Multi Objective Optimum Resource Scheduling for Cloud Computing Networks

Transcription

1 Internatonal Journal of Appled Engneerng Research ISSN Volume 12, Number 24 (2017) pp Research Inda Publcatons. Mult Objectve Optmum Resource Schedulng for Cloud Computng Networks P. Sowjanya 1 Research Scholar, Department of Computer Scence Engneerng, Jawaharlal Technologcal Unversty, Kukatpally, Hyderabad, Telangana, Inda. Orcd d: K.V.N. Suntha 2 Prncpal & Professor, Department of Computer Scence Engneerng, Padmasr Dr. BV Raju Insttute of technology - [BVRIT], College of Engneerng for Women, Bachupally, Hyderabad, Telangana, Inda. Orcd d: Abstract The Mult Objectve Optmum Resource Schedulng strategy for cloud computng envronment s addressed n ths manuscrpt. The dvergent metrcs depcted n regard to optmum resource utlzaton wth mnmal reschedulng rate and maxmum feasblty at resource utlzaton cost. A novel scale called resource optmalty rato s projected. The expermental study evnced the sgnfcance of the proposal that compared to other contemporary models. Keywords: Cloud computng, task schedulng, resource schedulng, MOORS, workflows, load balancng INTRODUCTION Smooth and effcent functonng of cloud computng technologes requres effcent allocaton of resources and an approprate job schedulng algorthm. Due to resource constrants present n cloud computng data centre, effcent technques for resource allocaton and n addton effcent job schedulng of these resources becomes vtal. The servce provders and users of cloud computng envronment are beneftng from computng patterns n cloud technologes. Revenue mprovsaton s the prmary deal of the servce provders, whch s crtcally proportonate to the maxmal usage of the correspondng resource by the target users under defned Servce Level Agreements. The servce provder attempts to acheve ths through the use of vrtualzaton technology along wth effectve job schedulng and reducng the power usage. Resource schedulng n cloud data centres s much more challengng amd the varyng load levels, dverse set of user patterns and prce constrants across dfferent geographes. A large part of contemporary lterature focused on developng algorthms for performng the prmary tasks n cloud computng- allocaton of resources and schedulng jobs. Recent technologcal advancements n IT have bult effectve resource sharng models for emergng dstrbuton systems lke grds and clouds. In [1], proxes were desgned for dssemnatng data and these proxes work as agents for ther respectve dssemnaton resources. The prmary goal of the model s on lowerng the dssemnaton overheads. A shared platform wth secure montorng of resources was proposed n [2]. It apples the exstng secure data sharng method to the cloud envronment by applyng the model for health data. However, the model faced scalablty ssues as several user requests were unaddressed. Schedulng through data aware algorthms was proposed n [3]. The key objectve of the ntegrated cloud envronment was mprovng resource utlzaton and quck completon of workflows. Novel cloud schedulng algorthm was developed n [4], whch ncorporates protocols for trust establshment to ncrease effcency of resource schedulng. In [5], vrtualzaton at user level coupled wth cloud gamng method was used for resource schedulng. The prme objectve of the study was to lower runtme delays and thereby to enhance resource utlzaton. Vrtualzaton technologes tend to have strong mpact on resource schedulng n cloud envronment. Several research works have been proposed on developng vrtualzaton solutons to buld cloud computng models. Adaptve schedulng schemes for Graphc Processng GPUs are proposed n [6]. The study used feedback control characterstcs to lower the runtme. The method was successful n lowerng runtme but the relablty of cloud remaned unaddressed. Ths research work developed a method called, Mult Objectve Optmum Resource Schedulng for Cloud Computng Envronment. The prme focus of the proposal s to reduce reschedulng rate for optmal task completon rate and thereby boost the resource utlzaton for each request made by cloud users. The followng secton detals the avalable works on dfferent schedulng models and technques put forward by researchers n contemporary lterature. Further, the secton 3 provdes detaled exploraton of the proposed MOORS method. The Secton 4 presents the expermental study and smulaton results n a cloud envronment n a graphcal presentaton. The 16094

