Load Balancing By Max-Min Algorithm in Private Cloud Environment

Transcription

1 Internatonal Journal of Scence and Research (IJSR ISSN (Onlne: Index Coperncus Value (2013: 6.14 Impact Factor (2013: Load Balancng By Max-Mn Algorthm n Prvate Cloud Envronment S M S Suntharam Department of Cloud Computng, SRM Unversty, Chenna, Inda Abstract: Cloud computng s a latest emergng technology because of ts hgh avalablty, hgh performance, low cost and pay for use model. Wheren IT nfrastructure and applcatons are provded as servces to end-users. It enables On-Demand servces where resources can be commssonng and decommssonng accordng to user needs. Prvate cloud, publc cloud, hybrd cloud are the three man deployment model of cloud computng. Prvate cloud that can be buldng wthn the organzaton and the data securty s more when compared to the other model clouds. In whch storage nodes that can be ncreased when there wll be an ncrease n the storage demand. Durng such ncrease, storage nodes n the prvate cloud have to be balanced wth load n order to avod the traffc n prvate cloud. Cloudsm s a smulaton toolkt whch ensures smulatng, modelng and expermentng on cloud computng Desgn. It supports and to model the behavor component of cloud system such as data centers, vrtual machnes, hosts, servce brokers, Schedulng and allocaton polces. The dea behnd ths paper s to use max-mn algorthm n cloudsm to show how to balance the load across the dfferent storage nodes n the prvate cloud, whch reduce the make span and data traffc. Max-Mn algorthm s also used to reduce dle tme and so effcent n mappng the load across the nodes. Keywords: Storage nodes, Vrtual machne, Cloudsm, Max-Mn algorthm. 1. Introducton Rentng the computng resources lke hardware and software as servce through nternet s called cloud computng. Vrtualzaton s the Key enablng technque and backbone of Cloud Computng. It helps to abstract the software from underlyng hardware. Vrtualzaton that can be appled to all computng resources named such as storage vrtualzaton, memory vrtualzaton, network vrtualzaton and computng vrtualzaton. Cloud computng provdes the shared pool of resources to the end-user. All the resources that can be effcently utlzed by the cloud accordng to the customer requrements by the concept of elastcty and scalablty. The servce provded by the cloud computng are Infrastructure as a servce(iaas,platform as a servce(paas and Software as a servce(saas.prvate cloud that can provsoned for sngle organzaton and t can be accessed anywhere wthn that organzaton Publc cloud s provsoned for open use by all customer and t s mantaned by thrd party. Hybrd cloud s a combnaton of prvate cloud and publc cloud. Even though the publc cloud can be accessed from anywhere at any tme, but t has certan sgnfcant securty related rsks due to data remnants, unencrypted data and shared multtenant envronments. Prvate cloud storage s controlled by an organzaton. It s bult by commodty machnes wthn the organzaton where varous users store and access ther data. There may be ncreasng storage due to the ncludng of new customers or abundant storage of data by exctng customers when they cross beyond ther lmts. Due to ths storage nodes automatcally get ncreased accordng to scalablty property of cloud. Durng such expanson of the storage nodes load should be mantan across the nodes to avod the traffc and load mbalance Load balancng algorthm that can be mplemented n ths paper whch attempts to balance the load when load s storng on nodes. Volume 4 Issue 4, Aprl The rest of ths paper s organzed as follows. Secton II dscusses the related works. Secton III descrbes the Problem Descrpton. Secton IV dscusses the proposed system archtecture. Secton V descrbes the proposed algorthm. Secton VI descrbes the Analyss and Report. Secton VII Concludes and provdes Future work. 2. Related Works Martn Randles [1] proposed the