, March 12-14, 2014, Hog Kog Virtual Machie Schedulig Maagemet o Cloud Computig Usig Artificial Bee Coloy B. Kruekaew ad W. Kimpa Abstract Resource schedulig maagemet desig o Cloud computig is a importat problem. Schedulig model, cost, quality of service, time, ad coditios of the request for access to services are factors to be focused. A good task scheduler should adapt its schedulig strategy to the chagig eviromet ad load balacig Cloud task schedulig policy. Therefore, i this paper, Artificial Bee Coloy (ABC) is applied to optimize the schedulig of Virtual Machie (VM) o Cloud computig. The mai cotributio of work is to aalyze the differece of VM load balacig algorithm ad to reduce the makespa of data processig time. The schedulig strategy was simulated usig CloudSim tools. Experimetal results idicated that the combiatio of the proposed ABC algorithm, schedulig based o the size of tasks, ad the Logest Job First (LJF) schedulig algorithm performed a good performace schedulig strategy i chagig eviromet ad balacig work load which ca reduce the makespa of data processig time. Idex Terms Artificial Bee Coloy, Cloud Computig, Schedulig Maagemet, Virtualizatio Machie R I. INTRODUCTION ECENTLY, iteret commuicatio has become oe of the most importat factors i daily life activities. Cosequetly, iteret is the ceter of data sharig. Due to the large amout of icreasig users, the data is ofte chaged or modified. This causes the heavy task schedulig for computers while less services ca be processed to serve umerous users. However, this problem ca be solved by usig ew techology of Cloud computig which focuses o etworkig ad computig, storage, ad data service resources. Cloud computig is a emergig cocept of iformatio techology service. It cosists of both ifrastructure as well as the applicatio services ad focuses o types of users requiremets. The users ca idetify their eeds through the software i Cloud. Cloud computig [1], [2] is a termiology used i describig various computig cocepts that ivolve a large umber of computers coected through a real-time commuicatio etwork such as the iteret. Cloud computig is the result of evolutio ad adoptio of existig techologies ad paradigms. Its goal is to allow the users to Mauscript received November 21, 2013; revised December 3, 2013. B. Kruekaew is with the Departmet of Computer Sciece, Faculty of Sciece, Kig Mogkut s Istitute of Techology Ladkrabag (KMITL), Chalogkrug Rd., Ladkrabag, Bagkok, 10520, Thailad (e-mail: s3650853@kmitl.ac.th). W. Kimpa is with the Departmet of Computer Sciece, Faculty of Sciece, Kig Mogkut s Istitute of Techology Ladkrabag (KMITL), Chalogkrug Rd., Ladkrabag, Bagkok, 10520, Thailad (e-mail: kwarag@kmitl.ac.th). take advatages from these techologies without the requiremets of deep kowledge or expertise. The Cloud aims to reduce costs ad to provide the ease of resource maagemet. Virtualizatio is the priciple techology for Cloud computig. It provides the agility i operatios, ad reduces cost by icreasig ifrastructure utilizatio. O the other had, Cloud computig automates the process through which the users ca utilize resources o-demad. Virtualizatio [3], [4] is critical to Cloud computig because it delivers services by providig a platform for optimizig complex Iformatio Techology resources i a scalable maer. The mai aim of the virtualizatio is a ability to ru the multiple VMs o a sigle machie by sharig all the resources that belog to the hardware. The problem of load balacig services occurs whe the services from the users make a request to access to the same server while other servers have o request from the services. This pheomeo is called distributed load imbalace system. I this situatio, it ca be solved by schedulig the tasks or the services before usig the system. Therefore, a good task scheduler ca icrease the performace of resource utilizatio ad ca reduce the makespa of assiged tasks which is called distributed load balace system. Task schedulig i distributio system such as Cloud is used for balacig work load. It requires some coditios for example, stability of the system, makespa of work, ability to adapt to the eviromet chagig, ad etc. Those coditios i task maagemet are similar to the behaviors of Artificial Bee Coloy algorithm (ABC algorithm). ABC is ispired by the behavior of a hoey bee coloy i ectar collectio. Bee coloy based o the social behavior has the ability to adapt to the eviromet chagig. The suggestio for ehacig the performace of cloud task schedulig is to reduce the makespa of a give task set ad icreate performace of resource utilizatio by usig improved ABC algorithm. It ca be used to solve the problem ad the appropriate value ca be foud. Moreover, it has the ability to adapt to the eviromet chagig [5]- [7]. The structure of this paper is orgaized as follows: the sectio II presets a overview of the related work. The Artificial Bee Coloy is preseted i sectio III. Sectio IV presets Artificial Bee Coloy ispired schedulig ad load balacig algorithm. Sectio V is the experimetal results ad discussio. Fially the coclusio ad future work are preseted i sectio VI.
