of C j s depend on the result of NodeIR j and the more a stuaton fulfll the IRs the less t wll be deducted. Note that IR j s consttute by the request of CPU process capablty (the number of CPU cores), memory sze and hard dsc sze. And Node represents the actually CPU process capablty, free memory and free hard dsc volume of a target computng node. Thus, n that way a fulfll target node wll be encouraged whle an unft node wll be weed out probably. In formula (5), D s beng restrcted because that one computng node can not run multple VMs as we mentoned n Sec 3.2. When there are some duplcated alleles n a generaton, we wll fnd t out and calculate an advsable value that how many IRs should not be appeared. Before that, a sutable value of Dups wll be produced accordng to the count of IRs. For example, a chromosome s wll produce a Dups value 1. The IRCount s the count of IRs. Thus, we wll fgure out the value of D. IV. THE SIMULATION AND PERFORMANCE ANALYSIS In order to study the usefulness of the mproved genetc algorthm n ths supersheduler problem n IaaS cloud envronments, we have developed a smulator. A schematc vew of the man prmary modules of the smulator s show s Fg. 4. IR j We have set up the test usng some reasonable user requests and suppose the cloud computng nodes to be popular PCs and commodty servers. And we assume several approprate number of computng nodes of dfferent knds. For that dfferent cloud computng organzaton wll have dfferent counts of computng nodes. We don t need to consder the number of cluster controller or node controller (sub schedulers) for that we use a superscheduler and all computng nodes are under consderaton of t. Usually, IaaS gves several VM types accordng to the users requrements and current popular machnes, such as PCs, servers and so on. Take Eucalyptus for example, see Table I. And users wll post ther requested VMs by the numbers of each VM name or type. For nstance, a customer only requests 1 c1.medum and another requests 2 m1.large and 3 c1.xlarge. TABLE I. FIVE TYPICAL VM TYPES Name CPUs Memory (MB) Dsk (GB) m1.small c1.medum m1.large m1.xlarge c1.xlarge In Table II, we set up a seral of reasonable computng nodes dependng on present popular PCs and servers. Thus the superscheduler wll gather the free resources for the scheduler and allocate IRs to these resources. We analyze the performance of our N length genes comparng wth the 2 * N length genes show n Fg. 5, and comparng the utlzaton rate of our algorthm wth the Greedy, Round robn algorthm whch provded by Eucalyptus and an average level of Open Nebula and Nmbus show n Fg. 6. TABLE II. A RESONABLE COMPUTING NODES OF PRESENT POPULAR MACHINES Name CPUs Memory (MB) Dsk (GB) Fgure 4. Man modules of the smulator The smulator s wrtten n Java language usng the JGAP (Java Genetc Algorthm Package) package and tested under Wndows XP (Intel platform). We use the framework of the JGAP and defne our own genes usng the Integer gene of JGAP. Another mportant thng s developng our own ftness functon as we descrbed above

5 Evol ut on t me (s) Utlzaton rate TGA 2 IGA 2 TGA 1 IGA 1 TGA 2 IGA Number of I Rs Fgure 5. Evoluton tme for IGA and TGA usng dfferent evoluton generaton In Fg. 5, we analyze the evolutons tme of our mproved genetc algorthm and tradtonal genetc algorthm of Grd envronment usng dfferent evoluton generatons, such as 2, 1, 2, etc. And the populaton s beng set approprate for the two genetc algorthms. From the result, we can see the evoluton tme of the IGA s half the TGA s usng dfferent evolutons. Wth the ncrease number of IRs, the performance of IGA s more close to twce of the TGA. And the schedulng result of IGA s almost the same as TGA s n dfferent numbers of IRs F rstf t Round rob n IGA Average Number of IRs Fgure 6. Utlzaton rate of IGA and Frst ft, Round robn and average level n a contented stuaton In Fg. 6, we nvestgate the utlzaton rate of the IGA, Frst ft, Round robn algorthm and the average utlzaton for other queung and confgurable schedulng. We mplement the other algorthms wth our smulator, and use an effectve populaton and evolutons for the IGA. From the above fgure, we can work out how much resource each model wasted when allocatng dfferent VMs. And we fnd that some tmes the Frst ft, Round robn and queung systems can not allocate resources for all the IRs even f there are enough resources for the