Methodology to Determine Relationships between Performance Factors in Hadoop Cloud Computing Applications

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1 Methodology to Determne Relatonshps between Performance Factors n Hadoop Cloud Computng Applcatons Lus Eduardo Bautsta Vllalpando 1,2, Alan Aprl 1 and Alan Abran 1 1 Department of Software Engneerng and Informaton Technology - ETS, Unversty of Quebec, Montreal, Canada 2 Department of Electronc Systems, Autonomous Unversty of Aguascalentes, Aguascalentes, Mexco lebauts@correo.uaa.mx, {alan.aprl, alan.abran}@etsmtl.ca Keywords: Abstract: Cloud Computng, Measurement, Performance, Taguch Method, ISO 25010, Mantenance, Hadoop Mapreduce. Cloud Computng s a model for enablng ubqutous, convenent, on-demand network access to a shared pool of confgurable computng resources. Cloud Computng users prefer not to own physcal nfrastructure, but nstead rent Cloud nfrastructure, a Cloud platform or software, from a thrd-party provder. Sometmes, anomales and defects affect a part of the Cloud platform, resultng n degradaton of the Cloud performance. One of the challenges n dentfyng the source of such degradaton s how to determne the type of relatonshp that exsts between the varous performance metrcs whch affect the qualty of the Cloud and more specfcally Cloud applcatons. Ths work uses the Taguch method for the desgn of experments to propose a methodology for dentfyng the relatonshps between the varous confguraton parameters that affect the qualty of Cloud Computng performance n Hadoop envronments. Ths paper s based on a proposed performance measurement framework for Cloud Computng systems, whch ntegrates software qualty concepts from ISO and other nternatonal standards. 1 INTRODUCTION Cloud Computng (CC) s a model for enablng ubqutous, convenent, on-demand network access to a shared pool of confgurable computng resources (e.g., networks, servers, storage, applcatons, and servces) that can be rapdly provsoned and released wth mnmal management effort or servce provder nteracton (Mell and Grance 2011). Some CC users prefer not to own physcal nfrastructure, but nstead rent Cloud nfrastructure, or a Cloud platform or software, from a thrd-party provder. These nfrastructure applcaton optons delvered as a servce are known as Cloud Servces. Servce models for CC are categorzed as: Infrastructure as a Servce (IaaS), Platform as a Servce (PaaS), and Software as a Servce (SaaS) (ISO/IEC 2011). The model that relates the most to the software engneerng communty s the SaaS model. Software engneers focus on software components, and customers use an IT provder s applcatons runnng on a Cloud nfrastructure to process nformaton accordng to ther processng and storage requrements. One of the man characterstcs of ths type of servce s that customers do not manage or control the underlyng Cloud nfrastructure (ncludng network, servers, operatng systems, and storage), except for lmted user-specfc applcaton confguraton settngs. Performance measurement models (PMMo) for CC, and more specfcally for Cloud Computng Applcatons (CCA), should propose a means to dentfy and quantfy "normal applcaton behavour," whch can serve as a baselne for detectng and predctng possble anomales n the software (.e. applcatons n a Cloud envronment) that may mpact n a Cloud applcaton. To be able to desgn such PMMo for CCA, methods are needed to collect the necessary base measures specfc to performance, and analyss models must be desgned to analyze and evaluate the relatonshps that exst among these measures. One of the challenges n desgnng PMMo for CCA s how to determne what type of relatonshp exsts between the varous base measures. For example, what s the extent of the relatonshp between the amount of physcal memory used and the amount of nformaton to process by an applcaton? Thus, ths work proposes the use of a 375

2 CLOSER2014-4thInternatonalConferenceonCloudComputngandServcesScence methodology based on the Taguch method to determne how closely the performance parameters (base measures) nvolved n the performance analyss process are related. The Taguch method combnes ndustral and statstcal experence, and offers a means for mprovng the qualty of manufactured products. It s based on the robust desgn concept, popularzed by Taguch, accordng to whch a well desgned product should cause no problems when used under specfed condtons (Taguch, Chowdhury et al. 2005). Although the experment presented n ths paper was not developed n a CC producton system, the man contrbuton of ths work s to propose the Taguch method as a way to determne relatonshps between performance parameters of CCA. Ths paper s structured as follows. Secton 2 presents background of concepts related to the performance measurement of CCA and ntroduces the MapReduce programmng model, whch s used to develop CCA. In addton, secton 2 presents the PMFCC, whch descrbes the key performance concepts and sub concepts dentfed from nternatonal standards. Secton 3 presents the method for examnng the relatonshps among the performance concepts