Genetic-based Resource Allocation and Scheduling Technique for Multi-Class Users in Cloud Computing Environment

Transcription

1 Internatonal Journal of Pure and Appled Mathematcs Volume 117 No , ISSN: (prnted verson); ISSN: (on-lne verson) url: do: /jpam.v Specal Issue jpam.eu Genetc-based Resource Allocaton and Schedulng Technque for Mult-Class Users n Cloud Computng Envronment Ms. Dev #1, N. K. Sakthvel *2, S. Subasree *3 # Research Scholar, Department of Computer Scence, Manonmanam Sundaranar Unversty Trunelvel dev.sngaravelan@gmal.com * Department of Computer Scence and Engneerng, Nehru College of Engneerng and Research Centre, Thrssur, Kerala, Inda 2 nksakthvel@gmal.com Abstract More Enterprses and large Industres are employng Cloud Computng Envronments to get ts requred computng resources farly. The prme feature of Cloud Envronment s to execute and utlze ts varous applcatons and data from anywhere at any tme. Nowadays, almost all people usng Cloud Envronment for ther personal or commercal tasks and actvtes. At the same tme, Industres are expectng to employ Cloud Computng Resources, Data Centers and varous applcatons and Software n a cost-effectve manner to support cloud users demands. Thus, Cloud Envronment needs effectve and effcent mechansms to dentfy or allocate best resources to cloud users based on ther Demands and QoS. Ths s one of the challengng ssues and hence more researchers are focusng to develop an effcentresource Allocaton and Schedulng Technque. To address ths ssue, ths research work proposed an effcent and effectve Genetc based Resource Allocaton and Schedulng Technque for Mult-Class Users n Cloud Computng Envronment. The proposed model was mplemented and studed thoroughly. From the expermental results, t s noted that the proposed model s performng well n terms of Resource Allocaton (VM Provson), Cumulatve Proft and Convergence Cost to fnd best resources. It s also noted that the proposed model outperforms well as compared wth the exstng dentfed Resource Allocaton and Schedulng Technque. KeyWords : Gene Assocaton, Genetc Algorthm, Schedulng,Cloud Computng, Resource Provsonng,Proft Maxmzaton. 1 Introducton The Cloud Computng s emergng as a modern nnovatve computng paradgm whch s amng to offer Guaranteed and Trusted Qualty of Servce (QoS) based computng envronment to endusers. Ths Cloud Computng s offerng great dynamc envronments to ts endusers [1,2,3]. As Cloud Computng Envronment s the flexblty n nature, we could easly and swftly access more resources from Servce Provders depends upon our requrements and demands of our busness. Ths remote accessblty feature allows us to access all the avalable Cloud based Web Servces at any perod of tme from anywhere. To acheve the maxmum possble benefts, the varous avalable servce provders should be dentfed and best Servce need to allot effectvely to the applcatons whch s runnng on the cloud [3,4]. Resource Provsonng s an approach whch conssts of Task Schedulng Technque and Resource Allocaton Scheme as well. Resources avalable on the cloud must be provsoned n such a manner that ther tremendous capabltes are effcently utlzed and effectvely avalable to end users on-tme wthout much delay n completon of tasks gven by the cloud users. The Provsonng Servces to Cloud Users must qualfy the Servce Level Requrements n terms of Performance, Capablty, Avalablty and Costs of Resources. It has to consder all Cloud Users Requrements, Constrants and Demands to satsfy all ts Users. Thus, the Cloud Users are expectng to dentfy the best Resource for ther applcaton wth hghest avalablty and Mnmum Response Tme and need to avod overprovsonng computng resources as well as t wll lead to underutlzaton durng normal workloads. Ths research work consders one of the real tme applcatons called CloudTranscodng systems [3]. Frstly t s needed to provde requred resources depends on demandng workloads. Secondly, as dfferent users needed dfferent resource and performance demands, ths model needed an effcent schedulers. Thrdly, ths model needed resource provsonng as well as task schedulng polces to mnmze usage costs of resources by Cloud Users. To address the above mentoned demands and ssues, ths research work proposed an effcentgenetc-based Resource Allocaton and Schedulng Technques for Mult-Class Users n Cloud Computng Envronment. Ths Research paper s arranged and wrtten as follows. The Secton 2 brefly descrbed the recently 175

