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480 JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 Surve o Resource Allocatio Polic ad Job Schedulig Algorithms of Cloud Computig 1 Lu Huag Software School of Xiame Uiversit, Xiame, Chia Email: bagbag_4391@qq.com Hai-sha Che 2 Software School of Xiame Uiversit, Xiame, Chia Email: hsche@xmu.edu.c Tig-tig Hu Software School of Xiame Uiversit, Xiame, Chia Email: 815464551@qq.com Abstract Cloud computig is the product of the evolutio of calculatio. It is a ew distributed computig model. As more ad more people put ito the research ad applicatios o cloud computig, the techolog of computig becomes more ad more widel used. Cloud computig has a huge user group. It has to deal with a large umber of tasks. How to make appropriate decisios whe allocatig hardware resources to the tasks ad dispatchig the computig tasks to resource pool has become the mai issue i cloud computig. This paper is based o the curret situatio of resource allocatio polic ad job schedulig algorithms uder cloud circumstace. It summarizes some methods to improve the performace, icludig damic resource allocatio strateg based o the law of failure, damic resource assigmet o the basis of credibilit, at colo optimizatio algorithm for resource allocatio, damic schedulig algorithm based o threshold, optimized geetic algorithm with dual fitess ad improved at colo algorithm for job schedulig. Idex Terms Cloud Computig, Resource Allocatio, Job Schedulig, At Colo Algorithm, Geetic Algorithm is a super iteret based computig model i which tes of thousads of computers ad servers are coected ito a computer cloud [2]. Strictl speakig, Cloud Computig is a model of eablig ubiquitous, coveiet ad odemad etwork access to a shared pool of cofigurable computig resource(e.g. etworks, servers, storage, applicatios, ad service) that ca be rapidl provisioed ad released with miimal maagemet effort or service provider iteractio accordig to NIST(Natioal Istitute of Stadards ad Techolog) [3]. Cloud computig is a product from mixig traditioal computer techiques ad etwork techologies, such as grid computig, distributed computig, parallel computig, utilit computig, etwork storage, virtualizatio, load balacig, etc [4]. Grid Cloud I. INTRODUCTION Fig.1 shows the mode of calculatio i differet stages of developmet. Calculatio mode has goe through a process from the origial mode of gatherig all the tasks to large-scale processor for processig (Fig.1(a)), to the distributed tasks processig mode based o Iteret (Fig.1(b)) later, ad the to the cloud computig mode for immediate processig (Fig.1(c)) [1]. Calculatio is executed o distributed computer cluster i cloud computig. It is o kid of calculatio mode drive b a large-scale commercial demad. Eterprises ca get access to computig power, storage capacit, services ad other abilities accordig to their eeds. Cloud computig 1 Supported i part b a grat from the Leadig Academic Disciplie Program, Project 211 (the 3 rd phase) of Xiame Uiversit. 2 Correspodig Author: hsche@xmu.edu.c (a) (b) (c) Fig.1 Evolutio of Calculatio Mode II. RESOURCE ALLOCATION POLICY A. Overview Cloud computig evolves from grid computig, which is also regarded as its backboe ad basic structure. It is arguable that cloud computig is a higher form of grid computig. But there is a big differece betwee them i realit. The specific cotet of which ca be foud i Evo s paper [5]. The features that dispersio, heterogeeit ad ucertait of resources i the odes brig challeges to resource allocatio, which ca ot be doi:10.4304/jsw.8.2.480-487

JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 481 satisfied with traditioal resource allocatio polocies i cloud circumstace. How to achieve damic cofiguratio ad shared use of computig resources is a core issue i the field of cloud. Researchers have proposed a variet of schemes for damic resources providig ad maagemet, which have bee described i Irwi s paper [6] ad Padala s paper [7]. Ma scholars have put forward solutios for efficiet resources allocatio based o market mechaisms. CDA (Cotiuous Double Auctio) is oe of the commo market mechaisms beig used curretl. It esures high efficiec ad effective coordiatio of resource allocatio. Kee s paper [8] proves that web resource allocatio based o CDA framework is effective. Ad Li s paper [9] presets a cloud resource assigmet polic based o CDA framework ad Nash equilibrium to fulfill effective resource allocatio i cloud eviromet. Meawhile, Researchers have proposed several ideas to improve resource reliabilit, such as reliabilit verificatio method of resources