dentfed n the PMFCC. In ths secton, an expermental methodology based on the Taguch method of expermental desgn, s used and offers a means for mprovng the qualty of product performance. Secton 4 presents the results of the experment and analyzes the relatonshp between the performance factors of CCA. Fnally, secton 5 presents a synthess of the results of ths research and suggests future work. 2 BACKGROUND 2.1 Performance Analyss n Cloud Computng Applcatons Researchers have studed the performance of CCA from varous vewponts. For example, Jackson (Jackson et al., 2010) analyzes hgh performance computng applcatons on the Amazon Web Servces cloud, wth the objectve of examnng the performance of exstng CC nfrastructures and creatng a mechansm to quanttatvely evaluate them. Hs work s focused on the performance of Amazon EC2 as a representatve example of the current manstream of commercal CC servces, and ts potental applcablty to Cloud-based envronments n scentfc computng envronments. He quanttatvely examnes the performance of a set of benchmarks desgned to represent a typcal Hgh Performance Computng (HPC) workload runnng on the Amazon EC2 platform. Tmng results from dfferent applcaton benchmarks are used to compute a Sustaned System Performance (SSP) metrc, whch s a derved measure for measurng the performance delvered by the workload of a computng system. Accordng to the Natonal Energy Research Scentfc Computng Center (NERSC) (Kramer et al., 2005), SSP s useful for evaluatng system performance across any tme frame, and can be appled to any set of systems, any workload, and/or benchmark sute, and for any tme perod. In addton, SSP measures tme to soluton across dfferent applcaton areas, and can be used to evaluate absolute performance and performance relatve to cost (n dollars, energy, or other value propostons). In hs work, Jackson shows that the SSP metrc has a strong correlaton between the percentage of tme an applcaton spends communcatng and ts overall performance on EC2. Also hghlghted, the more communcaton there s, the worse the performance became. Jackson concludes that the communcaton pattern of an applcaton can have a sgnfcant mpact on performance. Other researchers focus on applcatons n vrtualzed Cloud envronments. For nstance, Me (Me et al., 2010) studes the measurement and analyss of the performance of network I/O applcatons (network-ntensve applcatons) n these envronments. The am of hs research s to understand the performance mpact of co-locatng applcatons n a vrtualzed Cloud, n terms of throughput performance and resource sharng effectveness. Me addresses ssues related to managng dle nstances, whch are processes runnng n an operatng system (OS) that are executng dle loops. Results show that when two dentcal I/O applcatons are runnng together, schedulers can approxmately guarantee that each has ts far share of CPU slcng, network bandwdth consumpton, and resultng throughput. It also shows that the duraton of performance degradaton experenced s related to machne capacty, workload level n the runnng doman, and the number of new vrtual machne (VM) nstances to start up. Although these publcatons present nterestng methods for performance measurement of CCA, the approaches used were from an nfrastructure perspectve and dd not consder CCA performance factors from a software engneerng perspectve. Ths work bases the performance evaluaton of CCA on frameworks developed for data ntensve 376

3 MethodologytoDetermneRelatonshpsbetweenPerformanceFactorsnHadoopCloudComputngApplcatons processng.e. lke Hadoop and MapReduce, and by ntegratng software qualty concepts from ISO 25010, as well as frameworks for Cloud Computng Systems (CCS) performance measurement. Ths approach was taken as a novel way to apply concepts of software engneerng to the new paradgm of cloud computng. 2.2 The ISO 5939 Measurement Process Model The purpose of a measurement process, as descrbed n ISO (ISO/IEC 2008), s to collect, analyze, and report data relatng to the products developed and processes mplemented n an organzatonal unt, both to support effectve management of the process and to objectvely demonstrate the qualty of the products. ISO defnes four sequental actvtes n a measurement process: establsh and sustan measurement commtment, plan the measurement process, perform the measurement process, and evaluate the measurement. These actvtes are performed n an teratve cycle that allows for contnuous feedback and mprovement of the measurement process, as shown n Fgure 1. The frst two actvtes recommended by the ISO measurement process, whch are: 1) establsh measurement commtment; and 2) plan the measurement process, were addressed n the work, "Desgn of a Performance Measurement Framework for Cloud Computng (PMFCC) (Bautsta et al., 2012). In ths paper, the bases for the measurement of Cloud Computng concepts that are drectly related to performance are defned. The PMFCC dentfes terms assocated wth the qualty concept of performance, whch have been dentfed from nternatonal standards such as ISO and those of the European Cooperaton on Space Standardzaton. The PMFCC proposes a combnaton of base measures to determne the derved measures of a specfc concept that contrbutes to performance analyss. 