2 Internatonal Journal of Pure and Appled Mathematcs Specal Issue proposed Resource Provsonng and varous Proft Maxmzaton Schemes. The proposed GenetcbasedResource Provsonng and Proft Maxmzaton Technque s descrbed n Secton 3. The results and strengths of the proposed modelsdscussed at Secton 4 and Concluson was gven n Secton 5. 2 Resource Provsonng and Schemes for Proft Maxmzaton In ths secton, ths work descrbed the System Archtecture desgned for Resource Provsonng and Task Schedulng by authors [3] partcularly for cloudtranscodng system and the workflows. 2.1 Archtecture The Archtecture of the author [3] proposed for Resource Provsonng and Task Schedulng s shown n the Fgure 1. Servce Interface: It s used to estmate the computng resource whch areneeded to complete the tasks. A few Interfaces are Command Lne Interface (CLI), Remote Procedure Call (RPC) and RESTFUL. Fgure 1. System Archtecture for Resource Provsonng and Task Schedulng Resource Provsonng: It s employed to manage Vrtual Machnes to schedule them to complete varous tas lke transcodng vdeos and etc.it s facltatng to acheve Resource Utlzaton as well. Ths s helpng Cloud Users to acheve QoS Task Schedulng:Ths s an mportant one to mantan Farness to the System. It s used to mantan queue to allow or block varous tasks requested for executon. Senstve Tasks can be forwarded earler wth Schedulng Polces to acheve QoS and Farness 2.2 Contrbutons Author [3] contrbutons are summarzed as follows. In the Herarchcal Control Archtecture, the StochastcOptmzaton Framework was ntroduced to maxmzeprofts for varous Servces requested and executed by Cloud Users through schedulng tasks. An Artfcal Neural Network was employed to estmate computng resources to successfully complete Transcodng Tasks. Cloud Transcodng System Morph was used to analyss the performances of the system [3] n a real envronment. 2.3 Value Based Schedulng Scheme (VBS) The Servce Provder wll be permtted and encouraged to negotate wth Cloud User to understand ther requred QoS.e. ther wllng prce and demanded performance such as speed, deadlne, delay and accuracy. Ths wll help Cloud Users to maxmze ther Proft along wth ther demanded QoS. That s f Cloud Users wanted less prce to complete tasks, ths system wll allocate resource n such way that the task wll take longer delay to complete Task. The prce functon [3] s defned [3] as n equaton (1). Suppose a task submtted at a partcular tmea and assume that t was completed at t. The prce charged for that partcular task can be denoted as t-a U ( t) R D 0 1, t a (1), Whereσ s the dscountng factor, R s consdered as the margnal prcefor one task of computng tme consumed, and D s theoverall computng tme consumed. If a task takes morecomputng resource, automatcally the charge wll be more. Themargnal prce can be calculated by the varous prorty levels of a task requested by cloud users. It s obvously notced that hgh prorty need to pay hgher prce. That s hgh prorty taskwll consume hgher prce, t wll charge a hgher prcng. Thedscountng factor reflects the deadlnedependent prcng. In general, servce provder can gan hgher revenue f atask completed faster.. The prcng functon was desgned n such a way thatwll affect the polcy of task schedulng. The framework [3] s applcable to some other functons too, The lnear functon s, U t) w ( t a, t a, 0 (2) ( ) where w s the ntal prce for task and ß s the, dscountng factor. When ( t) s less than zero, and ths wll be referred penaltyfor more delays. The step prcng functon s w, a t a U" ( t) (3) 0, t a where w s the recommended deadlne for task. If a task mssed ts deadlne, wll not be charged. 2.4 Learnng-based Resource Provsonng (LRP) The system wll decde how many numbers of actve VMs at the begnnng of each Nk need to run accordng to the requrements. The author [3] denotes the resource provsonng/allocatonpolcy n the slow tmescale as π s and the resource provsonng acton at Nk as vk. Specfcally, vk> 0 denotesthe actvated new VMs and vk< 0 denotes theshutdown VMs. The acton space for executng the resourceprovsonng s denoted as ^. The mappng between the slow and fast tmescales to the resourceprovsonng acton can be represented by the polcy π s s s s f : (, ) vk, k 0,1, 2,... (4) k kt The number of actve VMs after takng the resource provsonngacton s mk, wheremk = mk-1 + vk 0. The task schedulng polcy defned for fast tmescale fnds the transcodng order of pendng tasks. Ths wll facltate to maxmze overallrevenue. The system state n tmescale wll run Ttme slots untl the state changed to slow U 176