based upo the law of failure of cluster odes [10], proposed b Heath, multiple checkpoit strateg for improvig the abilit of avoidig performace loss caused b sstem failure for HPC service [11], preseted ad aalzed b Zhag, ad the capacit of HPC job queue structure for improvig the reliabilit of job ruig [12], aalzed b Hacker. However, there is little discussio i the literature about the research o how to esure the reliabilit of damic resource provisio uder cloud circumstace. It is importat for both cloud computig ifrastructure operators ad service operators to guaratee the reliabilit of damic provisio resource. Either the QoS (Qualit of Services) ad SLO (Service Level Objects) or the efficiec ad effectiveess of damic resource that service providers should esure depeded o the reliabilit of damic resource providig. For this questio, based upo the failure of rules that heterogeeous services preset i time ad space i cloud eviromet, researchers have proposed strategies such as damic resource provisio o the basic of the law of failure [13] ad damic resource allocatio based o credibilit [14] ad so o. Allocatio of resources is a importat compoet of cloud computig. Its efficiec will directl ifluece the performace of the whole cloud eviromet. Sice cloud computig has its ow features, origial resource allocatio poloc ad schedulig algorithms for grid computig are uable to work uder this coditio. To deal with this issue, Hua s paper [15] proposed a at colo optimizatio algorithm for resource allocatio, i which all the characteristics i cloud are cosidered. It has bee compared with geetic algorithm ad aealig algorithm, provig that it is suitable for computig resource search a allocatio i cloud computig eviromet. B. Damic Resource Allocatio Strateg Based o The Law of Failure As services o cloud computig platform ca be broadl divided ito two categories, which are data computatio-itesive services ad iteractio-itesive etwork processig services, the law of failure of these two services are took ito accout. Research workers have made a lot of researches for the rule of sstem failure of ode resources. Hua s paper[10], Schroeder s paper [16] ad Sahoo s paper [17] preset that through the stud of log o the sstem failure, researchers foud that the failure presets a strog time ad spatial localit. Ad the spacig iterval of ode restartig failure without plas is regarded as a radom process, which fits the Weibull (scale, shape) distributio with the parameters of shape less tha 1. This suggests that the ode which has just failed is more likel to fail agai, while ruig, the ode will become more ad more stable. So it is ecessar to make a period of test o the reliabilit of odes which have just failed. Meawhile, researchers poit out that for a tpical cluster sstem mixed b high-performace computig services ad etwork processig services, the probabilit of the failure of ode restartig radoml is from descedig to ascedig durig the ruig time [17]. This pheomeo is obvious for database odes i the mixed cluster. At the same time, sice service sstems (such as data ceter services) accumulate more ad more iteral errors while ruig, software agig problem is commo. I this regard, researchers propose software regeeratio strategies. Whe the sstem is active regeeratio, errors withi the sstem will be cleared ad the sstem will be back to ormal state [18]. Moreover, effective regeeratio strategies ca reduce sstem performace losses caused b software rejuveatio techolog. Combiig the failure law, research workers put forward a damic ode resource allocatio polic based o failure rules [13]. The polic icludes two major categories of a sigle queue ad multiple queue policies, i which the former is the basic of the latter. Damic provisio strateg of ode resource is a O-Demad strateg. The sigle queue strateg (Fig.2) maitais a ordered ode resource pool accordig to the last recover time (uptime). Failure odes are placed o the top of the queue. Whe there is a request, this strateg will select a free ode from the tail of queue to hadle this request, esurig that the ode is the most reliable oe i the idle resource pool. Whe the job is doe, the resource ode will be put back to the queue i order. For the basic sigle queue strateg, ol oe list is maitaied, while for the exteded multiple queue strateg (Fig.3), several queues are maitaied. At each time, oe appropriate queue is selected for resource providig ad recclig operatios. Tia s paper [13] presets the compariso experimets betwee the sigle queue ad multiple queue strateg uder a variet of circumstaces to prove the efficiec ad reliabilit of them.

