Pre-allocation Strategies of Computational Resources in Cloud Computing using Adaptive Resonance Theory-2

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1 Pre-allocaton Strateges of Computatonal Resources n Cloud Computng usng Adaptve Resonance Theory-2 Dr.T. R. Gopalakrshnan Nar 1, P Jayarekha 2 1 Drector, Research and Industry Incubaton Centre(RIIC), DSI, Bangalore. trgnar@yahoo.com 2 Research Scholar, Dr. MGR Unversty Dept. of ISE, BMSCE, Bangalore. ABSTRACT Member, Multmeda Research Group, RIIC, DSI, Bangalore. Jayarekha2001@yahoo.co.n One of the major challenges of cloud computng s the management of request-response couplng and optmal allocaton strateges of computatonal resources for the varous types of servce requests. In the normal stuatons the ntellgence requred to classfy the nature and order of the request usng standard methods s nsuffcent because the arrval of request s at a random fashon and t s meant for multple resources wth dfferent prorty order and varety. Hence, t becomes absolutely essental that we dentfy the trends of dfferent request streams n every category by auto classfcatons and organze preallocaton strateges n a predctve way. It calls for desgns of ntellgent modes of nteracton between the clent request and cloud computng resource manager. Ths paper dscusses about the correspondng scheme usng Adaptve Resonance Theory-2. KEYWORDS Adaptve resonance theory-2, cloud computng, pre-allocaton strateges. 1. INTRODUCTION Durng the past few years, cloud computng has emerged as an enablng technology and t has been ncreasngly adopted n many areas ncludng busness, scence and engneerng. A cloud s an aggregaton of resources/servces possbly dstrbuted and heterogeneous and operated by an autonomous admnstratve body whch s run by a company or an organzaton (e.g., Amazon, Google or Mcrosoft). Resources n a cloud are not restrcted to hardware, such as processors and storage devces, but t can also be software servces or Web servce n varous forms of nstances. A prmary drvng force of the recent cloud computng paradgm s ts nherent cost effectveness[12]. The cloud computng envronments are charged on the servce usage, smlar to many basc utltes lke electrcty and water. A request-response model s very appealng for both servce provders and consumers and much concentraton n research s requred to acheve dfferent models. As the cloud computng expands, few of the major challenges wll be n the DOI : /jccsa

2 doman of fluctuatng servce request volume and unpredctable request arrval patterns. Ths unstructured demand supply scenaro exstng between provders and consumers can hnder the effectve utlzaton of cloud computng envronments [4]. The problem of servce request schedulng n cloud computng systems s addressed n ths paper. We consder a three-ter cloud structure, whch conssts of nfrastructure vendors, servce provders and consumers, and the latter two partes are of partcular nterest to us. Clouds are prmarly drven by economcs and hence the servce provder ams to accommodate as many requests as possble wth ts objectve of maxmzng proft. Ths busness nterest may conflct wth many of the performance parameter especally the servce tme. Identfcaton of dfferent request streams for dfferent category and organzng the resource request pattern n a predctve way to reduce the response tme, are the man objectves of ths paper. Among the multple forms of cloud utlzaton, users tend to store more shared data on the cloud; the data of nterest s searched wth the help of servce provders. One major dfference exstng between the cloud and the web s that the cloud data lacks the explct lnk structure present n the web; ths lnk structure plays an mportant role n mprovng the effcency of web search. The objects n a cluster mght suggest that a partcular set of objects are regularly accessed sequentally and hence t mght be benefcal to prefetch them as soon as the frst one s requested [13]. Further, decsons used to perform object prefetchng, can n turn drve object placement decsons. For example, snce objects are prefetched together, t mght make sense to ensure that the objects resde on a dsjont set of hardware (nodes, routers, etc.). Furthermore, snce many clents store data on the cloud, the cloud can buld more robust models for clusterng, prefetchng, and object placement by aggregatng smlar access patterns for data across multple users [13]. Hence t s requred to develop a clusterng and prefetchng technque to effectvely manage the cloud s storage. Resultng n reducton of the number of jobs beng rejected and ncrease the proft of the servce provder globally. 2. RELATED WORKS 2.1 Related work n clusterng The clusterng of users based on ther web access pattern s an actve area of research n Web usage mnng. R. Cooley et al. [2] have proposed a taxonomy of Web Mnng and they present varous research ssues. In addton, the research n web mnng s centred on the extracton and applcatons of clusterng and prefetchng. Both these ssues are clearly dscussed n [5]. It has been proven n ths scheme that a 97.78% of predcton herarchy s acheved. 2.2 Related work n prefetchng Prefetchng means fetchng the objects much pror to the user request arrval. There are some exstng prefetchng technques, but they possess some defcency. In ths the clent suffers from start-up delay for the frst-tme access snce, prefetchng acton s only trggered when a clent starts to access that object. However, an neffcent prefetchng technque causes wastage of network resources by ncreasng the web traffc over the network.[7] J Yuan et al., has proposed a scheme, n whch proxy servers aggressvely prefetch meda objects wthout a pattern before they are requested. They make use of servers knowledge about access patterns to ensure the accuracy of prefetchng, and have tred to mnmze the prefetched data sze by prefetchng only the ntal segments of meda objects. [10] KJ Nesbt et al., has proposed a 32