2.3 Performance Measurement Framework for Cloud Computng Jan s System Performance Concepts and Sub Concepts A well known perspectve for system performance measurement was proposed by Jan (Jan, 1991), who suggests that a performance study must frst defne a set of performance crtera (or characterstcs) to help carryng out the system measurement process. He notes that system performance s typcally measured usng three sub concepts, f t s performng a servce correctly: 1) responsveness, 2) productvty, and 3) utlzaton, and proposes a measurement process for each. In addton, Jan notes that there are several possble outcomes for each servce request made to a system, whch can be classfed n three categores. The system may: 1) perform the servce correctly, 2) perform the servce ncorrectly, or 3) refuse to perform the servce altogether. Moreover, he defnes three sub concepts assocated wth each of these possble outcomes whch affect system performance: 1) speed, 2) relablty, and 3) avalablty. Fgure 2 presents the possble outcomes of a servce request to a system and the sub concepts assocated wth them. Fgure 1: Sequence of actvtes n a measurement process (adapted from the ISO 5939 measurement process model (ISO/IEC 2008)). 377

4 CLOSER2014-4thInternatonalConferenceonCloudComputngandServcesScence Fgure 2: Possble outcomes of a servce request to a system, accordng to Jan. by two man sub concepts: 1) performance effcency, and 2) relablty. We have observed that when a CCS receves a servce request, there are three possble outcomes (the servce s performed correctly, the servce s performed ncorrectly, or the servce cannot be performed). The outcome wll determne the sub concepts that wll be used for performance measurement. For example, suppose that the CCS performs a servce correctly, but, durng executon, the servce faled and was later renstated. Although the servce was ultmately performed successfully, t s clear that the system avalablty (part of the relablty sub concept) was compromsed, and ths affected CCS performance Defnton of Cloud Computng Applcaton Performance The ISO (ISO/IEC 2011) standard defnes software product and computer system qualty from two dstnct perspectves: 1) a qualty n use model, and 2) a product qualty model. The product qualty model s applcable to both systems and software. Accordng to ISO 25010, the propertes of both determne the qualty of the product n a partcular context, based on user requrements. Based on Jan s performance perspectves and the man ISO product qualty characterstcs, we propose the followng defnton of CCA performance measurement: The performance of a Cloud Computng applcaton s determned by analyss of the characterstcs nvolved n performng an effcent and relable servce that meets requrements under stated condtons and wthn the maxmum lmts of the system parameters. Although at frst sght ths defnton may seem complex, t only ncludes the sub concepts necessary to carry out CCA performance analyss Defnton of the Performance Measurement Framework for Cloud Computng Performance measurement concepts and sub concepts have prevously been related usng a proposed relatonshp model whch was descrbed n detal n the PMFCC (Bautsta et al., 2012) (see n Fgure 3). Ths model presents the logcal sequence, from top to bottom, n whch the concepts and sub concepts appear when a performance ssue arses n a Cloud Computng System (CCS). In Fgure 3, system performance s determned Fgure 3: Model of the relatonshps between performance concepts and sub concepts. Thus, PMFCC defnes the base measures related to the performance concepts that represent the system attrbutes, and whch can be measured to assess whether or not the CCA satsfes the stated requrements. These base measures are grouped nto collecton functons, whch are responsble for conductng the measurement process usng a combnaton of base measures through a data collector. They are assocated wth the correspondng ISO qualty derved measures, as presented n Table 1. An example of usng the framework s: how can be measured the CC avalablty concept (presented n Table 1) usng the PMFCC? As a frst step, t needs three collecton functons: 1) the tme 378

