Energy Aware Virtual Machine Migration Techniques for Cloud Environment

Transcription

1 Energy Aware rtual Machne Mgraton Technques for Cloud Envronment Kamal Gupta Department of CSE MMU, Sadopur jay Katyar, PhD Department of CSE MMU, Mullana ABSTRACT Cloud Computng offers ndspensable nfrastructure for storage and computng facltes for development of dversfed servces. The large utlzaton of resources leads to ncreased energy consumpton that has mposed a lmt on performance growth. Owng to hgh operatonal costs and carbon doxde footprnts, an effcent energy management technque needs to be developed and deployed that reduces overall energy consumpton of a cloud envronment whle mzng the resource utlzaton. In the frst phase of ths research, some vrtual machne mgraton technques were explored. In the second phase, a vrtual machne mgraton technque has been mplemented whch ams at reducng energy consumpton n cloud datacentres. General Terms rtual Machne Mgraton, Bn Packng Algorthms. Keywords Cloud Computng, rtualzaton, Energy Management. 1. INTRODUCTION Cloud Computng [1] s a provder of dynamc and dversfed servces over nternet by utlzng vrtualzed and scalable resources. It s known as servce orented paradgm as t provdes everythng as a servce. A cloud can be termed as an executon envronment of resources whch provdes metered servces at multple levels to multple stakeholders n a very effcent and elastc manner. It allows for executon of applcatons and servces n a much managed way. The term managed ensures relablty n ts operatons accordng to already stated qualty parameters. Elastcty depcts dynamsm and scalablty n resource utlzaton accordng to current requrements. The typcal servces of cloud that can be shared over nternet nclude platform, software s and nfrastructure [2]. The term Cloud Computng elaborates the platform and the type of applcaton. As a platform, cloud confgures, reconfgures and supples servers that can be physcal machnes or vrtual machnes. As a computng faclty, t ncludes applcatons that are accessble over nternet through supportng large data centres. Data centres are composed of powerful servers that ams to host web applcaton and servces [3]. Cloud systems [4] can be classfed nto three broad categores, namely, prvate clouds, publc clouds and hybrd clouds. The provder of cloud servces multplexes heterogeneous demands of users for computng resources such as bandwdth, storage, CPU etc. through vrtualzaton technque. The technque of vrtualzaton [5] ams to mze the utlzaton of avalable resources such as network, storage, processor etc. It ams to reduce cost of IT operatons by combnng a number of dle resources together to create shared pools. It can be accomplshed by creatng vrtual machnes that operates smultaneously. rtualzaton technque ncludes the process of M creaton, placement and mgraton. Wth the help of such technology, a sngle data centre or hgh power server can be slced to act as multple machnes. The number of vrtual machne, a system may be dvded, depends upon the hardware confguraton of system. Therefore, vrtualzaton s an effcent technque that ncreases resource utlzaton and thus helps to conserve energy. The cloud data centres are capable of housng a large number of IT equpments whch consumes enormous amount of energy for ther servces. The ncrease n amount of energy consumpton has become a major concern for cloud data centres as t leads to large emsson of carbon doxde, hgher operatonal cost and thus shorter lfe of hardware equpments [6]. The vrtualzaton technology helps n mprovng power effcency of the data centres by consoldatng the workload of several physcal machnes onto a sngle machne by creatng multple Ms. As a result, many physcal machnes gets turned off because of shftng of load [7][8]. Thus, vrtualzaton technque refers to abstracton of computer resources (such as CPU, storage, network, memory, applcaton stack and database) from the applcatons and the end users consumng the servce [9][10].Energy Management Technques n cloud are mplemented both at hardware and software level as shown n Fg.1. At hardware level, the hardware devces are montored for reducng the overall consumpton of energy and at software level, the vrtualzaton technque helps n reducton n energy consumpton. 2. LITERATURE SUREY The exstng solutons for energy effcent resource allocaton cannot be mplemented for Green computng as they only focus on mnmzng the energy consumpton n cloud envronments or on mnmzng the operatonal cost. Such solutons do not take nto account the dynamc and heterogeneous needs of cloud users and ther applcatons [15]. The technque of power-aware schedulng has been addressed that reduces CPU energy consumpton through dynamc voltage scalng n hard real-tme systems. Some exstng ntertask voltage schedulng schemes have been explored and on the bass of study a new technque s proposed that outperforms exstng technques. 11