482 JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 tail tail head Operato HPC Iteret Services Node Pool Fig.2 Basic Sigle Queue Strateg HPC Iteret Services tail Node Pool N head Operator tail Node Pool 1 head Fig.3 Exteded Multiple Queue Strateg buers ad the sellers will cofirm the quote accordig to the market trasactio eviromet ad their ow excitatio mechaism. Takig the law of the ode failure of cloud resource, ad o the basic of CDA mechaism ad the credibilit of ode, researchers proposed a damic resource allocatio model (T-CDA). First, both sides of suppl ad demad should cofirm their ow prices based upo their ow pricig strateg. The auctioeer will sort the price of resource provider i a descedig order ad the price of resource demader i a ascedig order. The it will determie whether the resource trasactio ca be cocluded based o the utilit model of resource tradig. Cheg s paper [14] presets a simulatio experimet o this strateg i Matlab 7.1. Through the result of simulatio ad evaluatio of successful executio ratio ad a deviatio from fairess i resource allocatio, this strateg is proved to be markedl superior to the resource allocatio strategies without cosiderig the credibilit of odes. D. At Colo Optimizatio Algorithm for Resource Allocatio Cloud computig distributed cluster uses a Master/Slaves structure (Fig.4). There is a Master ode resposible for cotrollig ad supervisig all the Slave odes. Sice the specific coditio of resource is ukow uder cloud circumstace, ad the etworks do ot have a fixed topolog, the structure ad the resource allocatio of the whole cloud eviromet is upredictable. I this case, the locatio ad qualit of computig resources for data odes is ukow. C. Damic Resource Assigmet o The Basis of Credibilit The requests for resource uder cloud eviromet tpicall exhibit strog volatilit. To esure the credibilit of damic resources without affectig its service efficiec, researchers propose a more credible damic resource provisio strateg [19]. I the cloud, resource allocatio model based o CDA mechaism mail icludes cloud resource providig aget, cloud resource requiremet aget, ad iformatio servig aget. The cosult each other to achieve a balace o the price ad the amout of resource for trasactio. Whe eterig or leavig a cloud resource sstem, both the ower of the resource ad the cloud user eed to be registered to the iformatio servig aget. Ad the ower will set a price ad allocate the resources through resource providig aget, while the user will allocate appropriate amout of resource through resource requiremet aget to the jobs eeded to be doe. I CDA mechaism, resource providig aget, resource requiremet aget ad iformatio servig aget correspod to the seller, the buer ad the arbiter i the auctio respectivel. The arbiter is resposible for orgaizig the auctio ad collectig market iformatio. At a time uit durig the auctio, the seller ad the buer offer their ow price to the arbiter, ad the arbiter will match the resource trasactios based o both sides price lists ad give a average price for both sides. The Data Node SLAVE Name Node Task Tracker MASTER Job Tracker Data Node SLAVE Fig.4 Master/Slaves Structure of Hadoop Cluster Task Tracker At Colo Optimizatio (ACO) is a updated bioic optimizatio algorithm which is i simulatio of at foragig behavior. It is origied b M.Dorigo et al. who was ispired b the research result of the group behaviour of real ats [20]. ACO algorithm shows characteristics of rapidit, distributio ad global optimizaio whe solvig complex optimizatio problems. Ad the rapidit of fidig the optimal solutio is due to the regeerative feedback mechaism of pheromoe. While its feature of distributed computatio avoids premature covergece of the algorithm. Meawhile, the at sstem, with the feature of greed heuristic search, ca fid a acceptable solutio earl i the search process. The pesudocode of the prototpe sstem of the at colo algorithm ca be expressed as follows:

JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 483 Procedure: ACO Algorithm Begi While (ACO has ot bee stopped) do Schedule activities At s allocatio ad movig ( at distributio ad movemet ) Local pheromoe update ( local pheromoe update ) Global pheromoe update ( global pheromoe update ) Ed schedule activities Ed While Ed Procedure At colo algorithm ca fid out computig resources i ukow etwork topolog ad select the most appropriate oe or more resources to user s job util it meets user s requiremets. Whe the search begis, quer messages will be set b slave ode, ad the will pla the role of ats. All of the ats obe the priciple of the more pheromoe oe ode has, the larger probabilit it will be, vice versa to choose the ext hop ode, ad it will leave pheromoe o the ode of the path as it goes through. I order to reflect the chage of the pheromoe, researchers adopt a local update strateg to modif the pheromoe itesit oto the ode. Hua s paper [15] provides a detailed descriptio about at colo algorithm i resource allocatio ad uses Gridsim to simulate local domai of cloud computig to ispect the operatig coditios of the algorithm uder cloud etwork eviromet. At the same time, this algorithm is compared with geetic algorithm ad simulated aealig algorithm. Through GridResource class ad a series of helper classes i Gridsim, researchers simulate the computatio ad etwork resources of cloud computig ad costructs a relativel real cloud laout. After much experimetaito, researchers foud that at colo algorithm is more effective tha other two algorithems i the case that there are more odes ad fewer resources, which is just the characteristics of cloud eviromet. At colo algorithm aims at the large-scale, shared, damic ad other characteristics of cloud eviromet. It assigs search ad allocates computatio resources to user s job damicall. Ad it shows more advatages i cloud eviromet. III. JOB SCHEDULING ALGORITHM UNDER CLOUD CIRCUMSTANCE job dispatcher, which is almost resopsible for all the task allocatios, resposes ad retrasmissios [22]. Over-reliace o the scheduler ma lead to some virtual machies overload while others are idle after dispatcher allocatig tasks accordig to the load of virtual machies. Whe this occurs, the ol solutio is to assig tasks for ext period accordig to what the feedback schedulig device gets. The process i differet vitrual machies is idepedet. A virtual machie does ot have access to other virtual machies ruig coditios. So if oe of the followig two problems occures, the executio efficiec of the tasks will be affected. Oe of the problem is that the dispather is out of joit, ad the other oe is the eviromet of virtual machies chages, resultig i the problems of some virtual machies ad makig them uable to sed the iformatio back to the scheduler. This will also cause some virtual machies overload while others free. B. Damic Schedulig Algorithm Based o Threshold To get the real-time feedback of the state of virtual machie, there are two was. Oe of them is to costruct a set of feedback mechaism betwee dispatcher ad virtual machies to get the real-time feedback of the tasks load o virtual machie, ad the make a real-time adjustmet o job allocatio upo the fact of virtual machies. The other oe is to use the damic schedulig amog virtual machies themselves to get the real-time state of the load of virtual machies. If overload or idleess occurred, tasks could be readjusted ad redistributed amog virtual machies. B damic dispatch i virtual machies, damic schedulig algorithm based o threshold ca allocate jobs ad resources flexibl ad reduce the efficiec impact caused b the schroizatio amog virtual machies. Fig.5 shows the model of the damic schedulig algorithm based o threshold [21]. The situatio showed i Fig.5 is that if there are some virtual machies overload ad some idle at a certai time, damic job adjustmet is coducted to shorte the total cost time, thereb ehacig efficiec. However, task allocatio betwee virtual machies refers to schroizatio problems, which is also the biggest problem of the damic schedulig algorithm based o