5 MethodologytoDetermneRelatonshpsbetweenPerformanceFactorsnHadoopCloudComputngApplcatons functon, 2) the task functon, and 3) the transmsson functon. The tme functon can use several dfferent measurements, such as CPU utlzaton by the user, job duraton, and response tme. These base measures can be obtaned usng a data collector, and then send the measures to a tme functon that calculates a derved measure of the tme concept. An ntermedate servce wll be desgned to combne the results of each functon n order to calculate a derved measure of the avalablty that contrbutes to CC performance, as defned n the framework. Table 1: Functons assocated wth Cloud Computng performance concepts. Base Measures Falures avoded Falures detected Falures predcted Falures resolved Breakdowns Faults corrected Faults detected Faults predcted Tasks entered nto recovery Tasks executed Tasks passed Tasks restarted Tasks restored Tasks successfully restored Contnuous resource utlzaton tme Down tme Maxmum response tme Observaton tme Operaton tme Recovery tme Repar tme Response tme Task tme Tme I/O devces occuped Transmsson response tme Turnaround tme Transmsson errors Transmsson capacty Transmsson rato Collecton Functons for Measures Falure functon Fault functon Task functon Tme functon Transmsson functon Hadoop Mapreduce ISO Derved Measures Maturty Resource utlzaton Fault tolerance Maturty Fault tolerance Avalablty Capacty Maturty Fault tolerance Resource utlzaton Tme behavour Avalablty Capacty Maturty Recoverablty Resource utlzaton Tme behavour Avalablty Capacty Maturty Recoverablty Resource utlzaton Tme behavour Hadoop s an Apache Software Foundaton s project, and encompasses varous Hadoop subprojects. The Hadoop project develops and supports the open source software that supples a framework for the development of hghly scalable dstrbuted computng applcatons desgned to handle processng detals, leavng developers free to focus on applcaton logc (Hadoop, 2012). MapReduce s a Hadoop subproject whch s a programmng model wth an assocated mplementaton for processng and generatng large datasets. Accordng to Dean (Dean and Ghemawat, 2008), programs wrtten n ths functonal style are automatcally parallelzed and executed on a large cluster of commodty machnes. Authors lke Ln (Ln and Dyer, 2010) pont out that today s ssue, whch s the need to tackle large amounts of data, s addressed by a dvde-and-conquer approach, where the basc dea s to partton a large problem nto smaller sub problems. Those sub problems can be processed n parallel by dfferent workers; for example, threads n a processor core, cores n a mult-core processor, multple processors n a machne, or many machnes n a cluster. The ntermedate results of each ndvdual worker are combned to yeld the fnal output. 3 METHODOLOGY 3.1 Defnton of the Problem To desgn the proposed collecton functons proposed n the PMFCC (see n Table 1), t s needed to determne how the varous base measures are related and to what degree. Studyng these relatonshps enables assess the nfluence each of them has on the resultng derved measures. The PMFCC shows many of the relatonshps that exst between the base measures that have a major nfluence on the collecton functons. In CCA, and more specfcally n the MapReduce applcatons, there are over a hundred base measures (ncludng system measures) whch could potentally contrbute to the analyss of CCA performance. A selecton of these measures has to be ncluded n the collecton functons so that the respectve measures can be derved, and from there an ndcaton of the performance of the applcatons can be obtaned. There are two key desgn problems to be solved here: 1) establsh whch base measures are nterrelated, and 2) determne how much the nterrelated measures contrbute to each of the collecton functons. In tradtonal statstcal methods, thrty or more observatons (or data ponts) are typcally needed for 379

6 CLOSER2014-4thInternatonalConferenceonCloudComputngandServcesScence each varable observed, n order to gan meanngful nsght. In addton, a few ndependent varables are needed for the experments desgned to uncover potental relatonshps among them. These experments must be performed under certan predetermned and controlled test condtons. However, ths approach s not approprate here, owng to the large number of varables nvolved and the tme and effort that would be requred, whch s much more than we have allowed for n ths step of the research. Consequently, we have to resort to an analyss method that s better suted to our constrants, specfc problem and study area. A possble canddate approach s Taguch s expermental desgn method, whch nvestgates how dfferent varables affect the mean and varance of a process performance characterstc helpng n determnng how well the process s functonng. Ths method only requres a lmted number of experments, but s more effcent than a factoral desgn n ts ablty to dentfy relatonshps and dependences. The next secton descrbes the method and the concepts to be used. 3.2 Taguch s Method of Expermental Desgn Taguch's Qualty Engneerng Handbook (Taguch et al., 2005) descrbes the Taguch method of expermental desgn, whch was developed by Dr. Gench Taguch, a researcher at the Electronc Control Laboratory n Japan. Ths method combnes ndustral and statstcal experence, and offers a means for mprovng the qualty of manufactured products. It s based on the robust desgn concept, popularzed by Taguch, accordng to whch a well desgned product should cause no problems when used under specfed condtons. Accordng to Chekh (Chekh and Abran 2012), Taguch s two phase qualty strategy s the followng: Phase 1: The onlne phase, whch focuses on the technques and methods used to control qualty durng the producton of the product. Phase 2: The offlne phase, whch focuses on takng those technques