3 prefetchng algorthm whch s based on a global hstory buffer that holds the most recent mssng addresses n FIFO order. S K Rangarajan, et al., [14] have proposed ART1 algorthm n whch clusterng and predcton has resulted n an accuracy of 97%. In ths work we have proposed ART2 NN clusterng algorthm for clusterng user request arrval pattern. Ths cluster helps the server s agent n prefetchng and makng the access ready for memory, processors and most mportantly storage, pror to the user request[3][7][11]. 2.3 Related work n cloud computng A servce provder rents resources from cloud nfrastructure vendors and prepares a set of servces n the form of vrtual machne (VM) mages; the provder s then able to dynamcally create nstances from these VM mages [4]. The underlyng cloud computng nfrastructure servce s responsble for dspatchng these nstances to run on physcal resources as shown n fgure 1. A runnng nstance s charged by the tme t runs at a flat rate per tme unt. It s n the servce provder's nterests to mnmze the cost of usng the resources offered by the cloud nfrastructure vendor (.e., resource rental costs) and maxmze the revenue (specfcally, net proft) generated through servng consumers applcatons. From the servce consumer s vewpont, a servce request for an applcaton consstng of one or more servces s sent to a provder specfyng two man constrants, tme and cost [12]. Although the processng (response) tme of a servce request can be assumed to be accurately estmated, t s most lkely that ts actual processng tme s longer than ts orgnal estmate due prmarly to due delays (e.g., queung and/or processng) occurrng on the provder s sde. Ths tme dscrepancy ssue s typcally dealt wth usng servce level agreements (SLAs). Schedulng strateges n ths cloud computng scenaro should satsfy the objectves of both partes. The specfc problem addressed n ths paper s the schedulng of consumers servce requests (or applcatons) on servce nstances made avalable by provders takng nto account costs ncurred by both consumers and provders as the most mportant factor. Here we try to present, a management server and method for provdng a cloud computng servce at hgh speed and reasonable cost, are provded. The management server provdes a vrtual machne to a clent as a computng resource. The vrtual machne s multplexed by operatng multple vrtual devces on a sngle vrtual machne. Accordngly, the demand pattern for computng resources may be predcted n advance and may be provded to a user more effcently. 33

4 Infrastructure Vendor Provder Consumer Fgure 1 Three Ter archtecture of Cloud computng 2.4 Related work n ART2. ART2 s an unsupervsed learnng and predctng algorthm derved from the resonance theory[9]. The ART network can be used for dentfyng patterns. Ths desgn allows the user to control the smlarty between the patterns accepted by the same cluster[1]. ART2 can learn about sgnfcant new classes, yet reman stable n response to prevously learned classes. Thus, t s able to meet the challenges n clusterng the request arrval pattern where numerous varatons are common. ART networks are confgured to recognze nvarant propertes of a gven problem doman; when presented wth data pertnent to the doman, the network can categorze t on the bass of these features[6][2] Ths process also categorzes when dstnctly dfferent data are presented and t ncludes the ablty to create new clusters. ART networks accommodate these requrements through nteractons between dfferent subsystems, desgned to process prevously encountered and unfamlar events, respectvely. We choose the ART2 neural network rather than other classfers because t s capable of ncrementally mprove the numbers of clusters f needed. ART2 networks were desgned to process contnuous nput pattern data. A specal characterstc of such networks s the plastcty that allows the system to learn new concepts and at the same tme retan the stablty that prevents destructon of prevously learned nformaton [6]. 4. METHODOLOGY 4.1 Preprocessng the request logs The, the log data of request arrvals are represented as <clent_id,date, requested_objects,readytme,deadlne> format. We have selected a sample format of 50 clents requestng for 200 dfferent vdeos. 34