optmzaton of network topology n whch he used clusterng and the honeybee foragng algorthm at the top of applcaton layer. For makng optmum resources allocaton he used smulaton of a self organzng and beehve-based load-balancng algorthm at the top of applcaton layer. The work examned the allocaton of servers n a large-scale SOA, he also proposed load balancng Based Random Samplng approach. Connectvty n a vrtual graph s used to represent the load. Klathem Al Nuam [2] proposed the most known contrbutons n the lterature for load balancng n cloud computng. He classfed the load balancng algorthms nto two types: statc algorthms and dynamc algorthms. Statc load-balancng algorthms that have been developed for cloud computng. Based on the ablty of the node to process new requests and tasks are assgned. The process s based on pror knowledge of the nodes propertes and capabltes. These would nclude the node s processng power, memory and storage capacty, and most recent known communcaton performance. Haozheng Ren [3] proposed a dynamc mgraton algorthm n cloud computng envronment under consderng the heterogenety of envronmental resources on cloud computng applcatons. He proposed fractal-based load balancng trgger strategy. Tradtonal trgger strateges are based on specfc threshold value. Vrtual machne mgraton strategy s called as Trgger strategy. It determnes the Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

2 Internatonal Journal of Scence and Research (IJSR ISSN (Onlne: Index Coperncus Value (2013: 6.14 Impact Factor (2013: mgraton tmng udged by the overloaded node n the system. Yupeng Zhang and Wng Shng Wong [4] proposed a model whch s to presented to study the dstrbuted load balancng problem. There have three basc assumpton: frst one s users ndependently dstrbute ther tasks to servers accordng to pre-assgned bnary sequences.second s no real-tme synchronzaton s requred, ths s no centralzed controller after the ntal sequences assgnment. Chun-We Tsa, We-Cheng Huang [5] proposed a hghperformance hyper-heurstc algorthm to fnd better schedulng solutons for cloud computng systems, ther algorthm has two detecton operators that automatcally determne when to change the low-level heurstc algorthm and a perturbaton operator to fne-tune the solutons obtaned by each low-level algorthm to further mprove the schedulng results n terms of makespan. Z.Zhang and Xu. Zhang [6], Proposed a load balancng algorthm n whch ant colony concepts and complex network theory has descrbed n cloud computng and mproved many related ant colony algorthm that balance the load n cloud computng system. H. Mehta, P. Kanungo [7], proposed workload and clent aware polcy.t s a method n whch hybrd approach of clent that can be descrbed wth the workload that can be used as a request dstrbuton polcy. Y. Lua, Q. Xea, G. Klotb, A. Gellerb, J. R. Larusb, and A. Greenber [8], proposed algorthm named as Jon- Idle -queue for balancng load n large system. No communcatons that can be occur between dspatchers and processors when the ob arrved at node. X. Lu, Pan, C. Wang, and J. Xe,[9], proposed a lock-free multprocessng load balancng soluton whch elmnated the use of shared memory wth other multprocessng load balancers n whch lock concepts that can be used to mantan the user secton. H. Lu, S. Lu, X. Meng, C. Yang, and Y. Zhang [10], proposed load balancng vrtual storage strategy whch offers storage as servce on cloud storage and also large data storage model whch proposed a two-level task schedulng to meet load balancng crtera. S. Wang, K.Yan, W. Lao and S. Wang [11], proposed algorthm whch combnes Opportunstc Load Balancng and Load Balance Mn-Mn schedulng algorthms whch can offers a good effcency and mantan the load balancng n the system. A. Bhadan, and S. Chaudhary [12], proposed Central Load Balancng Polcy for Vrtual Machnes for load balancng n the vrtualzaton and cloud computng