A Resource Scheduling Algorithms Based on the Minimum Relative Degree of Load Imbalance

Transcription

1 Jounal of Communcatons Vol. 10, No. 10, Octobe 2015 A Resouce Schedulng Algothms Based on the Mnmum Relatve Degee of Load Imbalance Tao Xue and Zhe Fan Depatment of Compute Scence, X an Polytechnc Unvesty, X an , Chna Emal: xt73@163.com; @qq.com Abstact Dynamc vtual machne mgaton s a key technque technology n the cloud computng, an algothm s poposed n ths thess, whch s a dynamc esouce schedulng based on mnmum load mbalancng measuement. Fst of all, load balance n schedule judges the oveload phenomenon; we need to mgate VM (Vtual Machne) on physcal machne. Then, accodng to the ecods n load balance, we calculate the load mbalancng measuement of the othe ente wokng PM (physcal machne) elatve to the oveload host. Choose the PM whch has the mnmum load mbalancng measuement as canddate host. Smulaton esults ndentfy ths algothm s able to ealze effcent load balancng, acque an optmal esouce utlzaton of cloud computng system and mantan a low level of load mbalancng. Index Tems Cloud computng, vtual machne, esouce schedulng, dynamc mgaton, load balancng, QoS I. INTRODUCTION Cloud computng [1] s a sevce model, whch s calculated based on the shaed netwok esouce pool can be confgued (ncludng netwok, physcal stoage, applcatons, and sevces, etc.) and be convenent, ondemand access to. The emegence of cloud computng platfom make computng esouces lke wate and electcty and used by odnay uses, and thee s no doubt that ths technology wll bng huge benefts to the majoty of entepses. Now, many companes have launched commecal cloud computng platfom, such as EC2 [2], Google's App Engne [3], as well as Mcosoft's Azue Amazon [4], and so on. Ths pape manly studes IaaS [5] laye, and vtual machne placement s the key ssue of IaaS, ncludng the ntal placement of vtual machne placement and dynamc mgaton. In cloud computng platfom, computng nodes often esult n the need to mgate the vtual machne deployed on the faled node because of oveload o accdental falue, wtch n ode to ensue the elablty of the ente cloud platfom uns. In cloud computng, how to pefom lve mgaton of vtual machnes quckly and effectvely, makng cloud computng platfom obtan hghe hgh Manuscpt eceved May 24, 2015; evsed Septembe 23, Ths wok was suppoted by the Chna's Natonal Development and Refom Commsson (NDRC) and Hgh-tech ndustalzaton pojects (Shaanx NDRC [2009] no. 1365); X'an Polytechnc Unvesty doctoal eseach stat-up fund (BS0725). Coespondng autho emal: @qq.com do: /jcm esouce utlzaton, whle ensung the elablty of cloud platfoms, t Is a hot and dffcult pont of cuent cloud computng eseach [6]. In ecent yeas, many studes have focused on the cloud cluste load balancng poblem [7], and pesent a numbe of effcent algothms. In Lteatue [8], t put fowad a method of mgaton of mnmum cost of vtual machne placement. The algothm consde the dynamc vtual machne esouce allocaton contnung change and mgaton cost constants, and consde the cost of mgaton poblems dung the pocess n the ntal confguaton and mgaton of vtual machnes, to acheve the ultmate hgh esouce utlzaton and steady sevce qualty. Howeve, ths algothm had consdeed only one CPU computng esouces, and cloud computng esouces n the esouce pool have a multdmensonal attbutes. In Lteatue [9], t poposed a vtual machne schedulng method based on genetc algothms, whch can acheve the desed load balancng state, and ensue mnmal vtual machne mgaton ovehead, but the algothm gnoes the mpact ceated by data cente esouce utlzaton and Enegy consumpton closely elated paametes, so the Consdeaton s not compehensve enough. In Lteatue [10], t poposed a esouce allocaton mechansm, whch s based on the mgaton tme mantanng a mnmum, whle also mnmzng the numbe of the numbe of vtual machnes mgate vtual machne mgaton stategy. The algothm takes nto account the esouce utlzaton CPU, RAM and netwok bandwdth, but dd not consde the cluste physcal esouces. In Lteatue [11], t poposed a vtual machne lve mgaton stategy, whch