World Applied Scieces Joural 29 (Data Miig ad Soft Computig Techiques): 159-165, 2014 ISSN 1818-4952 IDOSI Publicatios, 2014 DOI: 10.5829/idosi.wasj.2014.29.dmsct.30 Towards Efficiet Load Balacig ad Gree it Mechaisms i Cloud Eviromet 1 2 2 S. RamKumar, V. Vaithiyaatha ad M. Lavaya 1 CSE, School of Computig, SASTRA Uiversity, Idia 2 School of Computig, SASTRA Uiversity, Idia Abstract: Cloud Eviromet allows users to access o-demad IT resources over the cyberspace based o pay per use patter. Optimizig the resource utilizatioad reducig the amout of active server machie usage i clouds become a challegig task. The major ited of this work is to builda model which supports balacig the workload i cloud dataceters ad miimizig the amout of active servers to support Gree-IT model.based o the covetioal Earliest Deadlie First(EDF) algorithm, this work presets a Adaptive Earliest Deadlie First (AEDF) algorithm for load balacig problem i order to achieve efficiet cosumptio of cloud resources ad also to support Gree Computig techology. Comparig with traditioal load balacig algorithm,the proposed method achieves high throughput, miimized cost ad effective schedulig of tasks. Our proposed model supports both Load balacig ad Gree-IT techology. Key words: Cloud Eviromet Gree Computig Load balacig Resourceutilizatio INTRODUCTION software based virtualizatio techiques. Hybrid virtualizatio [3] is the combiatio of both hardware Cloud Computig [1, 2] is the model for deliverig based virtualizatio machiery ad traditioal Para- IT resources, i which the resources are retrieved from the virtualizatio techiques. Para-virtualizatio is the cyberspace usig pay per use patter. It allows the cloud virtualizatio techique used i xe server ad it serves cosumer to extet the ifrastructure dyamically i the iterfaces to the virtual machie. Para-virtualizatio withi rapid period. I ature, the cloud cotais uses the kerel for guest os is exactly loaded ad virtualizatio of resources (i.e. software ad hardware) compiled before istallig i to the virtual machie. This these resources are maitaied ad maaged i cloud hybrid virtualizatio techique ca decrease the eviromet itself. Cloud eviromet provides several complicatios ad overhead i the covetioal virtual characteristics: machies. This work presets Gree Computig techology or O-demad utilizatio. Gree IT it ca maximize the utilizatio of cloud resources Network of Shared Resources. i eergy efficiet maer. The Gree IT also reduces the Elastic Provisioig. eergy expediture. This proposed work iteds to attai Fie graied meterig. the both Load balacig techique ad Gree IT techology: Virtualizatio i cloud computig is the costructig virtual system istead of real system, it may be a Gree IT: Maximize the cosumptio of cloud computatioal resources, hardware platforms, software virtual resources, also it decreases the cost of cloud platforms, storage machie depeds upo the user IT services ad miimize the amout of active server requiremets. Now a day s several ivestigatios are machie. focused o costructig efficiet virtualizatio Load Balacig: Allocate the workload techologies for IT purposes (i.e. Gree computig ad across several computers, servers ad other cloud computig). Hardware based virtualizatio resources to attai efficiet resource machiery itroduced for reducig the costraits of the cosumptio. Correspodig Author: S. RamKumar, CSE, School of Computig, SASTRA Uiversity, Idia. 159