and methods nto account before manufacturng the product, that s, durng the desgn phase, the development phase, etc. One of the most mportant actvtes n the offlne phase of the strategy s parameter desgn. Ths s where the parameters are determned that make t possble to satsfy the set qualty objectves (often called the objectve functon) through the use of expermental desgns under set condtons. If the product does not work properly (does not fulfl the objectve functon), then the desgn constants (also called parameters) need to be adjusted so that t wll perform better. Chekh explans that ths actvty ncludes several steps, whch are requred to determne the parameters that satsfy the qualty objectves (output). Accordng to Taguch's Qualty Engneerng Handbook, orthogonal arrays (OA) organzes the parameters affectng the process and the levels at whch they should vary. The OA show the varous experments that wll need to be conducted n order to verfy the effect of the factors studed on the output. Taguch s method tests pars of combnatons, nstead of havng to test all possble combnatons (as n a factoral expermental desgn). Wth ths approach, we can determne whch factors affect product qualty the most n a mnmum number of experments. Taguch s OA arrays can be created manually or they can be derved from determnstc algorthms. They are selected by the number of parameters (varables) and the number of levels (states). An OA array s represented by Ln and Pn, where Ln corresponds to the number of experments to be conducted, and Pn corresponds to the number of parameters to be analyzed. Table 2 presents an example of Taguch s OA L4, meanng that 4 experments are conducted to analyze 3 parameters. Table 2: Taguch s Orthogonal Array L4. No. of Experments (L) P1 P2 P An OA cell contans the factor levels (1 and 2) that determne the types of parameter values for each experment. Once the expermental desgn has been determned and the trals have been carred out, the performance characterstc measurements from each tral can be used to analyze the relatve effect of the varous parameters. Taguch s method s based on the use of the sgnal-to-nose rato (SNR), whch s a measurement scale that has been used n the communcatons ndustry for nearly a century for determnng the extent of the relatonshp between the qualty factors n a measurement model. The SNR approach nvolves the analyss of data for varablty, n whch an nput-to-output relatonshp s studed n the 380

7 MethodologytoDetermneRelatonshpsbetweenPerformanceFactorsnHadoopCloudComputngApplcatons measurement system. To determne the effect each parameter has on the output, the SNR (or SN number) s calculated by the formula 1. In ths formula y s the mean value and s s the varance (y s the value of the performance characterstc for a gven experment). where s y SN 10log (1) s 2 1 y N 1 N 1 N u1 2 2 y, u N y, u y u1 =Experment number u=tral number N=Number of trals for experment To mnmze the performance characterstc (objectve functon), the followng defnton of the SNR should be calculated: SN N 2 y u 10 log (2) u 1 N To maxmze the performance characterstc (objectve functon), the followng defnton of the SNR should be calculated: SN N log 2 (3) N u 1 yu Once the SNR values have been calculated for each factor and level, they are tabulated as shown n Table 3, and then the range R (R = hgh SN - low SN) of the SNR for each parameter also s calculated and entered nto Table 3. Table 3: Rank for SNR values. Level P1 P2 P3 1 SN 1,1 SN 2,1 SN 3,1 2 SN 1,2 SN 2,2 SN 3,2 3 SN 1,3 SN 2,3 SN 3,3 Range R P1 R P2 R P3 Rank Accordng to Taguch s method, the larger the R value for a parameter, the greater ts effect on the process. 3.3 Experment Expermental Setup All the experments were conducted on a DELL Studo Workstaton XPS 9100 wth an Intel Core 7 12-core X980 processor runnng at 3.3 GHz, 24 GB DDR3 RAM, a Seagate 1.5 TB 7200 RPM SATA 3Gb/s dsk, and a 1 Gbps network connecton. We used a Lnux CentOS bt dstrbuton and Xen 3.3 as the hypervsor. Ths physcal machne hosts fve vrtual machnes (VM), each wth a dual-core Intel 7 confguraton, 4 GB RAM, 10 GB vrtual storage, and a vrtual network nterface type. In addton, each VM executes the Apache Hadoop dstrbuton verson , whch ncludes the Hadoop Dstrbuted Fle System (HDFS) and MapReduce framework lbrares. One of these VM s the master node, whch executes NameNode (HDFS) and JobTracker (MapReduce), and the rest of the VM are slave nodes runnng DataNodes (HDFS) and JobTrackers (MapReduce). The Apache Hadoop dstrbuton ncludes a set of applcatons for testng the performance of a cluster. Accordng to Hadoop (Hadoop 2012), these applcatons can test varous cluster characterstcs, such as network transfer, storage relablty, cluster avalablty, etc. Four applcatons were selected to obtan performance measures from the Hadoop cluster as for example; the amount of physcal memory used by a Job s a measure that vares accordng to values gven to confguraton parameters, such as the number of fles to process, the amount of nformaton to process, etc. The vewpont taken for the selecton of the above applcatons s that t s possble to use the same type s o parameters to confgure each applcaton as well as cluster machne. Below s a bref descrpton of the applcatons used n the experments: 1. TestDFSIO. Ths s a MapReduce applcaton that reads and wrtes the HDFS test. It executes tasks to test the HDFS to dscover performance bottlenecks n the network, to test the hardware, the OS, and the Hadoop setup of the cluster machnes (partcularly the NameNode and the DataNodes), and to determne the speed of the cluster n terms of I/O. 2. TeraSort. The goal of ths applcaton s to sort large amounts of data as fast as possble. It s a benchmark applcaton that combnes HDFS testng, as well as testng of the MapReduce layers of a Hadoop cluster. 