models. Z. Zhang, H. Wang, L. Xao and L. Ruan [13], proposed an algorthm named as Statstc based Load Balance whch has used for onlne resource allocaton decson wth the help of statcally predcton mechansm. Volume 4 Issue 4, Aprl Bhathya [14], proposed two vrtual machne load balancng algorthms n whch frst algorthm s Actve Montorng Load Balancng algorthm, whch dstrbutes the load equally to all vrtual machnes and second algorthm s throttled load balancng algorthm whch allows the allocaton of task for each VM, wth help of ths better load balancng algorthm can be acheved to all VM. M. Randles, D. Lamb and A. Bendab [15], proposed three load balancng algorthm whch are large-scale complex system, based random samplng on walk procedure and Actve Clusterng. Dynamc network system s created whch provded the load dstrbuton status n based random samplng. Actve Clusterng s used for rewre the network. In these applcatons all the nodes are aware of other nodes whch can be easly help for load balancng. 3. Problem Descrpton In a Load balancng problem, where obs are to be assgned mmedately to the resources n heterogeneous envronment. The allocaton should be as fast as possble, whle at the same tme optmzng several crtera such as utlzaton and response tme can also be consder. There are no dependences between the dfferent tasks. The arrval rate of obs determnes the system load, and there may be a dfferent runtmes for a partcular task on dfferent machnes. Max- Mn s a smplest schedulng algorthm whch s proposed to balance the load n the prvate cloud storage. In whch mean completon tme of all vrtual machnes storage nodes n partcular prvate cloud can be calculated. Then the machne whose completon tme s greater than the mean value s selected and those obs wll be rescheduled to the node or machne whch has least completon tme. 4. Proposed System Archtecture A prvate cloud s bult wth numerous vrtual machnes several storage nodes are resde nsde the VM. Prvate cloud storage whch contans Walrus controller, Storage controller, Load balancer, and Vrtual Machnes wth Storage node. Walrus controller offers persstent storage to all of the vrtual machnes n the prvate cloud and can be used as a smple HTTP put/get storage as a servce soluton. There are no data type restrctons for Walrus, and t can contan mages.e., the buldng blocks used to launch vrtual machnes, volume snapshots and applcaton data. Only one Walrus can exst per cloud storage. The storage controller communcates wth the vrtual machne and manages block volumes and snapshots to the nstances wthn ts specfc cluster. If an nstance requres wrtng persstent data to memory outsde of the cluster, t would need to wrte to Walrus, whch s avalable to any nstance n any cluster. Load balancer whch s used to balance the load wth the help of Max-Mn algorthm. Durng the start-up of the Load balancer, storage nodes regster ther detals such as free space, network utlzaton, utlzed space and IP address.storage controller whch forwards the load to the load balancer for dstrbutng them across the varous storage nodes n the cluster to avod load mbalance Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

3 Internatonal Journal of Scence and Research (IJSR ISSN (Onlne: Index Coperncus Value (2013: 6.14 Impact Factor (2013: Et (t, m = Mch (m + MT(t, m Where Mch (m s the machne avalablty tme.e. the tme at whch machne m completes any prevously assgned tasks. MT (, represents the estmated executon tme for task t on machne m Proposed related Defnton of Max-Mn Algorthm MT the amount of tme taken by machne M to execute 5. Proposed Algorthm A. Storage Node Sub Module Fgure 1 task ET - the expected completon tme of M Mch (m - the machnes avalablty tme.e. the tme at whch Machne completes any prevously assgned tasks. Cmb(ET,Machnes The functon f1 s used to combne all the tasks and machnes that has mnmum. The best mnmum task/machne par (m, n s selected from the Group The storage node sub module gets the status of the storage nodes. It contans RAM, bandwdth, capacty, utlzed space, resdual space, network utlzaton, current load of each storage node. Then t receves the free space, network utlzaton, utlzed space and IP address from the load balancer. B. Path Transmsson Sub Module Path transmsson sub module path gets the status of path and path d s smulated, by whch load can transfer from load balance to VM C. Vrtual Machne Sub Module Vrtual machne sub module whch gets the status of Vrtual machne and t contans vrtual machne dentfcaton, RAM, bandwdth, and MIPS(Mllon Instructon Per Second.It also receves storage nodes and allocate those storage nodes nto vrtual machne n random process. D. Job Provsonng Sub Module Job provsonng sub module whch gets status of obs that are enterng nto a prvate cloud and ob Id,fle Sze and partcular path Id s smulated, from whch s entered nto Storage node. E. Load Balancng Module In ths paper load balancng can be done wth Max-Mn Algorthm. It s one the schedulng algorthm. Allocaton s done based on the maxmum and mnmum value. The mean completon tmes of all the machnes are calculated and the machne whose completon tme s larger than the mean value s selected. Then the tasks assgned to the selected machnes are reassgned to the machnes whose completon tme s less than the mean value. The expected completon tme of the task t on machne m s calculated by Max-Mn Algorthm Volume 4 Issue 4, Aprl Whle there are tasks to schedule 2 Every Task to schedule 3 For all Machne 4 Compute ET, ; ET, = Mch (m + T 5 End for 6 Cmb (ET, Machnes =f1 (CT, 1, T, End for 8 Select the best maxmum par (task, machne from the combned Group 9 Compute level maxmum ET m,n 10 Reserve task m on n 11 End whle 12 Calculate MeanET= (ΣET /No of machnes 13 For all Machne 14 f (ET <=MeanET 15 Select partcular tasks reserved on the host 16 End for 17 Every Task reselected 18 For all Machne wth (ET >=MeanET 19 Compute NewET, = ET(task, host 20 f (NewET, >= MeanET 21 Combne (ET, Machnes =f1 (ET, 1, ET, End for 23 Choose the best maxmum par from the Combned Group 24 Reschedule (task on machne 25 Compute NewET 26 End for 6. Analyss and Report Proposed algorthm s mplemented wth the help of smulaton package lke cloudsm and cloudsm based tool. Java language s used to mplementng VM storage node load balancng algorthm. We assume that the cloudsm toolkt has Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

4 Internatonal Journal of Scence and Research (IJSR ISSN (Onlne: Index Coperncus Value (2013: 6.14 Impact Factor (2013: been deployed n 6 storage nodes wth 4 vrtual machnes, where the parameter values are as under. Parameter Values VM archtecture X86 VM bandwdth Change accordng to each machne VM memory Change accordng to each machne No. of processor each machne 6 Process element 5 MIPS Randomly Vares In smulaton process ob path s automatcally smulated and assgned to VM by cloudsm accordng to the VM status, whenever the VM status changes ob d and ts path s also change accordng to t. A storage node gets automatcally assgned nto VM by smulaton process The proposed Max- Mn Algorthm s mplemented for smulaton. Table 2 shows the result based on fnal Max-Mn VM Load Balancng algorthm for overall balancng obs on nodes n VM. In whch Job length and current load of storage node for four VM that can analyze usng cloudsm for our proposed algorthm. Table 2 Vrtual Machne ID Job Length Current Load Analyss shows that Max-Mn balancng algorthm consumes less tme for storng obs n node. When number of vrtual machne or storage node ncreases t creates the respectve obid for reachng each and every node. It decreases the problem of deadlock by cloud envronment ob path provsonng polcy n vrtual macne. Fgure 2 and Fgure.3 descrbes the Max-Mn produce the hgh suffcency and schedulng effcency to all obs n the