3 Internatonal Journal of Pure and Appled Mathematcs Specal Issue tmescale. 2.5 System Implementaton The Cloud Users can access the system through Web Portal or Command LneInterface (CLI).The HTTP requests from usersare processed by Apache web server. Ths work was mplemented [3] wth the help of MySQL tostore system nformaton. The resource provsonng and taskschedulng technque was mplemented n a lbrary. The system developed by author [3] conssts of a. Master Node,. Resource Manager Nodeand.TranscodngWorker Nodes. Ths s shown n the Fgure 2. It s confgured n such a way that each and every node can run on a Vrtual Machne. The manfunctonaltes of each node are shown as follows Master Node: As shown n the Fgure 3, the servce nterface module and taskschedulng module have been mplemented n ths Node. It s used to process Cloud Users requests and all these requests wll be scheduled as per the polcy to transcodetasks. 3.1 Genetc based Resource Allocaton and Schedulng Technque Task Schedulngs the major concern n the exstng model. To mprove the performance of the Schedulng Technque n terms of speedup the convergence and dversty of Resources, ths work proposed Genetc based Schedulng Technque. The Framework of the Genetc based LRP+VBS s gven below. Algorthm : LRP+VBS Step 1: Intalzaton Step 2: Confgure Parameters accordng to VBS Step 3: Generate ntal poston of the populaton (LRP + VBS) populaton and demands Step 4: Whle (Iteraton!= Max. Iteraton) Step 5: { Step 6: Calculate the dstances between neghbours, and store ts poston and cost value; Step 7: Update the ndvdual poston at the setup Step 8: Generate correspondng soluton for Rank Step 9: } Step10: Return the best soluton Step 11: Call Master Node through VBS Fgure 2. The man components the cloud transcodng system [3] Transcodng Worker Node: [3] It wll request vdeoblocks that s from the Master Node and transcode the vdeo blocksnto the requested another dgtal format. When ths converson task s completed,t wll nform to Master Node. Resource Manager Node: [3] It s developed and deployedon a VM. It wll dynamcally manage accordng to the system workloads and the resource provsonng polcy as well. 3 Genetc based Resource Allocaton and Schedulng Technque Task Schedulng n the Cloud Envronment s consdered an NP-Hard Problem [5,6,7]. From the lterature survey, t was notced that the Resource Allocaton.e. Resource Provsonng, Proft to Cloud Users through effectve schedulng and fndng or allocatng the best resources to address users demands and QoS are the major challenges. To address ths dentfed ssues, ths research work proposed an effcent and effectve Genetcbased Resource Allocaton and Schedulng Technque for Mult-Class Users n Cloud Computng Envronment. Genetc based Value Based Schedulng Fgure 3. The man components of the proposed GA- LRP+VBS 4 Expermental Study and Analyss Ths Research Work proposed an effcent Genetc-basedResource Provsonng and Proft Maxmzaton Technque. Ths s mplemented wth our own Cloud Setups wth the help of VC++ Programmng Language. Our Tool has the followng Modules to create Cloud Setup. The Modules are Resource Admnstraton Module, Servces Identfcaton Module, Servce and Access Management Module, Addng Users and Users Groups Module whch conssts of Addng IP Group(s), Addng Cloud Group, Addng Hypervsors, Confgure Hypervsors Module whch focusng Storage and Network and Confgure Shared Servces. For testng the performances of the proposed technque, ths research work s used the followng cloud setup whch s located n Delh NCR. IBM/Super/HP DL 160 G8 Seres 1 X Intel Hexa Core Xeon Processor E