, March 12-14, 2014, Hog Kog II. RELATED WORK Schedulig ad load balacig techiques are crucial for implemetig the efficiet parallel ad distributed applicatios i makig the appropriate use of parallel ad distributed system. Task schedulig ca icrease the efficiecy of Cloud computig if it is able to work uder the reasoable resources [4], [8], [9]. Li et al. [10] proposed the tree structure method i order to solve Job-Shop Schedulig i supportig the eeds of a complex ad flexibility to Job-Shop Schedulig problem. It also appropriately maaged the specific features of model maagemet ad schedulig algorithm by usig Reusable Scheme ad Multi-Aget system. Later, Fag et al. [11] preseted task schedulig i Cloud computig cosiderig the requiremets of the users ad load balace i Cloud eviromet whe the users icrease ad chage the eviromet. The schedulig strategy was simulated by usig CloudSim toolkit package. Calheiros et al. [12], [13] developed the CloudSim simulatio for modelig ad simulatio of virtualized Cloud-based dataceter eviromets, icludig dedicated maagemet iterface for VMs, memory, storage, ad badwidth. Simulatio-based approaches ca evaluate Cloud computig system ad applicatio behaviors offerig sigificat beefits. Modal et al. [14] proposed troubleshoot of load balace i Cloud computig usig Stochastic Hill Climbig. I 2011, Hsu et al. [9] preseted a focus o eergy efficiecy i dataceter by cosiderig the task schedulig to physical server ad reducig eergy i the system. The criterio used to measure the performace of a eergy loss was to prepare computer exceeds the requiremet of the job. Experimet strategies for allocatig server to a sequece of jobs were a largest machie first heuristic, a best fit method, ad a mixed method. The experimet results idicated that all three algorithms waste less eergy cosumptio i overprovisio. I 2010, Hu et al. [15] itroduced the schedulig strategy o load balacig of VM resource i Cloud computig eviromet by adoptig a tree structure usig Geetic algorithm for schedulig. It cosidered previous data ad the curret state of work i advace to the performace behavior of the system which ca solve the problem of load imbalace i Cloud computig. I 2012, Wei et al. [16] preseted Geetic algorithm for schedulig i Cloud computig to icrease the system performace. Li et al. [17] proposed a Load Balacig At Coloy Optimizatio (LBACO) Algorithm. ACO was use to schedule o load balacig ad reduce makespa i Cloud. Karaboga et al., i 2012 [7], preseted ABC algorithm which was used to solve the problem ad fid the most appropriate value withi eviromet chagig. ABC algorithm based o the swarm-based optimizatio algorithm ad it is used to solve the optimizatio problems i searchig, electrical egieerig, data miig, ad etc. I the same year, Bitam et al. [18] proposed Bee Life algorithm which was used for schedulig i Cloud computig. Bee Life algorithm is ispired by the behavior ad reproductio of bee to fid food source. The algorithm evaluated the performace of the resources ad it has the aim to reduce time ad complexity of work. Miza et al. [19] preseted job schedulig i Hybrid cloud by modifyig Bee Life algorithm ad Greedy algorithm to achieve a affirmative respose from the ed users ad utilize the resources. III. ARTIFICIAL BEE COLONY ALGORITHM Artificial Bee Coloy (ABC) algorithm was proposed by Karaboga [5], [6], [20], [21]. It is the method to fid the appropriate value. This method is ispired by the foragig behavior of hoey bees. I ABC model, there are three kids of hoey bee to search food sources, which iclude scout bees search for food source radomly, employed bees search aroud the food source ad share food iformatio to the olooker bees, ad olooker bees calculate the fitess ad select the best food source. I the ature, bees ca exted themselves over log distaces i multiple directios. After scout bees fid the food source ad retur to the hive, they compare the quality of food source ad go to the dace floor to perform a dace kow as waggle dace. The waggle dace is the commuicatio of bees to shares the iformatio about directio of the food source, distace from the hive, ad the ectar amout of the food source. While sharig iformatio, bees evaluate the ectar quality ad eergy waste. After sharig iformatio o the dace floor, olooker bees select the best food source ad the scout bees will retur to the food source to brig ectar back to the hive. A pseudo code of ABC algorithm is described i Fig. 1. Fig. 1. Basic of ABC algorithm. IV. ARTIFICIAL BEE COLONY INSPIRED SCHEDULING AND LOAD BALANCING ALGORITHM Cloud computig provides a dyamic resource pool of VM accordig to differet requiremets from the users or the system. The routig of services which request to the diverse servers, deped o the Cloud maagemet policies based o load of idividual server. Schedulig maagemet o Cloud computig is differet degrees of loads o each ad every VM. This may lead to differet load amog the VMs. Load balacig techiques are effective i reducig the makespa ad respose time.