prvate cloud. Fgure 2 Fgure 3 7. Concluson and Future Work The expermental results show that the Max-mn algorthm whch helps to store the obs n quck and effcent manner to all the storage nodes n vrtual machne and avod traffc and load mbalance on storage nodes. The future research can also be extended to mprovng the complexty more and also fault tolerance can be consdered, because after a ob s submtted to the node, f the node may get affected whch may lead to affect all obs whch submtted to that partcular node. Reference [1] Martn Randles, Davd Lamb, A. Taleb-Bendab, A Comparatve Study nto Dstrbuted Load Balancng Algorthms for Cloud Computng n 2010 IEEE 24th Internatonal Conference on Advanced Informaton Networkng and Applcatons Workshops [2] Klathem Al Nuam, Nader Mohamed, Maram Al Nuam and Jameela Al-Jarood, A Survey of Load Balancng n Cloud Computng: Challenges and Algorthms n 2012 IEEE Second Symposum on Network Cloud Computng and Applcatons [3] Haozheng Ren, Yhua Lan,Chao Yn The Load Balancng Algorthm n Cloud Computng Envronment n nd Internatonal Conference on Computer Scence and Network Technology [4] Yupeng Zhang and Wng Shng Wong Dstrbuted Load Balancng n a Multple Server System by Shft- Invarant Protocol Sequences n IEEE Wreless communcaton and networkng conference [5] Chun-We Tsa, We-Cheng Huang, Meng-Hsu Chang, Mng-Chao Chang, and Chu-Sng Yang, A Hyper- Heurstc Schedulng Algorthm for Cloud n IEEE Transacton on Cloud Computng, Vol. 2, NO. 2, Aprl- June 2014 [6] Z. Zhang and Xu. Zhang A Load balancng mechansm based on ant colony and complex network theory n open cloud computng federaton, 2nd Internatonal Conference on Industral Mechatroncs and Automaton (ICIMA, Wuhan, Chna, vol. 2, pp , May [7] H. Mehta, P. Kanungo, and M. Chandwan, Decentralzed content aware load balancng algorthm for dstrbuted computng envronments, Proceedngs of the Internatonal Conference Workshop on Emergng Trends n Technology (ICWET, pp , February Volume 4 Issue 4, Aprl Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY

5 Internatonal Journal of Scence and Research (IJSR ISSN (Onlne: Index Coperncus Value (2013: 6.14 Impact Factor (2013: [8] Y. Lua, Q. Xea, G. Klotb, A. Gellerb, J. R. Larusb, and A. Greenber, Jon-Idle-Queue: A novel load balancng algorthm for dynamcally scalable web servces, An nternatonal Journal on Performance Evaluaton, vol. 68, pp , November [9] X. Lu, Pan, C. Wang, and J. Xe, A lock-free soluton for load balancng n mult-core envronment, 3rd IEEE Internatonal Workshop on Intellgent Systems and Applcatons (ISA, pp. 1-4, May [10] H. Lu, S. Lu, X. Meng, C. Yang, and Y. Zhang, LBVS: A load balancng strategy for vrtual storage, Internatonal Conference on Servce Scences (ICSS, pp , May [11] S. Wang, K.Yan, W. Lao and S. Wang, Towards a load balancng n a three-level cloud computng network, 3rd IEEE Internatonal Conference on Computer Scence and Informaton Technology (ICCSIT, vol. 1, pp , July [12] A. Bhadan, and S. Chaudhary, Performance evaluaton of web servers usng central load balancng polcy over vrtual machnes on cloud, Proceedngs of the Thrd Annual ACM Bangalore Conference (COMPUTE 10, Artcle No. 16, January [13] Z. Zhang, H. Wang, L. Xao and L. Ruan, A statstcal based resource allocaton scheme n cloud, IEEE Internatonal Conference on Cloud and Servce Computng (CSC, pp , December [14] W. Bhathya, CloudAnalyst a CloudSm-based tool for modellng and analyss of large scale cloud computng envronments, MEDC Proect, Cloud Computng and Dstrbuted Systems Laboratory, Unversty of Melbourne, Australa, pp. 1-44, June [15] M. Randles, D. Lamb, and A. Bendab, A comparatve Study nto dstrbuted load balancng algorthms for cloud computng, IEEE 24 th Internatonal Conference on Advanced Informaton Networkng and Applcatons Workshops (WAINA, pp , Aprl 2010 Volume 4 Issue 4, Aprl Paper ID: SUB Lcensed Under Creatve Commons Attrbuton CC BY