3. MapRedRelablty. Ths program tests the 381

8 CLOSER2014-4thInternatonalConferenceonCloudComputngandServcesScence relablty of the MapReduce framework by njectng faults/falures nto the Map and Reduce stages. 4. MapRedTest. Ths applcaton loops a small job a number of tmes, placng the focus on the MapReduce layer and ts mpact on the HDFS layer. To develop the set of experments, three parameters were selected, whch can be set wth dfferent values for each type of applcaton. These parameters are: 1) the number of fles to process, 2) the total number of bytes to process, and 3) the number of tasks to execute n the cluster. Also, a number of dfferent MapReduce base measures such as Job Duraton, Job Status, Amount of Amount of physcal memory used, etc. were selected as possble qualty objectves (objectve functon). These base measures are related to one or more of the performance derved measures dentfed n the PMFCC Defnton of Factors and Qualty Objectve In a vrtualzed Cloud envronment, Cloud provders mplement clusterng by slcng each physcal machne nto multple vrtual machnes (VM) nterconnected through vrtual nterfaces. So, we establshed a vrtual cluster wth the features mentoned above, n order to obtan representatve results. Ffty experments were performed to test the Hadoop vrtual cluster, varyng the three parameters mentoned prevously. In each experment, four dfferent applcatons were executed, and performance results were recorded for ther analyss. In ths way, the set of experments nvestgates the effect of the followng varables (or control factors, accordng to the Taguch termnology) on the output dependent varable: Number of fles to be processed by the cluster Total number of bytes to be processed by the cluster Number of tasks nto whch to dvde the Job applcaton Accordng to Taguch, qualty s often referred to as conformance to the operatng specfcatons of a system. To hm, the qualty objectve (or dependent varable) determnes the deal functon of the output that the system should show. In our experment, the observed dependent varable s the followng: Amount of physcal memory used by the Job (Mbytes) Experment Development Accordng to the Hadoop documentaton, the number of fles and the amount of data to be processed by a Hadoop cluster wll be determned by the number of processors (cores) avalable and ther storage capacty. Also, the number of tasks to be processed by the cluster wll be determned by the total of number of processng unts (cores) n the cluster. Based on the above premses and the confguraton of the expermental cluster, we have chosen two levels for each parameter n the experment. We determne the dfferent levels of each factor n the followng way: Number of fles to process: o Small set of fles: fewer than 10,000 fles for level 1; o Large set of fles: 10,000 fles or more for level 2. Number of bytes to process, as determned by the storage capacty of the cluster: o fewer than 10,000 Mbytes to process for level 1 (a small amount of data to process); o 10,000 or more Mbytes to process for level 2 (a large amount of data to process). Number of tasks to create, determned, accordng to the MapReduce framework, by the number of processng unts (cores) n the cluster and the number of nput fles to process. Snce the cluster has a total of 10 cores, we decded to perform tests wth: o fewer than 10 tasks for level 1; o 10 or more tasks for level 2. Table 4 Presents a summary of the factors, levels, and values for ths experment. Factor Number Table 4: Factors and Levels. Factor Name Level 1 Level 2 Number of fles to process Number of MB to process Number of tasks to create < 10,000 10,000 < 10,000 10,000 < Usng Taguch s expermental desgn method, the selecton of the approprate OA s determned by the number of factors and levels to be examned. The resultng OA array for ths case study s L4 (presented n Table 2). The assgnment of the varous factors and values of ths OA array s shown n Table