Fig. 1: Cloud Server Architecture I Gree computig the amout of physical machies used should be miimized ad also this techology reduces the eergy cosumptio by turig off the idle systems. Load balacig techique [4] is essetially used i distributed system eviromet. There are various algorithms used for task schedulig i cloud eviromet which allocates the cloud virtual machie for user s o-demad request. Load balacig methods are categorized ito two types: Static workload balacig algorithms ad dyamic workload balacig algorithms. Static methods are maily appropriate for homogeous platforms ad this algorithm is ot suitable for dyamic modificatios of attribute for the period of executio. Whereas dyamic algorithms provides flexibility ad it cosiders the various kids of attributes i both static period ad durig o-demad. These methods ca produce efficiet throughput i o-demad eviromet. I Cloud computig improvig the efficiecy of cloud server is becomes the challegig task. The key cocer is balacig the load [5] i dataceters to icrease effectiveess of the host system ad cloud eviromet should support Gree-IT techology. I this work, we preseted the ivetive model based o Gree computig techique ad also based o traditioal Earliest Deadlie First(EDF) algorithm this work presets Adaptive Earliest Deadlie First(AEDF) algorithm for efficiet schedulig ad executio of tasks. This paper orgaized as: Sectio II provides related works of load balacig algorithm ad gree computig techiques. Sectio III discusses the implicatios of gree computig ad cloud virtual server optimizatio. Sectio IV presets proposed system ad algorithms. Sectio V discusses the experimetal works ad Sectio VI cocludes the work. Related Works: Zhe xiao, Weijia sog et al [6] proposed the ew techique that dyamically allocates resources depeds upo applicatio requiremets ad it miimizes the sum of servers to be used for resource allocatio process.based o the skewess algorithm this paper also provides measurig of icosistecy cosumptio of various resources o the server.this work which cocludes by reducig the skewess measure the cloud cosumer s ca avoid overload ad ehace the comprehesive cosumptio of resources i the server. I order to avoid overloadig problems i Real-time distributed systems, Suaib Akhter, Mahmudur Rahma Kha et.al [7] proposed the iovative approach which is the mixture of both EDF (Earliest Deadlie First) algorithm ad RM (Rate-mootoic) algorithm. It is the combiatio both dyamic schedulig algorithm (EDF) ad static schedulig algorithm (RM). This algorithm prevets the system eterig i to the overloaded state with successful completio all tasks i the system. This system model assures efficiet cosumptio of resources ad efficiecy of the system. I paper [8] Youg Choo Lee, Albert Y.Zomaya proposed a ew model for eergetic usage of resources i cloud eviromet. This paper presetsthe task cosolidatio algorithm, to which icreases the resource cosumptio effectively. Ad also it takes accout ito both alive ad empty status of the eergy cosumptio.this method allocates each job to the resource for performig the job without loss of performace of that job. I paper [9, 10] itroduces various load balacig algorithms ad gree computig techiques i clouds. Shridhar G.Domaal ad G.Ram Mohaa Reddy [9] itroduces virtual machie allocated load balacig algorithm for efficiet access of cloud virtual machies resources. This algorithm which assigs the arrivig demads to the every available virtual machies i a well-orgaized maer. Yatedra Sahu, R.K.Pateriya et.al [10] presets a ew balacig algorithm for cloud server efficiet utilizatio. I order to balacethe workload we have to migrate the virtual machies host from highly loaded server to least loaded server. This method ca miimize the cost with efficiet cosumptio of applicable resources. Zhaghui Liu ad Xiaoli Wag [11] proposed theiovative schedulig approach, which ehaces the both job executio period ad resource cosumptio ratio. Depeds upo this schedulig approach, this work also ehaces the existig PSO algorithm by applyig simple mutatio method ad other automatic-adaptig weight methods by arragig the fitess measures. 160