2 Internatonal Journal of Appled Engneerng Research ISSN Volume 12, Number 24 (2017) pp Research Inda Publcatons. last secton of the research- secton 5 evaluates dfferent technques and dentfes the method wth better outcomes. RELATED WORKS Ease of system mantenance coupled wth resource management on scalable levels can be accomplshed through Vrtual Machnes n cloud computng. Deployment of VMs for better resource schedulng s ncreasngly beng adapted but large volume of VMs can deterorate the performance of the entre system. Accordngly, de-duplcaton of fles at scalable levels was proposed by researchers n [7]. They focused manly on lowerng the system overhead durng the process of resource schedulng. However, the method compromsed on computaton costs nvolved n the process. In [8], researchers attempted to lower these computaton costs n the cloud envronment by desgnng QoS (Qualty of Servce) drven task schedulng. Through named data networkng, researchers tred to support VM mgraton and thereby address load balancng ssues n the dstrbuted pattern [9]. Data-aware computaton technques for resource schedulng n cloud envronment have been studed n [10]. In [11], cuckoo based model was bult to support query based operatons n the cloud envronment, n partcular for computng applcatons wth large resource requrements. Performancebased schedulng model was nvestgated n [12]. The researchers compared dfferent performances and accordngly allocated resources. Nash Equlbrum s usablty n resource allocaton process n cloud computng s nvestgated n [13]. It appled auctonbased model for allocaton. The study n [14] desgned a determnstc algorthm to mprove effcency n allocaton n the cloud through dynamc task assgnments accordng to user requests. Researchers n [15] analysed dfferent job types along wth the avalablty of resources and developed an effcent data structure. But the model also faced lmtatons of hgh computaton costs nvolved n resource schedulng. In [16], heterogeneous VM nstances for workflows were used to optmze costs nvolved n schedulng. Researchers n [17] reled on mult-qos (Qualty of Servce) orented selecton of resources for buldng and effcent and relable sharng platform. For collaboratve computng, the researchers also used the prce assstance for resource control n cloud. The model was successful n effcent resource schedulng but compromsed on load balancng actvty. Mult-Objectve Optmum Resource Schedulng (Moors) The proposed model called Mult-Objectve Optmum Resource Schedulng (MOORS) assesses the mpact of multple objectves of the resources towards schedulng feasblty, such as reschedulng rate, job completon rate, cost, and Resource dle tme. In regard to ths here we proposed a new scale called Resource Optmalty Rato ( r opt ) that explores the scope of respectve resource qualty metrcs. The hgher values of the Resource Optmalty Rato ndcate the sgnfcance of that resource. The process of MOORS strategy follows: The controller shares the nformaton wth resource scheduler, whch s about the tasks to be scheduled that ncludes arrval tme, task completon tme out, requred resource, and cost lmt. Ths nformaton sharng can be fgure out through a task header n respectve to the task to be scheduled n sequence. The arrval tme s the sum of tme requred for a task to reach scheduler, and the tme taken to share the nformaton related respectve task that commonly referred as offset -tme. Let p( cf ) be the tme taken to process task header cf, ( cf ) be the tme taken by the cf to reach the scheduler from the controller and ( b ) be the assessed tme to transmt task b from controller to scheduler. Then arrval tme ett( b ) wll be measured as explored n Eq (1). ett( b ) p( cf ) ( cf ) ( b ).. Eq (1) Here n the Eq (1), ett( b ) s the total tme expected to be taken by task b to reach the scheduler. MOORS Schedulng Strategy Once the task header arrves, the scheduler ntates the process of schedulng. In regard to ths the scheduler explores the desred transmsson propertes called bandwdth n demand, requred tme slot of the resource. Further resource allocaton process under MOORS that explored followng. Intally the sad model s assessng the values of the projected resource s transmsson qualty metrcs of all avalable resources and orders these resources accordng to one of the projected qualty metrc that