2 Influental work of [16] llustrates challenges, archtectural elements and vson for energy-effcent management of components of cloud. Dynamc resource provsonng has been consdered as major force drvng cloud envronments. Stress has been lad on establshng synergy between several nfrastructural resources of a data centre. The research work proposes energy-effcent polces for allocaton of resources, archtectural prncples for energy-effcent cloud management, a novel algorthm (software) for energyeffcent cloud management. The survey done n [17] presents varous resource utlzaton technques that has been used to make data centres more energy effcent. The technques explored n ther research work, utlzed the concept of vrtualzaton to reduce the energy consumpton and a comparson has been made wth other exstng computng archtectures. The research work carred out n [18] elaborated vrtual machnes as solated boxes that shares and offers resources as when needed and serves the clents n the way as the real servers do. These machnes are connected to each other va same or a dfferent network. The concept of Hypervsor s dscussed and has been elaborated as a mn operatng system capable of runnng several vrtual machnes by supervsng and controllng them through establshment of communcaton and resource sharng. The advantage of vrtualzaton as dscussed n the research work s hgh avalablty. rtual machnes are formulatons of software's whch can be coped to other locatons wthout dffcultes, f any trouble occurs to the physcal servers or ts related devces. A bref descrpton of network and storage vrtualzaton has been depcted. In the last secton, the use of vrtualzaton n dfferent layers of cloud servces model s presented. In [19], survey of vrtualzaton n cloud computng has been done. The research work explans possble threats for cloud servce users and cloud servce provders. Addtonally, several attacks on cloud computng has also been dscussed. Further, some potental solutons for handlng these attacks have been proposed. Technques have been elaborated n [20] wth whch vrtualzaton enhances securty n a cloud envronment. It has been dscussed that by protectng both the ntegrty of cloud nfrastructure components and guest vrtual machnes, securty can be ncreased. An Advanced Cloud Protecton System (ACPS) has been proposed that can be deployed on varous cloud solutons for guaranteeng ncreased securty n cloud paradgm. ACPS s capable of effectvely montorng the nfrastructure components and ntegrty of guests whle remanng completely transparent to cloud users and vrtual machnes. ACPS has been deployed and talored onto varous cloud mplementatons. ACPS has been mplemented on current open source solutons. The proposed approach has been found to be effectve and protects machnes from several attacks. The survey work presented n [21] ncludes mechansms wth whch effcency n cloud data centres of task completon can be enhanced. The study explores varous cloud computng technques such as vrtualzaton, energy management and resource allocaton. The prmarly goal of the research work conducted was to walk around the technques that leads to reduced energy consumpton n data centres. Wth the mplementaton of ncreased computng n consumer, scentfc and busness domans, profound concerns have rsen up relatng to tremendous energy consumpton and assocated costs. In [22], solutons for Green Cloud Envronment have been presented that ams to mnmze ts envronmental mpact. It can be accomplshed by takng nto consderaton statc and dynamc portons of the cloud components. The proposed methodology presents a generc model to capture cloud computng data centres. Some energy consumpton patterns have been nvestgated and t has been concluded that by applyng approprate optmzaton polces, 20% of energy consumpton can be saved n cloud envronment. The research work presented n [23] explores all areas that lead to ncreased energy consumpton n