5 4.2 Gettng the Popularty Value The popularty value s the most mportant parameter to get the effectve prefetch operaton. Ths popularty value may consder the long term measurement of requestfrequency, whch s neglected n the other algorthms. In ths work the reference count value s used to get the popularty value. The reference count value s hghly varable over short tme scales, but ths s much smoother over long tme scales. Ths property makes the popularty value to deal wth the long term measurement of request frequency. Snce the maxmum and mnmum value of request frequency s known durng the submsson of nput to the ART2 system, the normalzed value of the popularty can be obtaned usng the followng transformaton. mn d d δ = (1) max mn d d The transformed nto a range between [0,1]. 4.3 Extracton of feature vectors For clusterng, we need to extract the popularty of each resource that represents the frequency of number of tmes that resource s requested. The pattern vector maps the access frequency of each base vector element to real values. It s of the form P ={P 1,P 2,.P n } where each P vares between 0 to Fgure 2 Sample Pattern Vector Fgure 2 s a sample of pattern vector generated durng a sesson. Each pattern vector has a real value pattern of length 200.For each sesson we nput 50 such pattern to an ART2, snce we have 50 clents. 5 PROPOSED ARCHITECTURE AND ALGORITHM Archtecture of ART2 s shown n Fgure 3. It s desgned for processng analog as well as bnary nput patterns. ART2 network module ncludes two man parts: attentonal subsystem and orentng subsystem. Attentonal subsystem preprocess analog nput pattern, and then choose the best matchng pattern under compettve selecton rule from the nput pattern prototypes[9]. Orentng subsystem carry out smlarty vglancetestng of the selectve pattern prototype and trgger resonance learnng and adjustng weght vectors when vglance-testng passed, otherwse get rd of the current actve node and search the other new ones. If there s no pattern prototype matchng the nput pattern, create a new output node to represent t. Its memory capacty can ncrease wth the ncrease of learnng patterns. The network allows not only off-lne learnng but also 35

6 n an on-lne learnng and applyng way smultaneously, that s, the learnng and applyng states are nseparable. Expermental settngs The performance of the algorthm was thoroughly evaluated usng our dscrete-event cloud smulator developed n C/C++. Smulatons were carred out wth a dverse set of applcatons (.e., varous applcaton characterstcs) and settngs. Owng to the large scale consttuton of cloud computng envronments, the number of servce nstances (that can be created) s assumed to be unbounded. The total number of experments carred out s 100 Specfcally, we have generated 5 dfferent numbers of servces per applcaton randomly selected from a unform dstrbuton,.e.,u(10, 80), and 7 dfferent smulaton duratons (2,000, 4,000, 8,000,12,000, 16,000, 20,000 and 30,000). Fgure 3 The ART2 Neural networks The ART2 neural network algorthm used n ths work s summarzed below[9]. Neural network confguraton: The parameters requred for ART2 formulaton has been ntally chosen : Nose nhbton threshold: 0 θ 1 (2) Survellance Parameter: 0 ρ 1 (3) Error tolerance parameter ETP n the F1layer: 0 ETP 1 (4) Weght ntalzaton of the neural network: Top-down: Z j (0)=0 (5) Bottom-up: Z j (0) (6) Operaton steps: 36

7 1. Intalze the sub-layer and layer outputs wth zero value and set cycle counter to one. 2. Apply an nput vector I to the sub-layer W of the F1 layer. The output of ths layer s: W =I +au (7) 3. Propagate to the X sub-layer: w x e + W = (8) 4. Calculate the V sub-layer output: V = f(x ) + bf(q ) (9) In the frst cycle the second term of (9) s zero once the value of q s zero. The functon f(x) s gven by: 2 2θ 2 (x) = (x + θ ) x f 2 0 x θ x θ (10) 5. Compute the U sub-layer output: (11) u v = e + v 6. Propagate the prevous output to the P sub-layer: p = u + dz J (12) The J node of the F2 layer s the wnner node. If F2 s nactve or f the network s n ts ntal confguraton p = u (13) 7. Calculate the Q sub-layer output: p q = (14) e + p 8. Repeat steps (2) to (8) untl stablzng the values n F1 layer accordng to Error () = U() - U*(). If Error () ETP, the F1 layer s stable. 37