threshold. Sice each virtual machie is idepedet to each other, i the other word the are o-iterferig. The ca perform tasks i parallel. If virtual machies are schroized, the ievitabl brig effects to their performace. Therefore, the schroizatio operatio should be kept to miimum rage. A. Backgroud Job schedulig of cloud computig refers to the process of adjustig resources betwee differet resource users accordig to certai rules of resource use uder a give cloud eviromet [21]. Resource maagemet ad job schedulig are the ke techologies of cloud computig. At preset, there is ot a uiform stadard for job schedulig i cloud. Most alogorithms foucus o

484 JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 job pool classified job queue virtual machie VM1 VM2 VM3 resource 2 Fig.5 Model of the Damic Schedulig Algorithm Based o Threshold I order to reduce the impact of schroizatio, two measuremets are take. First, set the threshold. Schroizatio is executed ol whe the virtual machie reaches a threshold. The larger the threshold is, the smaller the impact of schroizatio will be. Secod, limit the schroizatio dow to two virtual machies. The smaller the umber of virtual machies for schroizatio is, the weaker the impact it brigs. Fig.6 idicates the flow of the damic schedulig algorithm based o threshold. task iitializatio data ceter resource 1 resource 3 resource 4 schroized. Ad their tasks will be balaced ad will cotiue workig. Task equilibrium meas that if there is at least oe idle virtual machie ad at least oe overload virtual machie, other virtual machies will execute tasks idepedetl. Xia s paper [23] compares betwee damic schedulig algorithm based o threshold ad virtual machies with the static idepedet job schedulig algorithm o CloudSim platform. The result suggests that whe there are a fairl large umber of tasks, the former ca complete task allocatio efficietl ad reduce the ruig time greatl. It shows a obvious advatage over the latter. C. Optimized Geetic Algorithm Geetic Algorithm (GA) is proposed b Holad, who was ispired b biological evolutio, i 1975. Parallelism ad global solutio space search are the two otable features of the GA [24]. Fig.7 shows the flow of GA. ecodig ad iitial populatio geeratio calculatig the fitess of each idividual ad evaluatio task assigmet satisfig the termiatio coditios task assigmet o virtual machies ruig o virtual machies selectio ed reachig the uptime or task threshold crossover overload Fig.6 Flow of the Damic Schedulig Algorithm Based o Threshold Task assigmet ivolves i settig task classificatio accordig to PRI. A task that has a higher executio priorit has higher PRI. Reachig the uptime or task threshold meas that the time threshold of task ruig or the umber of tasks that are waitig i the lie is reached. It icludes two coditios. There are two tpes of task threshold. Oe is the umber of tasks waitig to be doe i the queue o oe virtual machie. The other oe is the umber of tasks that have bee fiished o aother virtual machie. If both umbers were larger tha the threshold value at the same time, these two virtual machies would be task equilibrium ed idle variatio Fig.7 Flow of GA O the basis of Map/Reduce model i cloud [25], i order to cut dow both the total ruig time ad the average time of task executio, researchers add oe more fitess to improve the GA. That is the optimized geetic algorithm with dual fitess (DFGA), which has two fitess fuctios. DFGA algorithm uses the idirect ecodig method of resource task. The legth of chromosome is the umber of sub tasks. Ad the value of each gee o the chromosome is correspodig to the resource umber which is allocated to the sub task o this locatio. Iitializatio is to geerate a SCALE umber of chromosome, which has a legth of M, ad the value rage of gee is a radom umber i [1, WORKER]. Amog them, M stads for the total umber of sub tasks, ad WORKER is the umber of resources. There are two fitess fuctios, oe is the total time of job ruig o all virtual machies ad the other is the average time.

JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 485 Both of them should be short. The selectio operator uses Roulette method. Li s paper [24] describes the compariso betwee adaptive geetic algorithm (AGA) ad DFGA uder a local eviromet of cloud computig, which is simulated o Gridsim. The result shows that after ma geeratios of evolutio, both the total time ad the average time of tasks executio usig DFGA are sigificatl superior to the AGA. DFGA is a effective job schedulig algorithm. D. Improved At Colo Algorithm The essece of job schedulig is to select a wa of damic combiatio of resource with relativel good performace amog all the resource allocatio methods. From the perspective of problem-solvig, optimized at colo algorithm is ver suitable for resource allocatio i cloud eviromet [26]. As the radomess of at colo algorithm is large, it is easil trapped i local optimal solutio ad slow covergece. Thus, research workers itroduce GA, which has a capabilit of rapid ad radom global search, to each iteratio process of at colo algorithm. This ca greatl accelerate the speed of covergece ad esure the accurac of the origial algorithm. For each resource requester, cloud computig service cluster should give a fairl good combiatio of tasks ad resources. I improved at colo algorithm, at the same time, the factors affectig the resource state ca be described b pheromoe, ad the schedulig process ca get predictable results simpl ad quickl. I cloud circumstace, take ACS (At Colo Sstem) algorithm model based o ACO algorithm for example, the flow of job schedulig process based o ACO algorithm ca be described as Fig.8 [27]. Wag s paper [21] describes the aalog simulatio of the improved ACO algorithm based o a exteded cloud computig simulatio platform. It was compared with the Roud Robi (RR) algorithm ad the origial ACO algorithm. Geerall, improved ACO algorithm takes less time ad has a higher efficiec tha other two algorithms. start iitializatio place m ats o odes radoml geerate task targets reew local pheromoe all ats geerate allocatio table regard the solutio set that the ats fid ad the global optimal solutio of ACS as the iitial populatio of GA select idividual X, Y crossover X ad Y variatio meet the coversio requiremets update global optimal solutio IV. CONCLUSIONS This paper aalzes resource allocatio ad job schedulig issues uder cloud eviromet ad describes iterrelated solutios proposed b research workers. Progress has bee made i the existig strategies, which ca sigificatl improve the efficiec of the use of resources accordig to certai situatio i cloud. As the applicatio area of cloud computig becomes wider ad wider, resource allocatio ad job schedulig algorithms will be further improved i order to adapt to a variet of specific applicatio eviromets. update global pheromoe meet the target output the fial optimal solutio ed Fig.8 Flow of Job Schedulig Process Based o ACO Algorithm

486 JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 ACKNOWLEDGMENT This work was supported i part b a grat from the Leadig Academic Disciplie Program, Project 211 (the 3 rd phase) of Xiame Uiversit. REFERENCES [1] Li Qiao, Zheg Xiao.Research Surve of Cloud Computig[J]. Computer Sciece, 2011, 4(38): 32~37. [2] Chumei Chi, Feg Gao. The Tred of Cloud Computig i Chia[J]. Joural of Software, VOL.6, NO.7, 2011,7: 1230~1234. [3] Tog Yag, Be-Chag Shia, Jirui Wei, Kuaga Fag. Mass Data Aalsis ad Forecastig Based o Cloud Computig[J]. Joural of Software, VOL.7, NO.10, 2012.10: 2189~2194. [4] Li Hua, Yag Dogri, Liu Loggeg. Master iterview cloud computig promotes busiess ad techological chage[m]. Beijig: Electroic Idustr Press, 2011. [5] MC Evo G V. Schulze B. Usig clouds to address grid limitatios[c]. MGC 08. Belgium: Leuve Press, 2008. [6] Irwi D, Chase J S, Grit L et al. Sharig etworked resources with brokered leases[c]. Proceedigs of the USENIX Techical Coferece, Bosto, MA, USA, 2006, pp299~212. [7] Padala P, Shi K G, Zhu Xiao Yu et al. Adaptive cotrol of virtualized resources i utilit computig eviromets[c]. Proceedigs of