combnes the pefomance pedcton algothm. Accodng to the aveage usage of CPU, memoy, I/O and netwok bandwdth, the algothm makes a sees of judgments about the vtual machne mgaton. Such as whethe the mgaton s tggeed, whch VMs should be mgated, and vtual machnes ae mgated to a wtch physcal machne, etc. Howeve, ths algothm eles on statstcal analyss of hstocal data heavly, theefoe, t eques a elatvely long tme to obtan statstcally vald data, and then stat the optmzaton pocedue. In Lteatue [12], fo low load physcal machne n cluste, t popose a vtual machne mgaton appoach, whch s to make these physcal machnes nto sleep mode by mgatng all the undeutlzed VMs on some physcal machnes. In ode to ensue the load balancng of the 2015 Jounal of Communcatons 760

2 Jounal of Communcatons Vol. 10, No. 10, Octobe 2015 system and save enegy costs. But the cloud platfom has the chaactestcs of andom assgnment equest and a physcal machne load eal-tme dynamc changes, t wll lead to fequent physcal machne ON and OFF. It can be seen that as to the cuent cloud computng fo how to mgate vtual machnes dynamcally to acheve load balancng cluste poblems, we poposed many effcent algothms, but most algothms ae stll sgnfcant defcences to be futhe mpoved. In cloud computng esouce schedulng pocess [13], takng nto account the e-deployment of vtual machne mgaton and ntegated natue of the host s a bg dffculty. Because the task's equests ae andom and physcal machnes have eal-tme dynamc load, we mgate all the vtual machnes fom the cluste of low load physcal machne to close zeo load physcal machne whch may cause fequent physcal machne ON and OFF. So ths pape poposed the vtual machne mgaton algothm whch focuses on how to exceed load theshold hgh load physcal machne vtual machne lve mgaton. Ths pape has made mpovements n the followng two aspects: Tadtonal load balancng algothms [14] only pay attenton to one aspect of dffeent load of the physcal machne. In ths pape, we also consde the CPU, memoy and Multdmensonal attbute of band wdth. It make judgment accodng to the elatvely unbalanced degee of physcal machne load and choose the physcal machne whch has the lowest degee of elatve load balancng to make the vtual machne mgaton edeployment, to acheve load balancng, and can effectvely mpove the system esouce utlzaton and guaantee a cetan QoS [15]. ceate a vtual machne n a physcal machne, each equest s known fo a "task", and these tasks ae stoed n the cloud clent based on the cental task of the cache queue. Dspatch centes also have a load balance, whch peods T as a unt, to keep tack of evey sngle physcal machne all load nfomaton (dynamc load paametes) [16]. The load memoy n montong cente s used to stoe the load nfomaton of each physcal machne. Combned wth the load balance s nfomaton, accodng to the optmzaton algothm, the task dspatche of the dspatchng cente assgn tasks to the elatve load mbalance of the smallest physcal machne. The physcal machne nstead of PM and sngle PM s Maked by PM, and 1, 2,3 R. The esouces of the physcal machne on PM s defned as I dmensonal, and the amount of host's esouces s epesented by N, and 1, 2,3 I. As we know, cloud computng esouces nclude CPU, memoy, bandwdth, stoage space, etc., and we use to epesent the dmenson of compute esouces to keep genealty. In ths pape, we take the multdmensonal attbute of computng esouces nto account. And select the mnmum elatve degee of load mbalance to do vtual machne mgaton and edeployment, accodng to the physcal machne load's elatvely uneven degee. III. RELATIVE LOAD IMBALANCE SCHEDULING ALGORITHM In the cloud system of ths pape, the task can be completed wthn a peod of tme (T). The nfomaton stoage of load balance s used fo stong the physcal machne load nfomaton of the tme S and S-1. Fst, fo the physcal machne unde nomal opeaton n cloud system, the system needs to detemne whethe thee s oveload n physcal machne. If thee s oveload, we need dynamc mgaton unnng vtual machne n the physcal machne. Fo one physcal machne, the aveage utlzaton of the PM s ( w ) n tme T. The total amount of classes II. SYSTEM MODEL AND PROBLEM DESCRIPTION Fstly, buld a cloud system model shown n Fg. 1. Compute temnals Use eque esponse Dspatch ng Cente Task Cache Task Schedule Cental task buffe queue Optmal Schedulng Algothm Load Balance computng esouces of each physcal machne s Seve Seve Seve Seve n cloud system, then the load aveage value of classes computng esouces s: Data Cente Seve Seve w w (1) Load nfomaton memoy Vtual Machnes Vtual Machnes E s elatvely small constant, the load wanng value s Montong cente E Fg. 1. Cloud system model On the bass of the detemnaton of the state of the physcal machne n cloud system accodng to the alam theshold set n advance, f the load exceeds the alam value, the dspatchng cente wll need to adopt a schedulng algothm to dynamcally mgate the vtual machnes [17]. Suppose that the oveload phenomenon of The Cloud System Model constucted n ths pape, ncludng a dspatch cente, a data cente and a montong cente. Thee s a cental task buffe queue n task cache of the dspatch. Assumng that customes n the cloud system accodng to the actual needs equest to 2015 Jounal of Communcatons (2) 761

3 Jounal of Communcatons Vol. 10, No. 10, Octobe 2015 the physcal machne PM occus n ths peod S, t s a need fo makng the vtual machne PM mgaton. Then, based on the load nfomaton of PM n the last tme S-1, all the load nfomaton of othe nomal opeaton seves can be calculated nto the followng fomula, B a C N I (3) 1 Cm Nm In ths fomula, B epesents the load elatve physcal machne elatve load mbalance degee of the physcal machne PM. C epesents the aveage utlzaton of computng esouce of the nomal opeaton physcal machne PM at the tme of S-1. Such as CPU, memoy, and so on the aveage utlzaton ate. N epesents the capacty of the computng esouces of the physcal machne PM. C m s the aveage utlzaton ate of the oveloads physcal machne esouces at tme S-1, and N s the esouce capacty of the oveload physcal m machne at the tme of S-1. a epesents the weghtng facto of computng esouces. The ntal value s 1, you can appopately ncease o decease the value of a to emphasze o weaken the load equements of esouce, dependng on the dffeent emphass n the pocess of nstantate establshment of the cloud platfom. Theefoe, afte usng the fomula (1) can be deved the load nfomaton ato of all unnng seves n cloud platfom to the efeence physcal machne, the dspatchng cente wll select the physcal machne wth elatvely mnmal degee of load mbalance as an altenatve host to mgate the vtual machne. Afte selectng the altenate host, the need wll be fo futhe study of the load state of the altenatve. Combned wth equements of the task, the altenate host wll be selected as host of the eventual deployment of vtual machne unde the ccumstance that the altenatve host wll not be oveloaded afte vtual machne mgated to the altenatve host. Assumng that the demand fo the necessay esouces to mgate vtual machnes s an I-dmensonal vecto, each dmenson epesents the demand fo each tem of computng esouces, as follows: 1 2 H h, h h h, 1,2,3 I (4) The load nfomaton of altenatve host at tme S got fom the stoage of load nfomaton s stll wtten n vecto fom: N n, n n n, 1,2,3 I (5) avalable 1 2 I Compae the sze of 1,2,3 I. I h wth n, and h n, Though the check on the altenate host accodng to the above constants to, f the above condtons ae not satsfed, we can select the host wth the smallest degee of elatve load mbalance as the altenate host except that host. It wll be checked agan accodng to the constant tll we can select the appopate altenatve host to make dynamc mgaton of vtual machne. In ths algothm, the emboded deas ae as follows: Fstly, detemne the load o oveload accodng to the load nfomaton of host to pefom the lve mgaton of vtual machne. Secondly, select the host wth the smallest elatve degee of load mbalance as the altenate host to make the conduct of vtual machne mgaton accodng to the elatve load mbalance degee calculated fom the load nfomaton of ths host and othe nomal opeatng hosts. Fnally, detect the esouce capacty of the altenate to select the one co; fomng to ctea to make the mgaton of vtual machne; othewse, e-select the host wth the smallest elatve degee of load mbalance fom the othe hosts except the hosts have been checked. It wll be checked agan accodng to the constant tll we can select the appopate altenatve host to make dynamc mgaton of vtual machne. The algothm n ths pape s pesented as follows: Algothm: Mnmum elatve degee of load mbalance schedulng algothm. 