LOAD BALANCING IN PUBLIC CLOUD COMBINING THE CONCEPTS OF DATA MINING AND NETWORKING

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1 LOAD BALACIG I PUBLIC CLOUD COMBIIG THE COCEPTS OF DATA MIIG AD ETWORKIG Priyaka R M. Tech Studet, Dept. of Computer Sciece ad Egieerig, AIET, Karataka, Idia Abstract Load balacig i the cloud computig eviromet has a importat impact o the performace of whole system. A Good load balacig method makes cloud computig more efficiet with icreased user satisfactio. The combied cocepts of etworkig, data miig ad cloud computig techology are used to achieve a good load balacig strategy. Cloud partitioig helps to simplify the load balacig problem. The algorithms from etworkig ad data miig are used to achieve cloud partitios. A cloud partitioig cocepts itroduced here is used i load balacig model for the public cloud with a switch mechaism to choose differet strategies for differet situatios. The algorithm applies the roud robi ad game theory after cloud partitioig as the load balacig strategy to improve the efficiecy i the public cloud eviromet. Keywords: Load balacig, public cloud, Cloud partitio *** ITRODUCTIO Cloud Computig, the log-held dream of computig is similar to utility computig. It is expected to accelerate iovatio ad busiess agility of the IT idustry, eablig software to become more attractive as a service ad shapig the way IT hardware is desiged ad purchased [1]. Cloud computig provides a distributed computig eviromet that focuses o providig a wide rage of users with distributed access to virtualized, scalable hardware ad/or software ifrastructure over the iteret. Cloud computig provides hardware ad software packages which are delivered as a service to cliets over a outsized scale etwork [2]. 1.1 Load balacig cocepts Load balacig is a process of distributig the total load o to the idividual odes of the collective system to maximize throughput, resource utilizatio ad to miimize the respose time, alog with removig a coditio i which some of the odes are heavily loaded while some others are light. [3] Load balacer is a software program which receives coectio request from cliets ad forwards it to oe of the backed server which replies accordigly. Because of this the cliet will be uaware of where the data is stored. This separatio also has security beefit which hides the structure of iteral etwork ad helps to prevet attacks. I cloud computig eviromet, there is radom arrival of jobs with radom CPU utilizatio. Such requiremets ca load a specific resources heavily, while the other resources are less loaded. 2. RELATED WORK There are may studies beig coducted i this stream. However, load balacig i the cloud is still a ew problem that eeds ew architectures which ca adapt to chagig eeds. Soumya Ray ad Ajata De Sarkar [15] proposed a brief review of existig load balacig algorithms. K. Ramaa, A. Subramayam ad A. Aada Raohave [16] have put forth that Load balacig algorithm tries to balace the total system load by trasferrig the workload from heavily loaded odes to lightly loaded odes ad also preseted the performace aalysis of various load balacig algorithms based o differet qualitative parameters, cosiderig static ad dyamic load balacig approaches. I the existig system the partitioig of cloud is based o area which might ot always be well suited i all situatios. Hece, there is a eed of ew partitioig method usig which we ca achieve a better performace. 3. SYSTEM MODEL The load balacig strategy ivolves creatig cloud partitios. A cloud partitio is a sub area of public cloud. Here the divisios are based o the VDBSCA algorithm. Oce the public cloud is partitioed, the the load balacig starts whe a job arrives at the system. The mai cotroller decides which cloud partitio should receive the job. The load balacer assiged for each partitio the decides how to assig the jobs to the odes. Whe the load status of a cloud partitio is idle or ormal, this task ca is accomplished locally. If the cloud partitio is overloaded, this job should be trasferred to aother partitio. The whole process is show i Fig.1. Volume: 03 Special Issue: 03 May-2014 CRIET-2014, 167