consdered as prmary requrement of the optmum resource utlzaton. The strategc approach to assess the scope of each optmum resource utlzaton metrc that projected n regard to the avalable resources s explored n followng secton. Upon recevng the task headers, scheduler schedules the respectve tasks to optmal resources that accomplsh the task successfully. The objectve of ths manuscrpt s an optmum resource schedulng n regard to acheve maxmal optmalty towards resource utlzaton and task completon. The set of resources that are avalable to schedule are R { r1, r2, r3,... r x }. The schedulng of a resource to a task s needed to be resource utlzaton, and task completon qualty specfc. The resource selecton by Resource Optmalty Rato that scheduled to correspondng task s proposed n ths manuscrpt. The factors of optmum resource utlzaton projected n ths regard are descrbed as follows: A resource often reflects dversfed scope and dvergent qualty of servce factors. The prorty of the metrcs related to resource utlzaton qualty s dfferent from one context to other. Hence, t s obvous to argue that a resource that ranked best under a QoS metrc s often not optmal under mult-objectve qualty factors. In the context of ths constrant, the proposal 16095

3 Internatonal Journal of Appled Engneerng Research ISSN Volume 12, Number 24 (2017) pp Research Inda Publcatons. consders the mult-objectve qualty factors to schedule a resource to correspondng task. The depcted multple objectves towards qualty of resource utlzaton are explored n followng descrpton. Reschedulng Rate: In regard to a resource, the rato of reschedulng observed aganst total number of tmes that resource scheduled. The optmalty of the resource s reflected by ths metrc wth lower value. Ths can be measured as follows: rsr( r ) ruc( r ) j1 1 reschedules true 0 ruc( r ). (3) o Here n the above equaton rsr( r ) ndcates the reschedulng rate of resource r, whch s the rato of the count of reschedules observed aganst the number of tmes resource r scheduled. The notaton ruc( r ) denotes the the actual number of tmes the resource r scheduled. Job completon rate (transmsson realzaton rate): The rato of successful task completons aganst total number of tmes resource scheduled, whch can be measured as follows: jcr( r ) ruc( r ) j1 1 0 successfull task completon task completon faled ruc( r ) (7) o Here n the above equaton jcr( r ) s ndcatng the job completon rate of resource r. Cost feasblty: The cost lmt s essental n regard to accomplsh the task wthn the clent s budget. The resource cost must be lower. If the resource cost s less than the cost lmt defned by end-user then t represents the optmal factor of the resource. The measurng of cost feasblty s as follows: cf ( r ) ca( r ) cl( t) (8) o Here n the above equaton cf ( r ) s ndcatng the cost feasblty of resource r, ca( r ) s ndcatng the actual cost of the resource r and the cl() t s the cost lmt to accomplsh the task t. o If cf ( r ) s less than or equals to 0, then the resource s cost s feasble, whch ndcates that the cost feasblty s optmal wth lower values Idle Tme Feasblty ( tf ): The Resource dle tme must be greater than the total tme requred to accomplsh the current task to be scheduled, f not, that resource s not elgble for schedulng, hence ths factor s also prncple QoS factor. Snce the Resource dle tme must be greater than the requred task completon tme and must not be greater than the sum of requred task completon tme and reserved task completon tme threshold tf. If t s greater than the requred and less than the resource dle tme feasblty threshold tft then t s optmal, f t s greater than the tft then t s nfeasble to schedule. Ths can be measured as follows: tf ( r ) ta( r ) tr( t) (9) o Here n the above equaton tf ( r ) ndcates the dle tme feasblty of resource c, the notaton ta( r ) ndcates the avalable resource dle tme r, and the notaton tr() t ndcates the requred resource dle tme r to transmt the target task. o If 0 tf ( r ) tf then the resource r s optmal f not nfeasble to schedule. Evaluaton strategy of Optmalty Rato of Resources Let reschedulng rate ( rsr ), job completon rate (transmsson realzaton rate) ( jcr ), cost feasblty ( cf ), and Resource dle tme ( tf ) as a set of QoS metrcs denoted for each resource r as M( ) { rsr, jcr, cf, tf }. r The QoS factors cf ( r), tf ( r) are prncple metrcs, whch are usng