a typcal cloud envronment. Addtonally, some methodologes have been addressed that can decrease power utlzaton wthout compromsng overall performance and Qualty of Servce. Power Usage Effectveness (PUE) and Data Centre Infrastructure Effcency (DCIE) are two measures that have been used to calculate energy consumpton n a data centre. The next secton presents a bref survey of energy effcent resource schedulng. It has been concluded that few components of cloud archtecture are responsble for ncreased amount of power dsspaton such as host machnes, IT equpment etc. 3. IRTUAL MACHINE MIGRATION TECHNIQUES Gven a number of vrtual machnes and physcal servers, a feasble placement of avalable M's onto the physcal machnes s to be derved that mnmzes the total energy spent by the actve physcal machnes. Exstng Bn Packng vrtual machne mgraton technques have been explored and mplemented n ths secton. Further, a new vrtual machne mgraton technque has been developed and mplemented that reduces the energy consumpton n a cloud envronment. 3.1 Exstng Algorthms [24] Low Perturbaton Bn Packng Algorthm (LPBP) LPBP technque for M Mgraton focuses on keepng the number of mgratons less by slowng down the transton between the exstng and a new M-server assgnment scheme. The lst of servers s mantaned n descendng order of ther computng capactes. The algorthm ntates by calculatng the power consumpton by each server. Then, t computes the total energy spent n the data centre. Fnally, t mgrates the vrtual machnes of the most energy consumng server to the least energy consumng server, provded the total computng capacty consumed by vrtual machnes ( ncludng the newly mgrated M s) of the server (on whch mgraton s to be performed) should not exceed the server s total computng capacty. Else, no mgraton wll be performed and the consdered server s to be removed from the server lst. S x = Least energy consumng server S y = Most energy consumng server CPU = Computng capacty of server x. U (F,t)= Utlzaton of S as a functon of placement. P (F,t)= Power consumpton of a server S. P.= Maxmum power consumed by a server. Re q _ CPU x = Computng requrement of a M on k server. N= Number of servers. 12

3 T_CPU = Total Computng requrement of all Ms on Server S. Algorthm: 1. Intalze capacty requrements for Req_CPU, CPU and P for each server S. 2. Randomly assgn M onto server such that each server s allotted atleast one M. 3. Fnd Energy Consumpton of each server S as P (F,t) P (F,t)= 0.7 p * U (F,t) U (F,t)= F k * (Req_CPU (t)/ CPU ) 4. Arrange the servers n descendng order of ther energy consumpton. 5. Compute the total energy consumpton of the system. 6. Compute T_CPU y = 7. If T_CPU y + T_CPU x <= CPU x then a. Mgrate the Ms of S y to the S x. b. Swtch off S y and delete ts entry from server lst. 8. Compute the total energy consumpton of the system Best Ft Decreasng (BFD) BFD s a bn-packng algorthm whose man goal s to reduce the total energy spent n vrtualzed cloud envronment. Intally, the algorthm consders all the servers to be unused and unassgned. Then, on the bass of computng capacty of servers, the M s are assgned to them. The assgnment starts wth the server havng mnmum computng capacty. The M s lst s mantaned n descendng order of ther computng capacty. The mum capacty demandng M s consdered frst and gets placed on mnmum computng capacty server. Ths process contnues untl the entre M s are allocated to the servers or the computng capacty of the consdered and placed M s on a server exceeds the server s computng capacty. Once, the placement s done, the total energy spent n the cloud envronment s calculated accordng to the method followed n LPBP. Algorthm: 1. Intalze capacty requrements for Req_CPU, CPU and P for each server S. 2. Arrange the M s n descendng order of ther computng capactes n M lst. 3. Arrange the servers n ascendng order of ther computng capactes n Server lst. 4. Repeat step 5 untl all M s are allocated 5. For each server S, assgn the M from top of the lst to S such that, T_CPU <=CPU T_CPU = 6. Swtch off all the servers whch do not contan any M and delete the entry from Server lst. 7. Fnd Energy Consumpton of each server S as P (F,t) P (F,t)= 0.7 p * U (F,t) U (F,t)= F k * (Req_CPU (t)/ CPU ) 8. Compute the total energy consumpton of the system Power and Computng capacty- Aware Best Ft Decreasng (PCA-BFD) PCA-BFD s a modfed algorthm whch works n the same manner