2 We are usig followig techiques. 1. Dijkstra s algorithm to fid the shortest distace betwee two odes 2. VDBSCA Cluster based Cloud Partitio 3. Roud-Robi Techique 4. Game Theory Techique. Followig algorithm shows the procedure Algorithm 1 Begi Apply Dijkstra s algorithm to fid the shortest path Apply VDBSCA algorithm to fid clusters ad make each cluster as a partitio while job do searchbestpartitio (job); if (partitiostate == idle partitiostate == ormal) the Sed Job to Partitio; else search for aother Partitio; ed if ed while ed 3.1 Cloud Partitio A cloud partitio is a sub area of public cloud. Partitioig will simplify the process of load balacig Dijkstra s Algorithm The use of etworkig cocept will determie the distace at which each server is placed. For this, here, we use distace formula to fid weights ad the use the dijkstra s algorithm [4]. Dijkstra s algorithm solves the sigle-source shortestpaths problem o a directed graph G= (V,E) ad all edges have o-egative weights. I this sectio, therefore, we assume that w(u,v)>=0 for each edge for each edge E(u,v) [5]. Dijkstra s algorithm maitais a set S of vertices whose fial shortest-path weights from the source S have already bee determied. The algorithm iteratively selects the vertex u V- S with the miimum shortest-path estimate, adds u to set S, ad relaxes all edges leavig u. Here, we use a mi-priority queue Q of vertices, keyed by their values of d. The followig algorithm is referred from [5]. DIJKSTRA(G,w,s) IITIALIZE -SIGLE-SOURCE(.G, s) S=φ; Q=G.V while Q!=φ u=extract-mi(q) S=SU{u} for each vertex v G.Adj[u] RELAX (u,v,w) Thus, usig the Dijkstra s algorithm we first fid the shortest path ad the we use the shortest path cocept i VDBSCA method to develop clusters DBSCA I the pervious step, the shortest distace betwee ay two poits is calculated. This value is used i ext step to form clusters. Here we use VDBSCA( Varied Desity Based SCAig) method to form clusters. DBSCA ca fid clusters of arbitrary shape. However, clusters that lie close to each other ted to belog to the same class. Its computig process is based o six rules or defiitios, creatig two lemmas [6][13]. Defiitio 1: (The Eps-eighbourhood of a poit) The Eps (p) represets the Eps-eighbourhood of a poit p ad is defied by Eps (p) = {q D dist(p,q)<eps} (1) Fig -1: Job assigmet strategy Volume: 03 Special Issue: 03 May-2014 CRIET-2014, 168

3 Where D is set of give poits. For a poit to belog to a cluster it eeds to have at least oe other poit that lies closer to it tha the distace Eps. Defiitio 2: (Directly desity-reachable) There are two kids of poits belogig to a cluster; there are border poits ad core poits [6]. The Eps-eighborhood of a border poit teds to have sigificatly less poits tha the Eps-eighborhood of a core poit. The border poits will still be a part of the cluster ad i order to iclude these poits, they must belog to the Epseighborhood of a core poit q. p Eps (q) (2) I order for poit q to be a core poit it eeds to have a miimum umber of poits withi its Eps-eighborhood Eps (q) MiPts (core poit coditio) (3) Defiitio 3: ( Desity-reachable) A poit p is desityreachable from a poit q with respect to Eps ad MiPts if there is a chai of poits p 1...,p, p 1 =q, p =p such that p i+1 is directly desity-reachable from p i. Defiitio 4: (Desity-coected) There are cases whe two border poits will belog to the same cluster but where the two border poits do t share a specific core poit. I these situatios the poits will ot be desity-reachable from each other. There must however be a core poit q from which they are both desity-reachable. A poit p is desity-coected to a poit q with respect to Eps ad MiPts if there is a poit o such that both, p ad q are desity-reachable from o with respect to Eps ad MiPts. Defiitio 5: (cluster) If poit p is a part of a cluster C ad poit q is desityreachable from poit p with respect to a give distace ad a miimum umber of poits withi that distace, the q is also a part of cluster C. 1) " p, q: if p C ad q is desity-reachable from p with respect to Eps ad MiPts, the q C. Two poits belogs to the same cluster C, is the same as sayig that p is desity-coected to q with respect to the give distace ad the umber of poits withi that give distace. 2) p, q C: p is desity-coected to q with respect to Eps ad MiPts. Defiitio 6: (oise) oise is the set of poits, that do t belog to ay of the clusters. Lemma 1: A cluster ca be formed from ay of its core poits ad will always have the same shape. Lemma 2: Let p be a core poit i cluster C with a give miimum distace (Eps) ad a miimum umber of poits withi that distace (MiPts). If the set O is desity-reachable from p with respect to the same Eps ad MiPts, the C is equal to the set O. VDBSCA VDBSCA[13] algorithm detects cluster with varied desity as well as automatically selects several values of iput parameter Eps for differet desities.peg Liu, Dog Zhou ad aiju Wu have proposed a detailed descriptio i [14]. Descriptio of the Algorithm I geeral the algorithm has two steps, choosig parameters Eps i ad cluster with varied desities. The algorithm [13] is as follows, (i) It calculates ad stores k-dist for each project ad partitio the k-dist plots. (ii) The umber of desities is give ituitively by k- dist plot. (iii) The parameter Epsi is selected automatically for each desity. (iv) Sca the dataset ad cluster differet desities usig correspodig Eps i (v) Display the