4 Convergence Cost Cumulatve Proft (n Rs) Number of Vrtual Machnes (VMs) Instances Internatonal Journal of Pure and Appled Mathematcs Specal Issue 15 M Cache,2.0 GHz,7.2 GT/s Intel QPI), 2x Ggabt Ethernet Card 6 Cores 12 Threads Integrated SAS RAID 0,1,5 8 GB Fully Buffered DDR3 ECC Memory 2 X 1 TB SATA 7200 RPM HDD We have repeated our experments numbers of tmes by changng our requred Processor Speed, Prmary Memory Space, Secondary Memory Space, Executon Tme and Throughput and outputs were recorded Tme (Hour) Fgure4.No. of Vrtual Machnes Provsoned vs Tme As shown n the Fgure4, the Learnng-based Resource Provsonngand Value-based Task Schedulng (LRP+VBS)controls the VM for varous Tasks. However, from ths Fgure 4, t s revealed that the proposed Genetc-based LRP+VBS,.e. Genetc-based Value-based Task Schedulng controls effectvely the VMs. That s the proposed GA- LRP+VBS outperforms LRP+VBS. It s notced that Cloud Users usng less VMs to complete ther tasks successfully and hence the Resource Utlzaton and Proft gong hgh, whch encourages Cloud Users Performance Analyss : VMs Instances vs Tme Fgure 5.Cumulatve Proft vs Tme GA-LRP-VBS LRP-VBS Performance Analyss : Cumulatve Proft vs Tme Tme (Hour) Performance Analyss : Convergence Cost vs Iteratons GA-LRP-VBS LRP-VBS GQ-LRP-VBS LRP-VBS Number of Iteratons Fgure 6.Convergence Cost vs No. of Iteratons The cumulatve proft of the proposed GA- LRP+VBS Model s shown n the Fgure 5. From the Fgure3, t s notced that the proposed GA- LRP+VBS outperforms LRP+VBS over the perod. Fgure 6 shows the Convergence Cost (cost value) of the proposed GA-LRP+VBS and LRP+VBS aganst number of teratons. From the output, t s establshed that the proposed model always fnds the best Resource or Resource Provson to Cloud Users and ths acheves and satsfes users demanded QoS. Ths s the prme purpose of the proposed model whch acheves Proft too. The Crossoverand Mutaton operator of genetc algorthm facltates GA-LRP+VBS to outperform LRP+VBS 5 Concluson It s clearly observed that the Cloud Envronment needs effectve and effcent mechansms to dentfy or allocate best resources to cloud users demands and QoS. Ths s one of the challengng ssues and hence more researchers are focusng to develop an effcentresource Allocaton and Schedulng Technques. To address ths ssue, ths research work proposed an effcent and effectve Genetc-based Resource Allocaton and Schedulng Technque. The proposed model was mplemented and studed thoroughly. From the expermental results, t s noted that the proposed model s performng well n terms of Resource Allocaton (VM Provson), Cumulatve Proft and Convergence Cost to fnd best resources. It s also noted that the proposed model outperforms well as compared wth the exstng dentfed Resource Allocaton and Schedulng Technque. References [1] Ms. Dev, N. K. Sakthvel and S. Subasree, Performance Analyss of Resource Allocaton and Schedulng Technques for Mult Class Users n Cloud Computng Envronment, Internatonal Journal of Appled Engneerng Research (IJAER), pp , [2] Ms. Dev, Dr. N. K. Sakthvel and Dr. S. Subasree, Performance Analyss of Resource Allocaton and Schedulng Technques for Mult Class Users n Cloud Computng Envronment, Internatonal Conference on Computng Paradgms, [3] Guanyu Gao, Han Hu, Yonggang Wen, and Cedrc Westphal, Resource Provsonng and Proft Maxmzaton fortranscodng n Clouds: A Two-Tmescale Approach, IEEE Transactons on Multmeda, Vol.: 19, Issue: 4, [4] Hongyan Cu, Yang L, Xaofe Lu, Nrwan Ansar, Cloud Servce Relablty Modelng and OptmalTask Schedulng, IET Communcatons, Vol.: 11, [5] Sukhpal, Sngh, Inderveer Chana, A Survey on Resource Schedulng n Cloud Computng: Issues and Challenges, Journal of GrdComputng, June [6] Hao J X, Yu Y, Law R, et al. A genetc algorthm-based learnng approach to understand customer satsfacton wth OTA webstes[j].toursm Management, [7] K.P. Wong, Y.W. Wong,?Genetc and genetc/smulated-annealng approaches to economc dspatch,? IET Gener. Transm. Ds., vol. 141,pp ,

5 179

6 180