, March 12-14, 2014, Hog Kog Let VM = {VM 1, VM 2, VM 3,, VM N } is a set of N virtual machies ad Task = {task 1, task 2, task 3,, task K } is a set of K task to be scheduled ad processed i VM. All the machies are urelated but are paralleled. Schedulig is o-preemptive which meas that the processig of the tasks o VMs caot be iterrupted. The flowchart of the VM schedulig ad load balacig usig ABC algorithm is show i Fig. 2. capacity j = pe_um j pe_mips j + vm_bw j (2) Where capacity j is a capacity of VM j, pe_um j (processig elemet) is the umber processor i VM j, pe_mips j is millio istructios per secod of each processor i VM j, ad vm_bw j is the etwork badwidth ability of VM j. C. Select m Sites for Neighborhood Search Scout bees with the highest fitess are chose as Selected Bee ad the sites visitig by them are chose from eighborhood of m VMs. D. Recruit Bees for Selected Site Sed more bees to eighborhood of the best e VM, the evaluate the fitess based o (3). fit ij = i=1 task_legth i + IputFile_legth Evaluate capacity of VM j (capacity j ) (3) Where IputFile_legth is the legth of the task before executio. E. Select the Best Fitess Bees from Each Patch ad Assig Task to VM j For each roud, the bee with the best fitess will be chose to assig task i VM j. F. Calculate Load Balace After submittig tasks to the uder loaded VM j, the curret workload of all available VMs ca be calculated by usig the iformatio that received from the dataceter. Thus, Stadard Deviatio (S.D.) is calculated i order to measure the deviatios of load o VMs. Stadard deviatio of load ca be defied as (4): S. D. = 1 (X j=0 j X ) 2 (4) Fig. 2. Flowchart of the VM schedulig ad load balacig usig ABC algorithm. A. Iitialize Populatio At the begiig, the iitial scout bees are placed radomly i VMs o Cloud computig ad is the umber of scout bees. B. Evaluate the Fitess of the Populatio Fitess is calculated based o (1). fit ij = i=1 task_legth ij Evaluate capacity of VM j (capacity j ) Where fit ij is the fitess of the bees populatio of i i VM j or capacity of VM j with bee umber of i. task_legth is the legth of the task that has bee submitted i VM j ad capacity j is the capacity of VM j calculatig based o (2). (1) Processig time of a VM: X j = Mea of processig time of all VMs: X = k i=1 task_legth i (5) capacity j j=1 X j Where S. D. is Stadard deviatio of load, is umber of all VM. X j is processig time of VM j which ca be calculated based o (5) ad X is mea processig time of N virtual machies which ca be calculated based o (6). If the S.D. of the loaded VM is less tha or equal to the mea, the the system is i a balace state. O the other had, if the S.D. is higher tha the mea, the the system is i a imbalace state. (6)
, March 12-14, 2014, Hog Kog V. EXPERIMENTAL RESULTS A. Implemetatio Eviromet Accordig to the algorithm described above, the simulatio usig CloudSim-3.0.1 Tools will be addressed. The experimets cosist of 10 dataceters ad 100-700 tasks uder the simulatio platform. The parameters settig o the CloudSim-3.0.1 is show i Table I. TABLE I PARAMETERS SETTING OF CLOUD SIMULATOR Type Parameter Value Dataceter Number of Dataceter 10 Number of Host 5 Virtual Machie (VM) Type of Maager Space_shared, Time_shared Number of PE per Host 2-4 Badwidth 2000 Host Memory (MB a ) 2048-10240 Dataceter Cost (The cost of usig processig i this resource) Total umber of VMs 30-210 MIPS b of PE c 1000-2000 Number of PE per VM 1 VM Memory (MB) 512-2048 Badwidth (Bit) 1000 Type of Maager Time_shared Task Total umber of Tasks 100-700 Legth of Task (MI d ) 5000-20000 Number of PE per requiremet 1 Type of Maager Space_shared a MB = Megabyte, b PE = Processig Elemet, c MIPS (Millio Istructios Per Secod) is a measure of the processig speed of the computer, MI d is Millio Istructio. B. Parameters Settig of ABC Algorithm The parameters settig of improved ABC algorithm is show i Table II. TABLE II PARAMETERS SETTING OF IMPROVED ABC ALGORITHM Symbol Parameter Value Number of scout bees 1000 m Number of sites selected out of 5 visited site e Number of best site out of m select site 1 ep Number of bees recruited for best e site 800 sp Number of bees recruited for other (m-e) selected sites 200 C. Experimetal Results The evaluatio of the performace of the proposed method ca be described ad compared with the schedulig algorithm as the followigs: - First Come First Serve (FCFS) is cosiderig the sequece of arrival of the job. - Shortest Job First (SJF) is cosiderig the size of the job by selectig the shortest job first. - Logest Job First (LJF) is cosiderig the size of the job by selectig the logest job first. 10 There are two experimets; the first experimet is the compariso of the average makespa with the umber of requests chagig, the secod phase is the compariso of the average makespa with the umber of VMs chagig. I the first experimet, the compariso of the average makespa with the umber of requests chagig from the differet schedulig algorithm is performed. The umber of VMs is fixed as 50 VMs ad the umber of requests gradually icreased betwee 100 700 tasks. The x axis shows the effect o performace of icreased requests as show i Fig. 3. Fig. 3. Compariso of average makespa amog FCFS, LJF, SJF, ABC_FCFS, ABC_LJF, ad ABC_SJF algorithm based o the fixed Number of VMs ad the icreased umber of the requests. Fig. 3 shows the average makespa of the schedulig usig improved ABC algorithm with FCFS, LJF, ad SJF (ABC_FCFS, ABC_LJF, ad ABC_SJF), comparig with the origial schedulig (FCFS, LJF, ad SJF) which was based o 50 fixed VMs ad the umber of icreasig requests. The experimetal results idicated that, while the umber of requests was icreasig, the average makespa cosequetly icreased. It ca be cocluded that ABC_LJF performed the effective results i the optimal schedulig o the loaded system. I the secod experimet, the compariso of the average makespa with the umber of VMs chagig from the differet schedulig algorithm is performed. The umber of tasks was fixed as 500 tasks ad gradually icreased the umber of VMs from 30 to 210 VMs. The x axis shows the effect o performace i icreasig the system size (VMs). Fig. 4 shows the average makespa of the schedulig usig ABC_FCFS, ABC_LJF, ad ABC_SJF comparig with FCFS, LJF, ad SJF which was based o the fixed umber of tasks ad the umber of icreasig VMs. It ca be cocluded that ABC_LJF performed better tha all methods ad its performace is more promiet i scalability.
, March 12-14, 2014, Hog Kog Fig. 4. Compariso of average makespa betwee FCFS, LJF, SJF, ABC_FCFS, ABC_LJF, ad ABC_SJF algorithm based o the fixed umber of the requests ad the icreased umber of VMs. VI. CONCLUSION AND FUTURE WORK This paper presets ABC optimizatio algorithm which ca solve the Virtual machie schedulig maagemet uder the evirometal chagig of the umber of VMs ad requests o Cloud computig. Eve i chagig eviromet, Cloud computig eeds to be operated i a stable system. Therefore, ABC algorithm is suitable for Cloud computig eviromet because the algorithm is able to effectively utilize the icreased system resources ad reduce makespa. The experimetal results illustrated that the proposed methods of ABC_LJF performed effective results tha all methods ad its performace is more promiet i scalability. 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