valid cluster with respect to varied desity. Algorithm: Partitio k-dist plot. Give thresholds of parameters Eps i (i=1,2,..) For each Eps i (i=1,2,..) a) Eps = Eps i b) Adopt DBSCA algorithm for poits that are ot marked. c) Mark poits as c i Display all the marked poits as correspodig clusters. By usig this VDBSCA algorithm we ca form clusters of odes ad make each cluster as a partitio Assigig Jobs to the Cloud Partitio Whe a job arrives at the public cloud, the first step is to choose the right partitio. Job assigmet strategy is preseted from [7]. The cloud partitio status ca be divided ito three types: (1) Idle: Whe the percetage of idle odes exceeds α, chage to idle status. (2) ormal: Whe the percetage of the ormal odes exceeds β, chage to ormal load status. Volume: 03 Special Issue: 03 May-2014 CRIET-2014, 169

4 (3) Overload: Whe the percetage of the overloaded odes exceeds γ, chage to overloaded status. The parameters α, β ad γ are to be set by the cloud partitio balacers [7]. Whe the load status of a cloud partitio is idle or ormal, this partitioig ca be accomplished locally [7]. If the cloud partitio load status is ot ormal or idle, this job should be trasferred to aother partitio. The partitio load balacer the decides how to assig the jobs to the odes. Server load status is divided ito three types. If oe cloud server is overloaded ad it agai gettig ew cliet request while other servers are i Idle or ormal state the followig algorithms are used. Idle: If it is i idle status, this job should be trasferred to aother partitio by usig Roud Robi algorithm. ormal: If it is ormal, this job should be trasferred to aother partitio by usig Game theory algorithm. Overload: If it is overload, this job should be trasferred to aother partitio. That partitio selected usig above two algorithms. Step 1 Defie a load parameter set: F = {F 1, F 2,.. F m }with each F i (1 <= i<= m, F i [0,1]) parameter beig either static or dyamic. m represets the total umber of the parameters.[7] Step 2 Compute the load degree as: Load degree( ) = m i=0 α i F i (4) α i ( i=0 α i = 1) are weights that may differ for differet kids of jobs. represets the curret ode.[7] Step 3 Defie evaluatio bechmarks. Calculate the average cloud partitio degree from the ode load degree statistics as: Load degree avg = i=0 Load _degree ( i ) The bech mark Load_degree high is the set for differet situatios based o the Load_degree avg.[7] Step 4 Three odes load status levels are the defied as[7]: Idle Whe Load_degree() = 0 (6) There is o job beig processed by this ode so the status is chaged to Idle. ormal For 0 < Load degree( ) <= Load_degree high (7) The ode is ormal ad it ca process other jobs. Overloaded Whe Load degree high <= Load_degree() (8) The ode is ot available ad ca ot receive jobs util it returs to the ormal.[7] (5) The load degree results are iput ito the Load Status Tables created by the cloud partitio balacers. Each balacer has a Load Status Table ad refreshes it each fixed period T. The table is the used by the balacers to calculate the partitio status. Each partitio status has a differet load balacig solutio. Whe a job arrives at a cloud partitio, the balacer assigs the job to the odes based o its curret load strategy. This strategy is chaged by the balacers as the cloud partitio status chages Roud Robi Algorithm I the regular Roud Robi algorithm, every ode has a equal opportuity to be chose. However, i a public cloud, the cofiguratio ad the performace of each ode will be ot the same; thus, this method may overload some odes. Thus, a improved Roud Robi algorithm is used, which called Roud Robi based o the load status. Step 1:Job arrives at the mai cotroller Step 2: Job is assiged to balacer based o request locatio, balacer status Step 3:I particular partitio P Set i=0 Set s[] as o. of servers arraged i icreasig order of jobs i P for all =1,.., Set s[c] as o. of idle servers i P from s[] for all c=1,.., Step 4:Whe job arrives If s[c]!= ULL The, sed the coectio to s[i] i= i+1; If i == c The i=1 Else Go to game theory Ed if Step 5:go to step Game Theory The players i the game are the odes ad they coted for jobs. Suppose there are odes i the curret cloud partitio with jobs arrivig, the defie the followig parameters: µ i : Processig ability of each ode, i = 1,,. Ф j : Time spet for each job. Ф = j =1 фj Ф j :Time spet by the etire cloud partitio, Ф < μ S ij : Fractio of job j that assiged to ode i ( 0 <= S ij <= 1). Volume: 03 Special Issue: 03 May-2014 CRIET-2014, j =1 j =1 i Sij = 1 ad I this model, the most importat step is fidig appropriate value of S ij. Here the best reply method proposed by Grosuet al.[12]ca be used to calculate S ij of each ode. The ash Equilibrium here is to miimize the respose time of each job.