to fnd the compatblty scope of each resource. Ths key metrc s used to order the resources, whch assessed as follows Then fnd the key metrc as follows: Intal process normalzes the cost feasblty and resource dle tme as follows: step 1. foreach { rr R 1,2,3,..., R } Begn step 2. cf ( r) ca( r) cl( t) // assessng the cost feasblty, whch s the dfference between actual cost ca( r ) of the resource and the cost lmt cl() t prescrbed for the correspondng task t step 3. cf ( cf ( r )) //The set cf contans the olute values of the correspondng cost feasblty observed for each resource step 4. End x step 5. rr R Begn 1 step 6. cf ( r ) 1 max( cf ) cf ( r ) 1 // normalzng the cost feasblty such that the resource wth most optmal related to cost feasblty wll have hgher value, whch s between 0 and 1. step 7. End 16096

4 Internatonal Journal of Appled Engneerng Research ISSN Volume 12, Number 24 (2017) pp Research Inda Publcatons. step 8. foreach { rr R 1,2,3,..., R } Begn step 9. tf ( r ) ta( r ) tr( t) // the dle tme feasblty tf ( r ) of each resource r s the dfference between the dle tme avalable ta( r ) of the that resource r and dle tme requred tr() t for correspondng task t step 10. dff ( tf ( r )) //The set dff contans the olute values of the entres n dff step 11. End step 12. foreach { rr R 1,2,3,..., R } Begn step 13. tf ( r ) 1 max( dff ) tf ( r ) 1 // normalzng the resource dle tme (Resource dle tme State) such that the resource wth most optmal resource dle tme (Resource dle tme State) wll have hgher value, whch s between 0 and 1. step 14. End step 15. foreach { rr R 1,2,3,..., R } Begn step cf ( r ) tf ( r ) cf ( r ) 1 tf ( r ) 1 km( r ) cf ( r) tf ( r) //The resultant product s subtracted from 1, whch s snce, to obtan hgher value, as the product of two decmal fractons delvers the other decmal fracton that surely less than the decmal fractons nvolved n multplcaton. step 17. End Then the avalable resources are rated n regard to each metrc, such that each resource wll have ndvdual ratng for each metrc. For each metrc, resources wll be rated n ascendng order of correspondng metrc values, f hgher values are optmal, such that the resource havng lowest value for correspondng metrc wll be rated as 1, and the resource havng hghest value for correspondng metrc wll be rated as nn x, here the notaton x represents number of resources. If lowest values are optmal, then the resources wll be rated n descendng order of correspondng metrc values, such that the resource havng hghest value for correspondng metrc wll be rated as 1, and the resource havng lowest value for correspondng metrc wll be rated asnn x. Upon completon of the process, each resource reflects multple ratngs n regard to specfed qualty metrcs. Further these ratngs wll be used as nput to measure the Resource Optmalty Rato r opt as follows. For each resource { r 1,2,..., x} Begn km( r ) rsr( r ) jcr( r ) ( r ) (10) 3 // the above equaton represents the average of the ratngs obtaned for dfferent metrcs of resource r r ( r ) opt ( r ) km( r ) ( r ) rsr( r ) ( r) jcr ( r) (11) Resource optmalty rato r ( r ) s the nverse of root mean opt square dstance of the ratngs assgned to resource r, whch s snce, the lowest dstance s most optmal. Upon completon of assessng resource optmalty rato for gven resources, the resources wll be sorted n descendng order of ther ratng obtaned for key metrc. Further select the set of resources havng optmal ratng n regard to key metrc, whch s n accordance to gven threshold. The resources selected are sorted n descendng order of ther resource optmalty rato r opt, whch helps to project the best resource n frst place of the ordered lst. The same order s the preferred order to choose resources n regard to schedule the task. EXPERIMENTAL SETUP AND EMPIRICAL ANALYSIS The expermental study amed to compare the outcomes of the resource schedulng strategy MOORS that proposed and other contemporary models job schedulng wth effcent resource montorng (JS-ERM) [15] and cost based deadlne constraned workflow schedulng (DCWS) [16], whch are smulated usng Cloudsm [20] that enables to smulate the hgh dmensonal cloud computng network envronments. The nput jobs are syntheszed such that no order of prorty s applcable to the respectve jobs. The constrants executed to perform the smulaton are shftng of the jobs from one processor to other, and preempton