Survey on Virtual Machine Placement Techniques in Cloud Computing Environment

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1 Survey on Vrtual Machne Placement Technques n Cloud Computng Envronment Rajeev Kumar Gupta and R. K. Paterya Department of Computer Scence & Engneerng, MANIT, Bhopal, Inda ABSTRACT In tradtonal data center numbers of servces are run onto the dedcated physcal servers. Most of the tme, these data centers are not used ther full capacty n term of resources. Vrtualzaton allows the movement of VM from one host to the another host,whch s called vrtual machne mgraton, so these data centers can consoldate ther servces onto lesser number of physcal servers than orgnally requred. Vrtual machne placement s the part of the VM mgraton. To map the vrtual machnes to the physcal machnes s called the VM placement. In other word, VM placement s the process to select the approprate host for the gven VM. For the effcent utlzaton of the physcal resources, VM should be placed on to the sutable host. So many vrtual machne placement algorthms have been proposed by dfferent researchers that run under cloud computng envronment. Most of the VM placement algorthms try to acheve some goal. Ths goal can ether savng energy by shuttng down some severs or t can be maxmzng the resources utlzaton. Four steps are nvolved n the VM machne mgraton process. Frst step s to select the PM whch s overload or undreloaded, next step s to select one or more VM, and then select the PM where selected VM can be placed and last step s to transfer the VM. Selectng the sutable host s one of the challengng task n the mgraton process, because wrong selecton of host can ncreased the number of mgraton, resource wastage and energy consumpton. Ths paper only focuses to the thrd step that s selectng a sutable PM that can host the VM. It shows an analyss of dfferent exstng Vrtual Machne s placement algorthms wth ther anomales. KEYWORDS Vrtual machne, data centers, vrtualzaton, mgraton, power savng 1. INTRODUCTION Durng the last several decades, cloud computng s the new emergng technology n the feld of computer scence. It became so famous because of ther cheap, on-demand and pay as use servces [1]. Cloud computng provde three type of servces that s Software as a Servce (SaaS), Platform as a Servce (PaaS) and Infrastructure as a Servce (IaaS) [2] and t can be deploy n three dfferent way that s prvate, publc and protected [3][4]. Prvate cloud can only access wthn an organzaton, publc cloud can be access anywhere n the word and the hybrd cloud s the combnaton of prvate and publc cloud. Prvate cloud offer more securty than publc cloud. DOI : /jccsa

2 Fg. 1 cloud computng model Vrtualzaton [5, 6] s the key technology behnd the cloud computng. It ncreased the resource utlzaton by sharng the physcal resources among multple users. Vrtual machne montor (Hypervsor) s responsble for all management related to the VM.e. VM creaton, destructon and schedulng. It s a small software program whch resde between the hardware and the host OS must be nstalled onto the each host of the data center. Fg.2 Cloud framework Hypervsor create the VM for each user accordng to the user s requrement. These VMs are ndependent. One or more VM can assgn to the user and each VM can run multple applcatons. Furthermore, numbers of vrtual machnes are hosted on a sngle host. In the cloud envronment resource requred by the VM are changed dynamcally, sometmes resource requred by the VM are not fulfl by the PM n whch VM are currently runnng. Ths problem can be solved ether by addng some resources or by mgratng VM. VM mgraton [7, 8,] s the process of movng VM from one physcal machne to another physcal machne. VM mgraton solves the problem such as fault tolerance, load balancng and resource consoldaton etc. VM mgraton process conssts of four steps. Frst step s to select the PM from whch the VM s mgrated, second step s to select the VM for the mgraton, next step s to select the PM, where the selected VM wll be 2

3 placed and last step s to transfer the VM. Out of these four step thrd step.e. VM placement s the more challengng task, because t drectly affect the system performance. VM placement can be defned as a mappng between the VM and the PM. In the cloud envronment there are number of host and each host run number of VMs. If the number of physcal and vrtual machnes are less then statc VM placement can be possble, but f the number of vrtual machne and physcal machne are more, dynamc VM placement methods s requred. If n s the total number of vrtual machne and m s the total number of host then the number of possble mappng can be calculate by the followng equaton [9] Number of possble mappng = m n Ths ndcates as the number of vrtual and physcal machne are ncreased, vrtual machne placement becomes a challengng task. 2. CLASSIFICATION OF VM PLACEMENT ALGORITHMS Goal of the VM placement can ether savng energy by shuttng down some severs or t can be maxmzng the resources utlzaton. Based on these goal placement algorthm are classfed nto two type.. Power Based approach. Applcaton QoS based approach Man am of the power based approach s to save the energy. In these approach VM map to the physcal machnes n such a `way, that each servers can utlzed ther maxmum effcency and the other servers can be shut down dependng on load condtons. Whle n the Applcaton QoS based approach a VM map to the PM wth the am of maxmzng the QoS delvered by the servce provder. 3. LITERATURE SURVEY VM placement problem s a non determnstc problem. Number of vrtual machne placement algorthms have been proposed [10, 11, 12] that run under cloud computng envronment. Ths secton explans some of the extng vrtual machne placement approaches and ther anomales Heurstc based approach Frst Ft It s a greedy approach. In ths approach scheduler vsts the PM sequentally one by one and placed VM to frst PM that has enough resources. Each tme when the new VM arrved, scheduler starts searchng the PM sequentally n the data center tll t fnds the frst PM that has enough resources. If none of the physcal machne satsfed the resource requrement of the VM, then new PM s actvated and assgns VM to the newly actvated PM. Man problem wth ths approach s that load on the system can mbalanced Sngle Dmensonal Best Ft Ths methods use the sngle dmenson (CPU, memory, bandwdth etc.) for placng a VM. When VM arrved, scheduler vst the Physcal Machnes n the decreasng order of ther capacty used n a sngle dmenson and place the VM to the frst PM that has the enough resources. That means 3