as BCD does. The only dfference les n the consderaton of servers for ntal placement of M s. Here, the servers are consdered for allocaton on the bass of P / CPU rato. Then, the computed value for servers s arranged n ncreasng order. The server havng the mnmum P / CPU value s allotted the mum capacty M s frst and then the same process follows up as t was dscussed n BFD. Algorthm: 1. Intalze capacty requrements for Req_CPU, CPU and P for each server S. 2. Calculate P / CPU for each server S. 3. Arrange the servers n ascendng order of P / CPU value n the server lst. 4. Arrange the M s n descendng order of ther computng capacty n M lst. 5. For each server S, assgn the M from top of the lst to S such that, T_CPU <=CPU T_CPU = 6. Swtch off all the servers whch do not contan any M and delete the entry from Server lst. 7. Fnd Energy Consumpton of each server S as P (F,t) P (F,t)= 0.7 p * U (F,t) U (F,t)= F k * (Req_CPU (t)/ CPU ) 8. Compute the total energy consumpton of the system. 3.2 Developed Algorthm Proposed Algorthm Proposed Algorthm s a bn-packng algorthm whose man goal s to reduce the total energy spent n vrtualzed cloud envronment. Intally, the algorthm consders all the servers to be unused and unassgned. Then, on the bass of power consumpton of servers, the M s are assgned to them. The assgnment starts wth the server whose mum power consumpton value s least. The M s lst s mantaned n ascendng order of ther computng capacty. The mnmum capacty demandng M s consdered frst and gets placed on server havng ts P as the least value among the other servers. Further, f two or more servers have same P value, then allotment s done on the bass of ther computng capactes of servers. Ths process contnues untl the entre M s are allocated to the servers or the computng capacty of the consdered and placed M s on a server exceeds the server s computng capacty. Once, the placement s done, the total energy spent n the cloud envronment s calculated accordng to the method followed n BFD. Algorthm: 1. Intalze capacty requrements for Req_CPU, CPU and P for each server S. 2. Arrange the M s n ncreasng order of ther computng capacty. 3. Arrange the servers n ncreasng order of ther mum power requrements. 4. Servers havng same P value are arranged n descendng order of ther computng capactes. 5. Repeat step 6 untl all M s are allocated. 6. For each server S, assgn the M from top of the lst to S such that, T_CPU <=CPU 13

4 Energy Consumpton Energy Consumpton T_CPU = 7. Swtch off all the servers whch do not contan any M and delete the entry from Server lst. 8. Fnd Energy Consumpton of each server S as P (F,t) P (F,t)= 0.7 p * U (F,t) U (F,t)= F k * (Req_CPU (t)/ CPU ) 9. Compute the total energy consumpton of the system. 4. PERFORMANCE ANALYSIS A comparson among the four algorthms n terms of the total energy spent per unt tme s presented. Computng capactes for m servers and n vrtual machnes are randomly generated to fnd a sutable placement such that the overall energy consumpton can be mnmzed. The mum computng capacty of servers, mum power utlzaton of the servers and mum computng capacty of vrtual machnes has been kept as statc. On the bass of generated values, mum energy consumpton s calculated accordng to the mplemented M technques. Takng the value of CPU_Max=5000, P_Max=1500 and M_Max=450, energy consumpton n proposed algorthm n comparson wth other exstng algorthms s presented n Table 1, 3& 5 and Fgure 2, 3 & 4. Table 1 Smulaton results wth less Servers and Ms CPU_Max= 5000, P_Max=1500, M_Max=450 No. of Servers = 10, No. Of Ms= 30 LPBP BFD PCA-BFD EBFD Servers=10 rtual Machnes= LPBP BFD PCA-BFD PROPOSED Table 2 Energy Reducton by proposed technque n comparson to exstng technques wth less Servers & Ms. M Technque Energy Reducton(In Percentage) LPBP BFD PCA-BFD Table 3 Smulaton results wth average Servers and Ms CPU_Max= 5000, P_Max=1500, M_Max=450 No. of Servers = 10, No. Of Ms= 30 LPBP BFD PCA-BFD EBFD Fgure 3 represents energy consumpton wth 25 servers and 60 vms. Energy reducton (n percentage) n proposed algorthm n comparson to exstng algorthms s presented n Table 4. Table 4 Energy Reducton by proposed technque n comparson to exstng technques wth average Servers & Ms. M Technque LPBP BFD PCA-BFD Energy Reducton(In Percentage) Servers=25 rtual Machnes= LPBP BFD PCA-BFD PROPOSED rtual Machne Mgraton Technques rtual Machne Mgraton Technques Fgure 2 Energy Consumpton wth lesser machnes Fgure 2 represents energy consumpton by M mgraton technques when the computng capactes and the power requrements have been kept as statc. 10 servers and 30 vrtual machnes are taken to compute the total energy consumpton. Energy reducton (n percentage) n proposed algorthm n comparson to exstng algorthms LPBP, BFD, PCA-BFD s presented n Table 2. Fgure 3 Energy Consumpton wth average machnes Fgure 4 represents energy consumpton wth 48 servers and 88 vrtual machnes. 14