[7] EDF Earliest Deadlie First is a o-demad servers to be desely overloaded whereas other servers schedulig algorithm which allocates the tasks i priority are simply i idle state or slightly overloaded. Uiform queue. distributio of load will ehace the performace of this Implemetig Load balacig algorithm i IaaS cloud migratio techique. So we require a effective load eviromet is becomes the challegig task. I paper balacig mechaism to balace the various workloads [12] L.Shakkeera,Latha Tamilselva ad et.al proposed effectively to ehace the cosumptio of cloud server ew framework which implemets the load balacig resources. This paper presets ew Adaptive Earliest method o Ifrastructure as a Service (IaaS) cloud Deadlie First (AEDF) algorithm to achieve balacig the eviromet for efficiet access of cloud virtual workload perfectly ad effective utilizatio of cloud resources.this Quality of Service (QoS) load balacig resources. mechaisms iteds to reduce the applicatio cost. I this [12] proposed work the task mechaism used Gree Computig: The major objective of gree based o the cost table which allocatig the jobs to the computig is to miimize the use of risky resources ad virtual machies i efficiet maer. icreasig the eergy ability of resources i efficiet Resource Schedulig is a major raisig issue i cloud maer. Gree computig or Gree ITtechology is eviromet for efficiet cosumptio of resources. miimizes the use of amout of active cloud server I paper [13] Haihua Chag ad Xihuai Tag developed machie. Also, this techique achieves eergy savigs by the schedulig method depeds upo o-demad tured off idle physical machies temporarily. Skewess load balace. This algorithm cosiders various algorithm is used to measure the iequality i the data-processig cotrol machies ad also it cosiders cosumptio of various resources o the server. The various data-trasferrig capacity odes. It chooses the challegig problem i gree computig is to miimize the optimal virtual machie to satisfy the job for icrease amout of active server machies durig slightly loaded the effectiveess of cloud eviromet. Also, it decreases without loses of performace. the regular respose period of jobs. Our proposed gree computig method will ivoked Jihua Hu, Jiahua Gu et al [14] itroduced the whe the server workload threshold beyod the maximum. uique method for efficiet schedulig of virtual machie Oce the server reaches the workload threshold beyod resources for load balacig issues. Based o the geetic maximum the the arrivig resource requests from cloud algorithm operatio, this approach solves the issues of users are redirected to the ext cloud server.i.e. server that load iequality ad it decreases the migratio cost of satisfies the workload threshold coditio. The followig traditioal methods. method is used to evaluate the workload threshold of [4] Load Balacig is a method which dissemiates particular server s: the workload amog several computig resources ad it Workload Threshold (S) = is used to ehace the resource utilizatio. E i 0 i CSiWli = i= 0 (1) Implicatios of Load Balacig ad Gree Computig Load Balacig: I Cloud eviromet, the cloud Where Eiis the executio time of particular Task Tii the cosumers have to cosider some issues ad challeges task set.cs1 is the cloud server which executes the task whe maitais the load betwee cloud resources.load ad Wli is the workload threshold of particular server S. balacig is computer etworkig method which So, the all remaiig servers are i idle state i order to dissemiates theworkload across several computers, miimize the eergy cosumptio. It teds to miimize the servers ad other resources to attai efficiet resource amout of physical machies beig used ad it sustais cosumptio ad ited to icrease the throughput. If the Gree computig techology. amout of cosumers to the appropriate virtual system goes beyod the, workload balacig server will force to Proposed System forward the arrivig cosumer request to the aother Load Balacig: For Load Balacig issues i cloud virtual systems. eviromet, we itroduced a Adaptive Earliest Deadlie But, this geeral method is ot suitable for whe First (AEDF) algorithmto achieve balacig the various cosiderig the efficiecy, throughput ad time. I the workload icely ad effective utilizatio of cloud server meatime o-demad arrivig of load will cause a few resources.based o the covetioal Earliest Deadlie 161