are not allowed. The resources are scheduled usng the QoS factors consdered by proposed and other contemporary models those selected for performance analyss. Further we observed the performance metrcs those explored n next secton at dvergent tme ntervals. The proposed MOORS s evaluated by comparng wth other contemporary models JS-ERM [15], and DCWS [16]. The performance was scaled under QOS metrcs reschedulng rate, task completon rate, and resource utlzaton rate. The resource utlzaton rate observed for MOORS s hgh and stable that compared to other benchmarkng models JS-ERM, and DCWS (see Fgure 3). The reschedulng rate observed for MOORS s mnmal and lnear that compared to other benchmarkng models (see Fgure 1). Snce, the resource reschedulng rate s observed low n MOORS, obvously that delvers the best job completon rate (see Fgure 2). The process complexty s mnmal for MOORS, whch s low due to the scalable approach adapted for resource optmalty rato assessment

5 Internatonal Journal of Appled Engneerng Research ISSN Volume 12, Number 24 (2017) pp Research Inda Publcatons. Fgure 1: Resource reschedulng rate observed CONCLUSION The contrbuton of ths manuscrpt s a qualty aware schedulng algorthm that amed to schedule the resources and accordngly that endeavoured to optmze the task completon n cloud computng envronment. The manuscrpt addressed a novel scale called resource optmalty rato, whch ndcates the ftness of the resources under dvergent servce qualty metrcs proposed. The results obtaned from the model proposed were compared wth the other two contemporary models job schedulng wth effcent resource montorng (JS- ERM) [15] and cost based deadlne constraned workflow schedulng (DCWS) [16]. The performance analyss evncng that the proposed model s outperformed the other two contemporary models, n regard to dversfed qualty metrcs. The emprcal analyss of the proposed model can nfluence the future research to develop a schedulng and load balancng strategy to acheve optmal schedulng of vrtual machnes as resources n cloud computng. Fgure 2: Job Completon Rate observed Fgure 3: Resource utlzaton rate observed Fgure 1 s the representaton of reschedulng rate observed at dfferent tme ntervals of the smulaton. The observed reschedulng rate s aganst the task load. The fgure1 s ndcatng that the proposed MOORS s sgnfcantly best to mnmze the reschedulng rate that compared to other benchmark models. Fgure 2 s ndcatng that the job completon rate observed MOORS s also consderable and sgnfcant that compared to JS-ERM and DCWS, whch s snce, the mnmal reschedulng rate maxmzes the task completon rate, hence the consderable mprovement n task completon rate observed for MOORS. Fgure 3 depcts the MOORS advantage over other two contemporary models n regard to resource utlzaton rate, whch s prme objectve of the resource schedulng strateges. REFERENCES [1] Erdl, D. Cenk. "Autonomc cloud resource sharng for ntercloud federatons." Future Generaton Computer Systems 29.7 (2013): [2] Thlakanathan, Danan, et al. "A platform for secure montorng and sharng of generc health data n the Cloud." Future Generaton Computer Systems 35 (2014): [3] Zeng, Lngfang, Bharadwaj Veeravall, and Albert Y. Zomaya. "An ntegrated task computaton and data management schedulng strategy for workflow applcatons n cloud envronments." Journal of Network and Computer Applcatons 50 (2015): [4] Abbad, Imad M., and Anbang Ruan. "Towards Trustworthy Resource Schedulng n Clouds." IEEE Transactons on Informaton Forenscs and Securty 6.8 (2013): [5] Zhang, Youhu, et al. "A cloud gamng system based on user-level vrtualzaton and ts resource schedulng." IEEE Transactons on Parallel and Dstrbuted Systems 27.5 (2016): [6] Zhang, Chao, et al. "vgasa: Adaptve Schedulng Algorthm of Vrtualzed GPU Resource n Cloud Gamng." IEEE Transactons on Parallel and Dstrbuted Systems (2014): [7] Zhao, Xun, et al. "Lqud: A scalable deduplcaton fle system for vrtual machne mages." IEEE Transactons on Parallel and Dstrbuted Systems 25.5 (2014): [8] Bansal, Ndh, et al. "Cost performance of QoS Drven task schedulng n cloud computng." Proceda Computer Scence 57 (2015): [9] Xe, Rutao, et al. "Supportng Seamless Vrtual Machne Mgraton va Named Data Networkng n Cloud Data Center." IEEE Transactons on Parallel and Dstrbuted Systems 12.26, (2015)

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