9 MethodologytoDetermneRelatonshpsbetweenPerformanceFactorsnHadoopCloudComputngApplcatons No. of the Experment (L) Table 5: Matrx of Experments. Number of Fles Number of Bytes (MB) Number of Tasks 1 < 10,000 < 10,000 < 10 2 < 10,000 10, ,000 < 10, ,000 10,000 < 10 Table 5 shows the set of experments to be carred out wth dfferent values for each parameter selected. For example, experment 2 nvolves fewer than 10,000 fles, the number of bytes to be processed s greater than or equal to 10,000 Mbytes, and the number of tasks s greater than or equal to 10. A total of 50 experments were carred out by varyng the parameter values. However, only 12 experments met the requrements presented n Table 5. Ths set of 12 experments was dvded nto three groups of four experments each (called trals). The values and results of each experment are presented n Table 6. Taguch s method defned the SNR used to measure robustness, whch s the transformed form of the performance qualty characterstc (output value) used to analyze the results. Snce the objectve of ths experment s to mnmze the qualty characterstc of the output (amount of physcal memory used per Job), the SNR for the qualty characterstc the smaller the better s gven by formula 4, that s: SN log N u 1 2 y u N 10 (4) The SNR result for each experment s shown n Table 7. Accordng to Taguch s method, the factor effect s equal to the dfference between the hghest average SNR and the lowest average SNR for each factor. Ths means that the larger the factor effect for a parameter, the larger the effect the varable has on the process, or, n other words, the more sgnfcant the effect of the factor. Table 8 shows the factor effect for each varable studed n the experment. Table 6: Trals, experments, and resultng values. Tral Experment Number of Fles Mbytes to Process Num. of Tasks Physcal Memory (Mbytes) , , ,000 10, , , ,000,000 10,000, ,000 10,000 1, ,000,000 3, ,000,000 50, Table 7: SNR results. Experment Number of Mbytes to Number of Physcal Memory Physcal Memory Physcal Memory Fles Process Tasks Tral 1 Tral 2 Tral 3 SNR 1 < 10,000 < 10,000 < < 10,000 10, ,000 < 10, ,000 10,000 < Table 8: Factor Effect on the Output Objectve Number of Mbytes to Fles Process Number of Tasks Average SNR at Level Average SNR at Level Factor Effect (dfference) Rank

10 CLOSER2014-4thInternatonalConferenceonCloudComputngandServcesScence 4 RESULTS 4.1 Analyss and Interpretaton of Results Based on the results presented n Table 8, we can observe that: Number of tasks s the factor that has the most nfluence on the qualty objectve (physcal memory used) of the output observed, at Number of Mbytes to process s the second most nfluental factor, at Number of fles to process s the least nfluental factor n ths case study, at Fgure 4 presents a graphcal representaton of the factor results and ther levels. 0,6 0,4 0,2 0 0,2 0,4 0,6 L1 L2 Fgure 4: Graphcal representaton of factors and ther SNR levels. To represent the optmal condton of the levels, also called the optmal soluton of the levels, an analyss of SNR values s necessary n ths experment. Whether the am s to mnmze or maxmze the qualty characterstc (physcal memory used), t s always necessary to maxmze the SNR parameter values. Consequently, the optmum level of a specfc factor wll be the hghest value of ts SNR. It can be seen that the optmum L1 L2 Number of Fles Number of tasks Mbytes to process L1 L2 level for each factor s represented by the hghest pont n the graph (as presented n Fgure 4); that s, L1, L1, and L2 respectvely. Usng the fndngs presented n Tables 7 and 8 and n Fgure 4, we can conclude that the optmum levels for the factors n ths experment based on the expermental confguraton cluster are: Number of fles to process: The optmum level s fewer than 10,000 fles (level 1). Total number of Mbytes to process: The optmum level s fewer than 10,000 Mbytes (level 1). Number of tasks to be created to dvde the Job: The optmum level s greater than or equal to 10 tasks or more per Job (level 2). 4.2 Statstcal Data Analyss The analyss of varance (ANOVA) s a statstcal technque usually used n the desgn and analyss of experments. Accordng to Trved (Trved, 2002), the purpose of applyng the ANOVA technque to an expermental stuaton s to compare the effect of several factors appled smultaneously to the response varable (qualty characterstc). It allows the effects of the controllable factors to be separated from those of uncontrolled varatons. Table 9 presents the results of ths analyss of the expermental factors. As can be seen n the contrbuton column of Table 9, these results can be nterpreted as follows (represented graphcally n Fgure 5): Number of tasks s the factor that has the most nfluence (43% of the contrbuton) on the physcal memory n ths case study. Total number of bytes to process s the factor that has the second greatest nfluence (34% of the contrbuton) on the processng tme. Number of fles s the factor wth the least nfluence (23% of the contrbuton) on the processng tme n the cluster. Table 9: Analyss of Varance (ANOVA). Factors Degrees of Sum of Squares Varance Contrbuton Varance Freedom (SS) (MS) (%) raton (F) No. of fles Total no. of bytes to process No. of tasks Error Total Error estmate