1) At Tme S, we detemne the physcal machne appea oveload phenomenon, you need to make the dynamc mgaton of vtual machnes fom the physcal machne. 2) Get the load nfomaton at tme S-1 of ths physcal machne and othe nomal unnng physcal machnes. 3) Calculate elatve degee of load mbalance schedulng algothm of all the nomal opeaton of the physcal machne elatve to the load and oveload to the physcal machne. 4) Select the physcal machne wth the smallest degee of the elatve load mbalance as an altenatve host fom the tem 3. 5) Check on the altenate host n tem 4 accodng to the constants to, f the above condtons ae not satsfed, we can select the host wth the smallest degee of elatve load mbalance as the altenate host except that host. It wll be checked agan accodng to the constant tll we can select the appopate altenatve host to make dynamc mgaton of vtual machne. IV. SIMULATION In ths pape, we use the smulaton expement CloudSm [18] to smulate cloud computng envonment, and all expements ae been confgued as a 64-bt X86 pocesso, 32G memoy, had dsk space on the seve 2T. In ths test, we do expemental analyss accodng to the CPU, memoy, and bandwdth Jounal of Communcatons 762

4 Jounal of Communcatons Vol. 10, No. 10, Octobe 2015 We ceate thee dffeent types of physcal machnes n a data cente n CloudSm platfom, these thee physcal machne s confguaton s as follows: Type TABLE I: PM CONFIGURATION PM1 PM2 PM3 CPU/GHz MEM/GB Band Wdth/MB In ths test, the vtual machne ceaton types ae sx knds, they wee andomly assgned wth equal pobablty to ceate a vtual machne tasks. Vtual machne confguaton as follows: Type TABLE II: VM CONFIGURATION CPU/GHz MEM/GB Band Wdth/MB VM VM VM VM VM VM It s not easy to smulate vtual machne mgaton envonment on Cloud Sm platfom needed fo ths to smulate cloud platfom by addng loads such as the pobablty of a andom batch jobs and batch jobs long tme to adjust. Modfy CloudSm the Datacente Boke module, evey 10s tasks andomly added to the vtual machne. The addton of andom factos, so the load s not stable, thee wll be fluctuatons. Dffeent vtual machnes and the host va the monto data ae detected, at the same tme the montong s not the same. The smulaton wll be automatcally temnated when the pocess s completed all the tasks, and theefoe modfy the smulaton end of the flag. When thee s no task, t does not close the physcal and vtual machnes evoked. In ths pape, n ode to vefy the effectveness of the algothm, expements wll conduct compaatve tests n the followng thee aspects. 1. Request a dffeent numbe of tasks, the numbe of vtual machne mgaton. 2. a dffeent numbe of task equest, the utlzaton of computng esouces wthn the system. 3. Befoe and afte the vtual machne mgaton, the system load s not balanced contast. Though the above thee compason test, to pove that the algothm poposed n ths pape has the advantage that t s effectve. In Expement 1, the numbe of tasks to pefom the same numbe of vtual machne mgaton pont of vew, to compae the schedulng algothm poposed n ths pape, the mnmum cost mgaton schedulng algothms and andom schedulng algothm [19]. Accodng to the numbe of tasks, such as pobablty dstbuton, ths wll ensue that the vtual machne nstance s ceated fo each type of equest fo the same amount. Montos and load balances data collecton nteval s 5 seconds. The esults shown n Fg. 2: Fg. 2. The numbe of tasks n dffeent numbe of vtual machne mgaton The above analyss shows that the Random Schedulng Algothm wdely used n the commecal cloud platfom has lttle actual mgaton of vtual machnes when n the small numbe of