5 4. COCLUSIOS Load balacig i public clouds is a difficult task with cloud divisio problem beig the major oe. Usig clusterig algorithms is oe way of doig it, ad there may be several other ways that get the job doe. This problem ca be easily solved by combiig the cocepts of etworkig ad datamiig ito the load balacig problem of public cloud FUTURE WORK Sice this work is coceptual framework, more work is eeded to implemet the framework ad resolve ew problems. (1) Cloud divisio rules: It ca be further ehaced by devisig other methods ad comparig performace. The divisio rule should ot simply be based o the geographic locatio. (2) Set the refresh period: Refresh period to commuicate the load status should be properly set. It should either be too short or too log. Tests ad statistical tools ca be used to set a reasoable refresh periods. (3) Other load balace strategy: Other load balacig strategies may also be used, based o tests comparig differet strategies. May tests are eeded to guaratee system availability ad efficiecy. REFERECES [1].Michael Armbrust, Armado Fox, Guho Lee ad Io Stoica (2009) Above the clouds: a berkeley view of cloud computig, Uiversity of Califoria at Berkeley Techical Report o. UCB/EECS [2]. Soumya Ray ad Ajata De Sarkar Executio aalysis of load balacig algorithms i cloud computig eviromet, Iteratioal Joural o Cloud Computig: Services ad Architecture (IJCCSA), Vol.2, o.5, October 2012 [3]. K. Ramaa, A. Subramayam ad A. Aada Rao, Comparative aalysis of distributed web server system load balacig algorithms usig qualitative parameters, VSRD- IJCSIT, Vol. 1 (8), 2011, [4]. etwork Workig Group J. Moy, Editor Request for Commets: 1245, OSPF protocol aalysis, Editor Request for Commets: 1245 Proteo, Ic. July 1991 [5]. T. H. Corme et al, Itroductio to algorithms, 3 rd editio, Pretice-Hall of Idia,2010 [6]. Herik Bäcklud, Aders Hedblom adiklaseijma, A desity-based spatial clusterig of applicatio with oise, Data Miig TM LiköpigsUiversitet - IT [7].Gaochao Xu, Jujie Pag, ad Xiaodog Fu, A Load Balacig Model Based o Cloud Partitioig for the Public Cloud, IEEE trasactios o cloud computig year 2013 [8].Suriya Begum, Dr. Prashath C.S.R, Review of load balacig i cloud computig, IJCSI Iteratioal Joural of Computer Sciece Issues, Vol. 10, Issue 1, o 2, Jauary 2013 [9]. P. Mell ad T. Grace, The IST defiitio of cloud computig, IST Special Publicatio September 2011 [10]. Marti J. Osbore, A itroductio to game theory : Text book, Uiversity of Toroto, Caada, Oxford uiversity press 2000, pg 1-8 [11]. Jeffrey M. Galloway, Karl L. Smith ad Susa S. Vrbsky, Power aware load balacig for cloud computig, World Cogress o Egieerig ad Computer Sciece 2011Vol I WCECS 2011, October 19-21, 2011, Sa Fracisco, USA [12].Daiel Grosu ad Athoy T. Chroopoulos, ocooperative load balacig i distributed systems, Jourel of parallel ad distributed computig ELSEVIER Comput. 65 (2005) [13].M.Parimala, Daphe Lopez,.C. Sethilkumar, A survey o desity based clusterig algorithms for miig large spatial databases, Iteratioal Joural of Advaced Sciece ad Techology Vol. 31, Jue, 2011 [14]. Peg Liu, Dog Zhou adaiju Wu, VDBSCA: Varied desity based spatial clusterig of applicatios with oise, /07/$ IEEE [15]. Soumya Ray ad Ajata De Sarkar Executio Aalysis of Load Balacig Algorithms i Cloud Computig Eviromet, Iteratioal Joural o Cloud Computig: Services ad Architecture (IJCCSA), Vol.2, o.5, October 2012 [16]. K. Ramaa, A. Subramayam ad A. AadaRao, Comparative Aalysis of Distributed Web Server System Load Balacig Algorithms Usig Qualitative Parameters, VSRD-IJCSIT, Vol. 1 (8), 2011, Volume: 03 Special Issue: 03 May-2014 CRIET-2014, 171