IRs. But the IGA always gve a good schedulng as long as there are enough resources. On the other hand, we can see that our IGA saves the most resources. V. CONCLUSIONS AND FUTURE WORK Based on the deep research on current popular opensource IaaS cloud systems, we nvestgate the schedulng algorthms of these systems. We approached the optmzaton problem n a cloud computng envronment where heterogeneous resources usually provded as servces n forms of vrtual machnes. To solve the resource schedulng problem we ntroduced an mproved genetc algorthm, and optmze the request allocaton problem n a cloud envronment of superscheduler level. In order to testfy the effectveness of the IGA n the IaaS cloud envronment, we have developed a smulator and set some reasonable expermental data to do the smulaton. From the result of the smulaton, we can realze the performance of our IGA s twce the speed of the TGA, when comparng to the exstng open-source schedulers, we have mproved the utlzaton rate of computng resources. We make the scheduler run as many VMs as possble at a schedulng process and save as much energy as possble. Future work wll nclude more complete characterzatons of the constrant for schedulng n a cloud envronment. That s to say, we wll consder more thresholds of the envronment, such as I/O speed, network condtons and so on. And we wll develop t n the open-source scheduler frameworks such as Hazea, and mplement t n real envronment usng the above open-source IaaS cloud systems such as Eucalyptus, fnally test t practcally when there are enough physcal resources. ACKNOWLEDGEMENT Ths work was supported by Natonal Natural Scence Foundaton of Chna under Grant No and Innovaton Group Project of Yunnan Unversty. REFERENCES [1] Mladen A. Vouk, Cloud Computng Issues, Research and Implementatons, Journal of Computng and Informaton Technology, Unversty Computng Centre, Zagreb, Croata, 28, pp [2] I. Foster, Y. Zhao, I. Racu, and S. Lu, Cloud Computng and Grd Computng 36-degree compared, n Grd Computng Envronments Workshop, 28, pp [3] Whats.com, What s Cloud Computng?, 1,.html, 28. [4] D. Nurm, R. Wolsk, C. Grzegorczyk, G. Obertell, S. So-man, L. Youseff, and D. Zagorodnov, The Eucalyptus open-source cloud-computng system, IEEE Internatonal Symposum on Cluster Computng and the Grd (CCGrd 9), 29. [5] Open Nebular, [6] Nmbus, [7] Vncenzo D Martno, Marco Mllott, Schedulng n a grd computng envronment usng genetc algorthms, 3rd Workshop on Parallel and Dstrbuted Scentfc and Engneerng Computng wth Applcaton,

6 [8] V. D Martno, M. Mllott, Sub optmal schedulng n a grd usng genetc algorthms, grd resource schedulng based on mproved genetc algorthm, Parallel Computng, Scence Drect, 24, pp [9] Dudy Lma, Yew-Soon Onga,, Yaochu Jnb, Bernhard Sendhoffb, Bu-Sung Lee, Effcent Herarchcal Parallel Genetc Algorthms usng Grd computng, ScenceDrect, 26.11, pp [1] H. Kent Baker, DIVIDENDS AND DIVIDEND POLICY, John Wley & Sons, Inc., Hoboken, New Jersey, [11] Berman, F., Hgh-Performance Schedulers, Foster, Ian and Kesselman, C. eds., The Grd: Blueprnt for a New Computng Infrastructure, Morgan Kaufmann, 1999, pp [12] Lama Youseff, Mara Butrco et al., Towards a Unfed Ontology of Cloud Computng, Grd Computng Workshop (GCE 8), 28. [13] Gacomo V. Mc Evoy, Bruno Schulze et al., Performance and deployment evaluaton of a parallel applcaton n an onpremses Cloud envronment, Proceedng of the 7th Internatonal Workshop on Mddleware for Grds, Clouds and e-scence, Urbana Champagn, Illnos, 29. [14] T. Tan and C. Kddle. An Assessment of Eucalyptus Verson 1.4, Techncal Report , Department of Computer Scence, Unversty of Calgary, 29. [15] Amazon Web Servces, [16] openpbs, [17] SunGrdEngne, [18] Hazea, [19] A. Fukunaga, G. Rabdeau, S. Chen, D. Yan, Towards an Applcaton Framework for Automated Plannng and Schedulng, Proc Int.l Symp. on Art. Int,, Robotcs and Automaton for Space, Tokyo, Japna, [2] J. Napper et al., Can Cloud Computng Reach the Top5?, Combned Workshops on UnConventonal hgh performance computng workshop plus memory access, 29, pp [21] Armbrust M., Fox A. et al., Above the clouds: A berkely vew of cloud computng, Techncal Report UCB/EECS-29-28, EECS Department, Unversty of Calforna, Berkeley, [22] B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, Capacty leasng n cloud systems usng the opennebula engne, In Cloud Computng and Applcatons 28 (CCA8),