tasks, due to the moe openng physcal machnes, but when the numbes of tasks ncease, t becomes poo and need moe vtual machnes to be mgated. Ths s because of the uneven dstbuton of the lead load n the cluste. Compaed to the mnmum cost of mgaton algothm, the algothm poposed n ths pape can be found small pefomance gap to t. Ths s because these two algothms to choose the tmng of the vtual machne to a physcal machne selecton ae load exceeds a theshold value to detemne, and have focused on mantanng a balanced oveall load. When a task equests a smalle numbe, these two algothms to ensue the system load balancng, and physcal host oveload occus can be kept n a small numbe. When the numbe of tasks nceases, the vtual machne mgaton nceased because of the vtual machne can deploy the lmted physcal host and calculate the effectve utlzaton of the esouces ae lmted. In Expement 2, to consde the equest of dffeent tasks, t compaes the effects of thee types of schedulng algothms fo aveage system esouce utlzaton. The esults shown n Fg. 3: Fg. 3. The aveage system esouce utlzaton wth dffeent tasks numbe Above the smulaton esults we can found the aveage utlzaton ate of esouces though ths pape s much hghe than the mnmum cost of mgaton algothm and stochastc algothms. When the ntal opeaton, the load 2015 Jounal of Communcatons 763

5 Jounal of Communcatons Vol. 10, No. 10, Octobe 2015 s not a mnmum mgaton expense algothm poposed n ths pape to mpove the system and the esultng esouce utlzaton appoxmaton algothm, but the unnng tme s nceased when the load nceases, and the esultng algothm aveage esouce utlzaton has poposed sgnfcantly mpoved. Ths s because when thee s a physcal machne afte oveload poposed algothm befoe mgatng a vtual machne, and select the system s elatvely mnmal degee of load mbalance host mgaton as the destnaton host to ensue that the load balancng system; The mnmum load dung the mgaton pocess mgaton algothm consdes only the lagest of the taget host esouce utlzaton, oveall system esouce utlzaton was not consdeed. Ths shows that, n consdeaton of the system and loadbalanced condtons, the poposed algothm n the mplementaton of a numbe of tasks, can sgnfcantly mpove the utlzaton of system esouces, enhance the pefomance of the ente cloud system. In Expement 3, the pobablty of andomly geneated equest sx types of vtual machnes, and gadually ncease the numbe of tasks, vew the system load changes wthout equalzaton degees. The esults shown n Fg. 4: Fg. 4. The nequalty of the vtual machne befoe and afte mgate Fom the chat we can clealy found that afte the poposed method afte vtual machne mgaton, load mbalance of the whole system has been sgnfcantly educed. Ths s because po to mgaton, thee s a system load mbalance, some physcal machne long peod of hgh load state, and some of the physcal machne but a lage numbe of dle esouces. Afte the vtual machne mgaton, the ente system to acheve effcent load balancng, esouce utlzaton nceased, esultng n the utlzaton of each esouce vaance to solve the nequalty s lowe. V. CONCLUSION Studyng the pos and cons of esouce schedulng algothms mentoned by Scholas at home and aboad,it s theoetcally eveals how to acheve a condton n the cloud system unde the condton of guaanteeng QoS and load balancng, the nomal opeaton of the system s elatvely Oveload physcal machne hosts ae not elatve load equlbum s mnmum. Meanwhle, Though CloudSm smulaton platfom, fom the expemental pont of vew, It has obvous pefomance advantages fo andom schedulng algothm used n commecal cloud platfom, hgh esouce utlzaton and load the nequalty emaned at a low level. In futue wok, t s man task to put the schedulng algothm used n ths pape nto commecal cloud platfom n the load balancng pocess. Such as CloudStack, fo futhe test the possblty of pactcal applcaton of the algothm. REFERENCES [1] B. P. Rmal and E. Lumb, A taxonomy and suvey of cloud computng systems, n Poc. Ffth Intenatonal Jont Confeence on INC, IMS and IDC. IEEE Compute Socety, 2009, pp [2] S. Chas, R. Kaewpuang, B. S. Lee, et