4 VM place to the PM whch used the maxmum capacty along wth the gven dmenson. Problem wth approach s that t can ncrease the resource mbalancng because resource n the cloud s mult-dmenson (CPU, memory, bandwdth etc.). So there may be a stuaton where a host utlze ther full CPU capacty whle other resources such as memory and bandwdth are underutlzed. N. Rodrgo et al. [17], proposed a heurstc for the mappng between VM and PM. Man am of ths approach s to balance the CPU utlzaton on each PM. Only CPU utlzaton s consder as a load metrc, so after the mappng only amount of avalable CPU s check nstead of whole PM. Objectve functon of ths method s to mnmze the standard devaton of resdual CPU n each PM. If there s n host n the data centers and m s the number of VM n each host then objectve functon can be defne as = = Where rcpu PM the th PM and s the reamng CPU capacty of the th PM, cpu PM s the total CPU capacty of s the CPU used by the j th VM. Problem wth ths approach s that they only consder the CPU capacty for mappng between the PM and VM. So other resource can be mbalance Volume Based Best Ft Ths heurstc used the volume of the VM for placng a VM. Ths approach vsts the Physcal Machnes n a decreasng order of ther volume and place VM to the frst PM that has the enough resources. That means Physcal Machne whch has the maxmum volume wll be consdered frst. Sandpper [13, 14] s a Xen based automated system, whch s used for detectng and mtgatng the hot spot. It use the sand-volume to place the vrtual machne, whch s gven by the formula Sand-volume= * * Where cpu, net and mem are the normalzed utlzaton of the cpu, network and memory respectvely. In ths method Physcal Machnes are vsted n the decreasng order of ther sandvolume and place VM to the frst PM that has the enough resources. So the Physcal Machne whch has the maxmum sand-volume wll be consdered frst. Ths approach may select the wrong PM because they are not consdered the shape of the resource utlzaton. Man reason to select the wrong PM s to convert 3D (CPU, network, memory) resource nformaton nto the 1D that s sand-volume. So f two PM havng the same sand-volume then both physcal machnes are equally sutable for placng VM, ths may not be possble. 4

5 Dot Product Based Ft In ths heurstc resource requrement of vrtual machne and the total capacty of the physcal machne along the specfed dmensons are expressed as vectors. Dot product of these two vectors s calculated and then PMs are arranged nto the decreasng order of ther dot product. VectorDot [7] method use the dot product of resource utlzaton vector ( machne and resource requrement vector of vrtual machne ( and choose the physcal machne whch havng lowest dot product. ) of physcal ) to select physcal machne For the proper utlzaton of the resources t s necessary that the vrtual machne whch requred more CPU and less memory should be placed on the physcal machne whch has low CPU and more memory utlzaton. Ths method seems good, but t can choose the wrong PM, because they the not usng the length of vrtual machne ( machne. 3.2 Constrant based approach ) and the remanng capacty of the physcal Constrant based approaches are use n the combnatoral search problems [15]. In these approach some constrant are apply and these constrant must be fulfl durng VM placement. These constrants are Capacty Constrants: For all dmenson (CPU, memory and bandwdth) of a gven physcal machne, sum of the resource utlze by all VMs runnng n that host should be less than or equal to the total avalable capacty of that physcal machne cpu < C cpu j m mem < C mem j m bw < C bw j m Where C cpu, C mem and C bw s the total cpu, memory and bandwdth capacty of the th host respectvely and u cpu, u mem, and u bw are the total CPU, Memory and bandwdth used by the all VM n the th host respectvely. Placement Constrants: All vrtual machnes must be placed on to the avalable host. SLA constrant: VM should be placed to the PM where t fulfls the SLA. Qualty of servces (QoS) constrant: Some qualty of servces constrant such as throughput, avalablty etc. must be consdered durng the VM placement. Constrant Programmng s useful where the nput data s already known. That means we know the demands of the VMs, before calculatng the cost functon. 5