5 Energy Consumpton Servers=48 rtual Machnes= Fgure 4 Energy Consumpton wth more machnes Table 5 Smulaton results wth average Servers and Ms CPU_Max= 5000, P_Max=1500, M_Max=450 No. of Servers = 10, No. Of Ms= 30 LPBP BFD rtual Machne Mgraton Technques LPBP BFD PCA-BFD EBFD PCA-BFD PROPOSED Energy reducton (n percentage) n proposed algorthm n comparson to exstng algorthms LPBP, BFD, PCA-BFD presented n Table 6. Table 6 Energy Reducton by proposed technque n comparson to exstng technques wth average Servers & Ms. M Technque Energy Reducton(In Percentage) LPBP BFD PCA-BFD CONCLUSION In the research work, focus has been drected towards dervng a vrtual machne mgraton technque that reduces the overall energy consumpton n cloud data centres. As depcted n Fgure 1, energy management technques can be mplemented at both hardware and software levels. The research work was orented towards contrbutng n reducng energy consumpton at software level. To attan ths, some exstng vrtual machne mgraton technques were explored and on the bass of study, a new technque was proposed and mplemented. Smulaton results show that the proposed technque was effcacous n reducng energy consumpton n a number of M-server placements for effectve M mgratons wth less, average and good number of servers and vrtual machnes. Smulatng the proposed algorthm wth hgher number of servers and vrtual machnes on a vrtualzed data centre to reduce the energy consumpton consderably consttutes the future research work. 6. ACKNOWLEDGEMENTS We thank the experts from Maharsh Markandeshwar Unversty who are endowed wth nsght and expertse whch extensvely contrbuted n research fndngs and development of ths paper. 7. REFERENCES [1] S.Pnal, A Survey of arous Schedulng Algorthm n Cloud Computng Envronment, Internatonal Journal of Research n Engneerng and Technology, ISSN , olume 2, Issue (2), 2013, [2] S.Marston, Z. L, S. Bandyopadhyay, J. Zhang and A. Ghalas, Cloud Compuatng- The busness perspectve, Decson Support Systems, olume 51, Issue(1), , [3] The NIST Defnton of Cloud Computng, [4] Z.Zhang, Rchard T.B. Ma, J.Dng and Y.Yang, ABACUS: An Aucton-Based Approach to Cloud Servce Dfferentaton, IEEE, Internatonal Conference on Cloud Engneerng, 2013, [5] M.Duraraj and P.Kannan, A Study on rtualzaton Technques and Challenges n Cloud Computng, Internatonal Journal Of Scentfc & Technology Research, olume 3, Issue (11), 2014, [6] T. en and S. Mary, Dynamc Energy Management n Cloud Data Centers: A Survey, Internatonal Journal on Cloud Computng: Servces and Archtecture, olume 3, Issue (4), 2013, [7] Pragya and G..Manjeet, A Revew on Energy Effcent Technques n Green Cloud Computng, Internatonal Journal of Advanced Research n Computer Scence and Software Engneerng, olume 5, Issue (3), 2015, [8] C. Gong, J. Lu, Q. Zhang, H. Chen, and Z. Gong, The characterstcs of cloud computng, IEEE Internatonal Conference on Parallel Processng Workshops, 2010, [9] A. Younge, R. Henschel, T. Brown, G. Laszewsk, J. Qu and G. Fox, Analyss of vrtualzaton technologes for hgh performance computng envronments, IEEE, Internatonal Conference on Cloud Computng, 2011, [10] R.Metzner, T. Unger, R. Ttze and F. Leymann, Combnng dfferent mult-tenancy patterns n servceorented applcatons, IEEE Internatonal Conference on Enterprse Dstrbuted Object Computng, 2009, [11] A.Carpen-Amare, A.C. Orgere and C.Morn, Expermental Study on the Energy Consumpton n IaaS Cloud Envronments,IEEE/ ACM 6 th Internatonal Conference on Utlty and Cloud Computng, 2013, [12] S.Jng, S.Al, K.She and Y.Zhong, State-of-the-art research study for green cloud computng, Journal of Supercomputng, ol. 65(1), 2013, [13] L.Deboosere, B.ankesblck, P.Smeons, F.Turck, B.Dhoedt and P.Demeester, Effcent resource management for vrtual desktop cloud computng, Journal of Supercomputng, olume 62, Issue (2), 2012,

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