the 2d ACM SIGOPS/EuroSs Europea Coferece o Computer Sstems 2007. Lisbo, Portugal, 2007, pp289~302. [8] Kee J, Eberhart R. Particle Swarm Optimizatio[C]. Proc. of IEEE Iteratioal Cof. o Neural Networks. Perth. USA [s..], 1995. [9] Li Li, Niu Be. Particle swarm optimizatio[m]. Beijig: Metallurgical Idustr Press, 2009. [10] Heath T, Marti R P, Ngue T D. Improvig cluster availabilit usig workstatio validatio[c]. Proceedigs of the ACM SIGMETRICS. Maria Del Re, Califoria, USA, 2002, pp217~227. [11] Zhag Y, Squillate M S, Sivasubramaiam A et al. Performace implicatios of failures i large-scale cluster schedulig[c]. Proceedigs of the 10th Worksh-op o Job Schedulig Strategies for Parallel Processig JSSPP 2004. New York, NY, USA, 2004, pp233~252. [12] Hacker T J, Meglicki Z. Usig queue structures to improve job reliabilit[c]. Proceedig of the ACM HPDC 2007. Motere, Califoria, USA, 2007, pp43~54. [13] Tia Guahua, Meg Da, Zha Jiafeg. Reliable Resource Provisio Polic for Cloud Computig[J]. Chiese Joural of Computers, 2010, 10(33): 1859~1872. [14] Cheg Shiwei, Pa Yu. Credibilit-based damic resource distributio strateg uder cloud computig eviromet[j]. Computer Egieerig, 2011, 6(37): 45~48. [15] Hua Xiau, Zheg Ju, Hu Wexi. At colo optimizatio algorithm for computig resource allocatio based o cloud computig eviromet[j]. Joural of East Chia Normal Uiversit (Natural Sciece). 2010, (1): 127~134. [16] Schroeder B, Gibso G A. A large-scale stud of failures i high-performace computig sstems[c]. Proceedigs of DSN 2006. Philadelphia, Peslvaia, USA, 2006, pp249~258. [17] Sahoo R K, Sivasubramaiam A, Squillate M S et al. Failure data aalsis of a large-scale heterogeeous server eviromet[c]. Proceedigs of the DSN 2004. Florece, Ital, 2004, pp772~784. [18] Huag Y, Kitala C, Kolettis N et al. Software rejuveatio: Aalsis, module ad applicatios[c]. Proceedigs of the 25th Smposium o Fault Tolerat Computer Sstems. Pasadea, Califoria, 1995, pp381~390. [19] Valkehoef G, Famchur S D, Vteligum P, et al. Cotiuous Double Auctios with Executio Ucertait[C]. Proc. of Workshop o Tradig Aget Desig ad Aalsis. Pasadea, Califoria, USA [s..], 2009. [20] Dorigo, Maiezzo, Colomi. At sstem: optimizatio b a colo of cooperatig agets[j]. IEEE Trasactios o SMC, 1996, 26(1): 8~41. [21] Wag Yoggui, Ha Ruilia. Stud o cloud computig task schedule strateg based o MACO algorithm[j]. Computer Measuremet & Cotrol, 2011, 19 (5): 1203~1204, 1211. [22] Wu Jii, Pig Ligdi, Pa Xuezeg, etc. Cloud computig form cocept to platform[j]. Telecome Techolog, 2009, (12): 23~25. [23] Xia Ji, Yu Guicheg. Research o schedulig algorithm based o cloud computig[j]. Computer & Digital Egieerig, 2011, (7): 39~42. [24] Li Jiafeg, Peg Jia. Task schedulig algorithm based o improved geetic algorithm i cloud computig eviromet[j]. Joural of Computer Applicatios, 2011, 1 (31): 184~186. [25] DEAN J, GHEMAWATS. MapReduce simplified data processig o large clusters[c]. Proceedigs of the 6th Smposium o Operatig Sstem Desig ad Implemetatio. New York ACM, 2004, pp137~150. [26] Wag Tiaqig, Xie Ju, Zeg zhou. Grid resource schedulig strateg based o at colo algorithm[j]. Computer Egieerig ad Desig, 2007, 28(15): 3611~3613. [27] Dorigo M, Caro GD. At colo optimizatio: A ew meta-heuristic[a]. Proc. of the 1999 Cogress o Evolutioar Computatio. Washigto: IEEE Press, 1999, pp1470~1477. Lu Huag received the Bachelor of Digital Media Arts from Xiame Uiversit i 2011. At preset, she is a postgraduate studet at the Software School of Xiame Uiversit. Her research areas are cloud computig ad web data miig. Hai-sha Che received the Bachelor of Sciece from Xiame Uiversit i 1982. At preset, He is the professor of software school of Xiame Uiversit. His research iterests iclude database techolog, Web services ad cloud computig.

JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 487 Tig-tig Hu received the Bachelor of Digital Media Arts from Xiame Uiversit i 2011. At preset, she is a postgraduate studet at the Software School of Xiame Uiversit. Her research area icludes data miig ad cloud computig.