6 3.3 Bn packng problem Bn packng problem [16] s a NP hard problem. If PM and the VM are consder as a three dmenson object, then VM placement problem s smlar to the bn packng problem where tem represent the VM and contaner represent the PM. In the bn packng problem number of tem (VM) are placed nsde a large contaner (PM). The am s to places a number of tems nto a sngle contaner as possble. So that the number of contaner requred to packng the tem s mnmzed. Bn packng problem s dfferent from the VM placement problem. In the bn packng problem bns can be placed sde by sde or one on top of the other. But n the case of VM placement, placng VMs sde by sde or one on top of the other s not a vald operaton. Ths s because the resource cannot be reused by any other VM, once a resource s utlzed by a VM. 3.4 Stochastc Integer Programmng Stochastc Integer Programmng s used to optmze the problems, whch nvolve some unknown data. VM placement problem can be consder as a Stochastc Integer Programmng because resource demand of the VM are known or t can be estmated and the objectve s to fnd the sutable host whch consume less energy and mnmze the resources wastages. Stochastc Integer Programmng can be use where the future demands of the resources are not known, but ther probablty dstrbutons are known or t can be calculated. 3.5 Genetc Algorthm Genetc algorthm s a global search heurstcs. It s useful where objectve functons changed dynamcally. Ths approach s nspred by the evolutonary bology such as nhertance. Genetc Algorthm can be use to solve the bn packng problem wth some constrants. It s useful for the statc VM placements, where the resource demands do not vary durng a predefne perod of tme. 4. CONCLUSION Effcent placement of the VM can mproved the overall performance of the system. Vrtual machne placement s a technque whch maps the VM to the approprate PM. Snce the sze of the data center s large n the cloud computng envronment, so selectng a proper host for placng the VM s a very challengng task durng the vrtual machne mgraton. In ths paper number of VM placement approach are explaned wth ther anomales. Each placement algorthm performs well under some specfc condtons. So t s a crtcal task to choose a technque that s sutable for both the cloud user and cloud provder. 6

7 REFERENCES [1] R. Buyya et al., Cloud Computng and Emergng IT Platforms: Vson, Hype, and Realty for Delverng Computng as the 5th Utlty", Future Generaton Computer Systems, vol. 25, no. 6,june 2011, pp [2] Mell, P. et al., The NIST Defnton of Cloud Computng. NIST Specal Publcaton, [3] Sosnsk et al. Cloud Computng Bble, 1st ed., USA : Wley Publshng Inc.,2012 [4] Km et al., Expermental Study to Improve Resource Utlzaton and Performance of Cloud Systems based on Grd Mddleware proceedng n frst Internatonal Conference on Internet pp , December [5] Borja Sotomayor et al., Enablng cost-effectve resource leases wth vrtual machnes, Research Gate artcle may [6] L. Cherkasova et al. When vrtual s harder than real: Resource allocaton challenges n vrtual machne based t envronments n proc. 10th conference on hot topc n operatng system, Vol. 10, pp.20-20, [7] Mayank Mshra et al., On Theory of VM Placement: Anomales n Exstng Methodologes and Ther Mtgaton Usng a Novel Vector Based Approach, IEEE/ACM 4th nternatonal conference on cloud computng, pp , July [8] Y. Wu et al. Performance modelng of Vrtual Machne Lve Mgraton, IEEE/ACM 4th Internatonal conference on cloud computng, pp , July [9] Chrs Hyser et al., Autonomc vrtual machne placement n the data center, Techncal report, HP Labs, Feb [10] A. Beloglazov et al., Energy effcent allocaton of vrtual machnes n cloud data centers, proceedng n 10th IEEE/ACM Intl. Symp. on Cluster, Cloud and Grd Computng, pp , may [11] Le Xu,Wenzh et al., Smart-DRS:A Strategy of Dynamc Resource Schedulng n Cloud Data Center, IEEE Internatonal conference on Cluster Computng Workshops, pp , Sept [12] N. Bobro et al., Dynamc placement of vrtual machnes for managng SLA volatons, proc. n 10th IEEE nternatonal symposum on ntegrated network management pp , May [13] C. Clark et al., Lve mgraton of vrtual machne, proceedng of the 2nd conference on symposum on network system desgn and mplementaton, vol. 2, pp , [14] T. Wood et al., Black-Box and Gray-Box strateges for vrtual machne mgraton, NSDI'07 Proceedngs of the 4th USENIX conference on Networked systems desgn & mplementaton, pp. 7-17, 2007 [15] Constrant programmng. [16] [17] N. Rodrgo et al. A Heurstc for Mappng Vrtual Machnes and Lnks n Emulaton Testbeds n the proceedng of 9th IEEE nternatonal conference on parallel computng, pp ,

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