8 9. Calculate the R sub-layer output: r = e u + cp + u + cp (15) 10. Determne f a reset condton s ndcated. If ρ (e ) then, send a reset sgnal to F2, mark any actve F2 node as not enable for competton, reduce to zero the cycle counter and return to the step (2). If there s no reset sgnal and the counter s one, the cycle counter s ncreased and passes to step (11). If there s no reset and the cycle counter s larger than one, then control passes to step (14), once the resonance was establshed. 11. Calculate the F2 layer nput: j M T = P = 1 Z J (16) 12. Only the F2 wnner node has non-zero output. Any node marked as non capable by a prevous reset sgnal doesn't partcpate n the competton. d g(t ) = T = max(t ) j k j 0 otherwse (17) 13. Repeat the steps (6) to (10). 14. Update the bottom-up weghts of the F2 layer wnner node: u ZJ = (18) 1 d 15. Update the top-down weghts of the F2 layer wnner node: u ZJ = (19) 1 d 16. Remove nput vector, restore nactve F2 nodes and return to the step (1) wth a new nput vector. The ntal values chosen for ART2 s as follows a=10, b=10,d=0.9,c=0.1,e=0.0 θ=0.2 M = 5 Threshold value selecton and methodology used The use of several feature vectors requre a specal neural network, a supervsed ART2 NN s used. The performance of a supervsed or unsupervsed ART2 NN depends on the approprate selecton of the vglance threshold [10]. If the value of vglance 38

9 threshold s near to zero, a lot of clusters wll be generated, but f t s greater, then number clusters wll be generated. 5 RESULTS AND DISCUSSION Each request conssts of the type of resources requred and confguraton detals. A request r s a tuple contanng at least (n, rt, d), where n specfes the number of vrtual machnes requred; rt s the ready tme, before whch the request s not ready for executon; and d s the deadlne for request completon. A wde range of requests to the resources can be represented by these parameters. The users of the nfrastructure run dfferent applcatons wth dfferent computng requrements. Some applcatons need resources at partcular tmes to meet applcaton deadlnes are called deadlne-constraned applcatons. Whereas other applcatons are not strct about the tme when they are gven resources to execute as long as they are granted the resources requred called best-effort. Graph 1 Comparson of job rejected The graph 1 s a comparson for the number of jobs beng rejected when no cloud and wth cloud computng.art2 model s appled for clusterng and prefetchng the resources. As shown n the graph the number of jobs rejected s neglgble when compared wth that no cloud s appled. The servce provder's nterest s to mnmze the cost of usng the resources offered by the cloud nfrastructure ether provded by the vendor or n-house faclty and maxmze the revenue (specfcally, net proft) generated through servng consumers applcatons. As shown n the graph 2 cost per task completon remans constant throughout when clusterng and prefetchng s not appled. Along wth the smulaton tme cost per task reduces when ART2 model s used. 39

10 Graph 2 Comparson of cost per task completon Graph 3 Comparson of job rejected In graph 3 a comparson s presented between the Tght and Relaxed-Deadlne when cloud computng provdes the servces by prefetchng the resources. ART2 clusterng technque helps n predctng the resources requred hence resultng n reducton n the number of jobs beng rejected even at tght deadlne. 6 CONCLUSION Cloud computng has the potental of becomng a revolutonary technology that changes the way servce computng s performed. The prncples ntroduced n ths work are essental for the cloud computng vson to materalze to ts full extent. One of the most mportant benefts of Cloud computng s the ablty for Cloud clents to adapt the number of resources used based on ther actual use. Ths has great mplcatons on cost savng as resources are not pad for when they are not used. It follows that an accurate predcton method would greatly ad a Cloud clent n makng ts auto-scalng decsons. Adaptve Resonance Theory-2 dentfes the trends of dfferent request streams n every category by auto classfcatons and organzes pre-allocaton strateges n a predctve way. In the proposed desgn of ntellgent modes of nteracton between the clent request and cloud computng resource manager has resulted n reducton of number of jobs beng rejected and also reducton n cost per task completon. 40

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