al, Cost mnmzaton fo povsonng vtual seves n amazon elastc compute cloud, n Poc. IEEE 20th Intenatonal Symposum on Modelng, Analyss and Smulaton of Compute and Telecommuncaton Systems. IEEE, 2011, pp [3] R. Podan, M. Spek, and S. Ostemann, Evaluatng hghpefomance computng on google app engne, Softwae IEEE, vol. 29, nol. 2, pp , Mach [4] M. Smms and M. Thomassy. (Octobe 2014). Mcosoft Inc. Best pactces fo the desgn of lage-scale sevces on Wndows Azue cloud sevces. [Onlne]. Avalable: [5] P. Kanas, A. Menychtas, V. Anagnostopoulos, et al., ElaaS: An nnovatve elastcty as a sevce famewok fo dynamc management acoss the cloud stack layes, n Poc. Intenatonal Confeence on Complex, Intellgent and Softwae Intensve Systems, 2012, pp [6] G. Fenu and S. Sucs, A cloud computng based eal tmefnancal system, n Poc. Eghth Intenatonal Confeence on Netwoks, Mach 2009, vol. 48, pp [7] X. Ren, R. Ln, and H. Zou, A dynamc load balancng stategy fo cloud computng platfom based on exponental smoothng foecast, n Poc. Intellgence Systems IEEE Intenatonal Confeence on Cloud Computng, Septembe 2011, vol. 15, pp [8] J. W. Jang, T. Lan, S. Ha, et al., Jont VM placement and outng fo data cente taffc engneeng, Infocom Poceedngs IEEE, vol. 131, no. 5, pp , Mach [9] S. Kau and A. Vema, An effcent appoach to genetc algothm fo task schedulng n cloud computng envonment, Intenatonal Jounal of Infomaton Technology and Compute Scence, vol. 4, no. 10, pp , Septembe [10] J. C. Mooe, H. R. Rao, and A. B. Whnston, Infomaton pocessng fo a fnte esouce allocaton mechansm, Economc Theoy, vol. 8, nol. 2, pp , [11] X. Wang, X. Lu, L. Fan, and X. Ja, A decentalzed vtual machne mgaton appoach of data centes fo cloud computng, Mathematcal Poblems n Engneeng, vol. 2013, pp , Mach [12] Y. Gao, H. Guan, Z. Q, et al., A mult-objectve ant colony system algothm fo vtual machne placement n cloud computng, Jounal of Compute & System Scences, vol. 79, nol. 8, pp , Febuay [13] D. Egu, G. Kou, Y. Peng, and Y. Sh, The analytc heachy pocess: Task schedulng and esouce allocaton n cloud computng envonment, The Jounal of Supecomputng, vol. 64, no. 3, pp , June [14] S. Sud, R. Want, T. Peng, K. Lyons, B. Rosao, and M. X. Gong, Dynamc mgaton of computaton though vtualzaton of the 2015 Jounal of Communcatons 764

6 Jounal of Communcatons Vol. 10, No. 10, Octobe 2015 moble platfom, Moble Netwoks and Applcatons, vol. 35, pp , Febuay [15] A. M. Shamsul, J. W. Mak, and X. M. Shen, Relay selecton and esouce allocaton fo mult-use coopeatve OFDMA netwoks, IEEE Tansactons on Weless Communcatons, vol. 12, no. 5, pp , May [16] Y. Zhang, X. Lao, H. Jn, L. Ln, and F. Lu, An adaptve swtchng scheme fo teatve computng n the cloud, Fontes of Compute Scence, vol. 8, no. 6, pp , Decembe [17] H. Lu, H. Jn, C. Xu, and X. Lao, Pefomance and enegy modelng fo lve mgaton of vtual machnes, Cluste Computng, vol. 16, no. 2, pp , June [18] R. N. Calheos, R. Ranjan, and A. Beloglazov, et al., CloudSm: A toolkt fo modelng and smulaton of cloud computng envonments and evaluaton of esouce povsonng algothms, Softwae Pactce & Expeence, vol. 41, no. 1, pp , Januay [19] P. Gaccone, et al., An mplementable paallel schedule fo nput-queued swtches, IEEE Mco Magazne, vol. 22, pp , Januay Tao Xue was bon n Shaanx Povnce, Chna, n He eceved the B.S. degee n mechancal engneeng fom the X 'an Jaotong Unvesty of Chna (XJTU), X 'an, n 1995 and the M.S. degee n compute scence fom the Nothwest Unvesty of Chna (NWU), X 'an, n 2000 and the Ph.D. degee n compute softwae and theoy fom the XJTU, n He s cuently an Assocate Pofesso n the Compute Scence Dept at X an Polytechnc Unvesty. Hs eseach nteests nclude Cloud Computng, Bg Data and Content-based Netwokng. Zhe Fan was bon n Henan Povnce, Chna, n He eceved the B.S. degee n softwae engneeng fom the HuangHua Unvesty of Chna, Zhumadan, n He s cuently pusung the M.S. degee wth the Depatment of Compute technology, X an Polytechnc Unvesty. Hs eseach nteests nclude Cloud Computng, Bg Data and Content-based Netwokng Jounal of Communcatons 765