First (EDF) algorithm, this proposedaedf algorithm is used for efficiet schedulig ad executio of tasks. The major objective of this work is to support workload balacig i cloud dataceters ad miimize the amout of active server usage to support Gree computig model. AEDF Load Balacig Fuctioig Workflow: The AEDF algorithm provides optimal result for load balacig i cloud eviromet. Fig. 1 shows fuctioal workflow of the proposed method. Proposed Modelof Load Balacig System: Effective Load balacig mechaism is used to ehace the efficiet cosumptio of resources ad schedulig of tasks. This Adaptive Earliest Deadlie First (AEDF) algorithm is based o threshold value to attai the effective schedulig ad utilizatio of resources. The followig algorithm describes the load balacig mechaism for various icomig tasks i clouds. Algorithm: Adaptive Earliest Deadlie First (AEDF) for Workload Balacig. Iput: Task Set (T 1, T 2,...T ),Deadlie(D 1,D2,... D ), Executio Period(E1, E2,... E ). 1. Iitialize: NT (New Time), ST (Service Time) =0; 2. WhileTask Set queue! = NULL 3. Calculate U i= E i/ Di 4. Assig Threshold TH=0.5 5. If (T i(u i)) < TH 6. Isert Ti ito EDF queue 7. Else 8. Isert T i ito AEDF queue 9. Ed If 10. Ed While 11. While EDF Queue! = NULL 12. Schedule Task usig EDF Fig. 2: AEDF Load Balacig fuctioig workflow 13. Ed While 14. While AEDF Queue! = NULL Based o the threshold value coditio the miimum 15. Schedule Tiwith Mi (D i) utilizatio valued tasks will follow covetioal EDF 16. NT = E + ST schedulig to execute the tasks. Whe the task utilizatio i 17. If (T E ) + NT < = D rate goes beyod the threshold value the the tasks are (i+1) (i+1) i+1 18. Schedule Ti+1 scheduled i AEDF queue based o deadlie of 19. Else correspodig tasks. The experimetal aalysis shows 20. Ti+1= Ti+2 AEDF achieves efficiet schedulig ad cosumptio of 21. Ed If cloud resources for load balacig problems. 22. Ed While Gree Computig or Gree IT: The mai objective of The major goal of above AEDF algorithm is to Gree computig is to miimize the amout of active optimize the load balacig i cloud eviromet ad server machie i cloud eviromet.thus Gree IT alsothis method is ited to utilize the cloud resources iteds to achieve eergy savigs by tured off the idle efficietly.the cocludig results of AEDF method shows physical machies temporarily.our proposed gree better schedulig of tasks ad effective cosumptio of computig method will ivoked whe the server workload resources the the traditioal EDF algorithm. threshold beyod the maximum. 162
Pseudo code: Gree computig Techique Iput: Cloud Servers (Cs 1,Cs 2,...Cs ), Task Set (T1, T 2,...T ), Ready Queue (RQ), Task Set Worst Case Executio Time WCET (E 1,E 2,...E ), Workload Threshold (Wl 1,Wl 2,...Wl ). 1. Iitialize: Remaiig Workload(rl)=0; 2. If E 0 i <= CSiWli i= i= 0 3. Assig RQ = RQ = CSi 4. While RQ! = NULL 5. If Ti (Ei) < = Cs i (Wli) Cs i (rli) 6. Csistate = Active 7. Remaiig servers(cs) state= idle 8. Assig Tito Cs i 9. Cs (rli) = Ti (Ei) i i= 0 10. Icremet i 11. Else 12. Icremet j 13. Csj state= Active 14. rlj = 0 15. Ed If 16. Ed While Gree Computig Operatioal Workflow Model: The followig workflow diagram depicts dataceter maager to optimize the cloud server. The above stated algorithm for Gree IT is itededfor optimizig the cloud server.while ay task request come from cloud cosumer for accessig the cloud service the the tasks are allocated to the appropriate virtual machie for accessig the services. This proposed method of gree computig assigs the user request to cloud server virtual system based o the workload threshold mechaism. Assume that each server has differet disc image. This workload threshold mechaism reduces the amout of active server accessig ad it effectively reduces theuecessary eergy cosumptio. RESULTS The proposed method works better tha covetioal EDF algorithm. To demostrate the proposed method 10 sample tasks are give as iput to the iitial queue. Each task icludes its ow Deadlie (D 1,D 2,...D 10) ad Executio time(e 1,E 2,...E 10). Based o the utilizatio time of each task, the tasks are scheduled either i EDF queue or ADEF queue. For effective schedulig the task i the task set should be executed withi the deadlie of correspodig task. After schedulig of all tasks, the deadlie for each task is calculated ad compared with the traditioal system.table 2. Shows the compariso aalysis of the proposed method which effectively miimizes the umber of deadlie missed tasks ad it improves the resource cosumptio. Gree Computig: I this proposed method, the Gree IT approach is achieved based o workload threshold of each server i cloud eviromet. For aalysis we have take 10 sample tasks(t 1,T 2,...T ) with correspodig executio period (E 1,E 2,...E ). Assume that each server has differet disc image. Depeds upo the workload threshold of cloud server (usig Eq.o.1) this method achieves miimal utilizatio of active servers ad it reduces the cost of cloud IT services. Fig. 3: Gree computig Operatioal workflow Fig. 4: Efficiecy of AEDF Algorithm 163
Table 1: Iput Task Set Ti Di Ei Ui T1 30 10 0.333 T2 30 15 0.5 T3 10 3 0.3 T4 40 35 0.875 T5 20 15 0.75 T6 40 27 0.675 T7 50 26 0.52 T8 35 9 0.257 T9 56 20 0.357 T10 70 25 0.357 Table 2.Correlatio Aalysis Algorithm EDF Cocerted EDF & AEDF Amout of missed Tasks 7 3 Table 3.Optimizatio model for miimizig active cloud servers Ti ECET (Ei) Csi T1 30 CS1 T2 40 CS1 T3 20 CS1 T4 15 CS2 T5 40 CS2 T6 39 CS3 T7 24 CS3 T8 56 CS4 T9 11 CS5 T10 47 CS5 CONCLUSION Optimizig the cloud dataceter to balace the workload i clouds becomes a challegig task. I order toavoid overloadig issue i cloud eviromet, this work presets a iovative Adaptive Earliest Deadlie First (AEDF) algorithm to achieve efficiet cosumptio of cloud virtual resources ad effective load balacig. The major ited of this work is to build a model which supports both workload balacig mechaism ad reduced utilizatio of umber of active servers to support gree computig techology. Thus, the proposed gree computig approach is based o workload threshold of cloud server.whe comparedwith several traditioal load balacig techiques,the results of this proposed algorithm preseted hereshows efficiet resource cosumptio, miimized cost of cloud resourcesadreduced amout of active server utilizatio. REFERENCES 1. Giamario Motta, Nicola Sfrodrii ad Daielo Sacco, 2012. Cloud computig: A architectural ad techological overview, i Iteratioal Joit Coferece o Service Scieces. 2. Itroductio to Cloud Computig, url: www.priv.gc.ca. 3. Ihyuk kim, Taehyoug Kim ad Youg Ik Eom, NHVM:Desig ad Implemetatio of Liux Server Virtual Machie usig Hybrid Virtualizatio Techology, i IEEE, Iteratioal Coferece o Computatioal Sciece ad Its Applicatios, 2010. 4. Klaithem Al Nuaimi, Nader Mohamed, et al., A Survey of Load balacig i Cloud Computig: d Challeges ad Algorithms, i IEEE, 2 Symposium o Network Cloud Computig ad Applicatios, 2012. 5. Nidhi Jai Kasal ad Iderveer Chaa, 2012. Cloud Load Balacig Techiques: A step Towards Gree Computig, i IJCSI, Iteratioal Joural of Computer Sciece Issues. 6. Zhe Xiao, Weijia Sog ad Qi Che, 2013. Dyamic Resource Allocatio Usig Virtual Machies for Cloud Computig Eviromet, IEEE Trasactios o Parallel ad Distributed System, 24: 6. 7. Suaib Akhter, A.F.M., Mahmudur Rahma Kha ad Shariful Islam, 2012. Overload Avoidace Algorithm for Real-Time Distributed System, i Iteratioal Joural of Computer Sciece ad Network Security (IJCSNS), 12: 9, September 2012. 8. Youg Choo Lee ad Albert Y. Zomaya, 2010. Eergy efficiet utilizatio of resources i cloud computig systems, Spriger Sciece. 9. Shridhar G. Domaal ad G. Ram Mohaa Reddy, 2014. Optimal Load Balacig i cloud Computig by Efficiet Utilizatio of Virtual Machies, IEEE. 10. Yatedra Sahu, R.K. Pateriya ad Rajeev Kumar Gupta, 2013. Cloud Server Optimizatio with Load Balacig ad Gree Computig Techiques Usig Dyamic Compare ad Balace Algorithm, i IEEE, th 5 Iteratioal Coferece o Computatioal Itelligece ad commuicatio Networks. 11. Zhaghui Liu ad Xiaoli Wag, 2012. A PSO-Based Algorithm for Load Balacig i Virtual Machies of Cloud Computig Eviromet, Spriger-Verlag Berli Heidelberg, pp: 142-147. 164
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