11 MethodologytoDetermneRelatonshpsbetweenPerformanceFactorsnHadoopCloudComputngApplcatons Fgure 5: Percentage contrbuton of factors. In addton, based on the column related to the varance rato F shown n Table 9, we can conclude that the followng: The factors Number of tasks and Number of Mbytes to process have the most domnant effect and the second most domnant effect on the output varable respectvely. Accordng to Taguch s method, the factor wth the smallest contrbuton s taken as the error estmate. So, the factor Total number of fles to process s taken as the error estmate, snce t corresponds to the smallest sum of squares. The results of ths case study show, based on both the graphcal and statstcal data analyses of the SNR, that the number of tasks nto whch to dvde the Job n a MapReduce applcaton n our cluster has the most nfluence, followed by the number of bytes to process, and, fnally, the number of fles. To summarze, when an applcaton s developed n the MapReduce framework to be executed n ths cluster, the factors mentoned above must be taken nto account n order to mprove the performance of the applcaton, and, more specfcally, the output varable, whch s the amount of physcal memory to be used by a Job. 5 SUMMARY One of the challenges n CC s to delver good performance to ts end users. In ths paper, we present the results of usng a method that determnes the relatonshps among the CCA performance parameters. Ths proposed method s based on a performance measurement framework for Cloud Computng (PMFCC) system, whch defnes a number of terms that are necessary to measure the performance of CCS usng software qualty concepts. The PMFCC defned several collecton functons whch are automated to obtan derved measures and enable analyss of the performance of a CCA. One of the challenges we faced n desgnng these functons was to decde how to determne the extent to whch the base measures are related, and ther nfluence n the analyss of CCA performance. To address ths challenge, we proposed the use of Taguch s method of expermental desgn. Usng ths expermental desgn method, we carred out experments to analyze the relatonshps between the confguraton parameters of several Hadoop applcatons and ther performance qualty measures based on the amount of physcal memory used by a Job. We found that there s a strong relatonshp between the number of tasks executed by a MapReduce applcaton and the amount of physcal memory used by a Job. Our next research actvty wll be to reproduce ths experment n a producton envronment, n order to confrm these tral group results wth greater certanty. Also, ths early research work serves as a bass for a next actvty that wll need to determne the most mportant relatonshps between the performance concepts defned n the PMFCC and enable us to propose a robust model for CCA performance analyss. Further research s also needed on the desgn of measurement models and mechansms to analyze the performance of a real Cloud Computng applcaton, whch could also contrbute to further valdate our proposed method. Such evaluaton work would nclude performance concepts related to software, hardware, and networkng. These concepts would be mapped to the collecton functons dentfed n the PMFCC prevously developed n order to mprove t. We expect that t wll be possble, based on ths work, to propose a robust model n future research that wll be able to analyze Hadoop cluster behavor n a real Cloud Computng envronment. Ths would allow real tme detecton of anomales that affect CCS and CCA performance. REFERENCES Bautsta, L., A. Abran, et al. (2012). "Desgn of a Performance Measurement Framework for Cloud Computng." Journal of Software Engneerng and Applcatons 5(2): Chekh, L. and A. Abran (2012). "Investgaton of the Relatonshps between the Software Qualty Models of ISO 9126 Standard: An Emprcal Study usng the Taguch Method." Software Qualty Professonal Magazne. Dean, J. and S. Ghemawat (2008). "MapReduce: 385

12 CLOSER2014-4thInternatonalConferenceonCloudComputngandServcesScence smplfed data processng on large clusters." Communcatons of the ACM 51(1): Hadoop, A. F. (2012). "What Is Apache Hadoop?", from ISO/IEC (2008). ISO/IEC 15939:2007 Systems and software engneerng Measurement process. Geneva, Swtzerland, Internatonal Organzaton for Standardzaton. ISO/IEC (2011). ISO/IEC 25010: Systems and software engneerng Systems and software product Qualty Requrements and Evaluaton (SQuaRE) System and software qualty models. Geneva, Swtzerland, Internatonal Organzaton for Standardzaton: 43. ISO/IEC (2011). ISO/IEC JTC 1 SC38:Study Group Report on Cloud Computng. Geneva, Swtzerland, Internatonal Organzaton for Standardzaton. Jackson, K. R., L. Ramakrshnan, et al. (2010). Performance Analyss of Hgh Performance Computng Applcatons on the Amazon Web Servces Cloud. IEEE Second Internatonal Conference on Cloud Computng Technology and Scence (CloudCom), Washngton, DC, USA, IEEE Computer Socety. Jan, R. (1991). The Art of Computer Systems Performance Analyss: Technques for Expermental Desgn, Measurement, Smulaton, and Modelng. New York, NY, John Wley & Sons - Interscence. Kramer, W., J. Shalf, et al. (2005). The NERSC Sustaned System Performance (SSP) Metrc. Calforna, USA, Lawrence Berkeley Natonal Laboratory. Ln, J. and C. Dyer (2010). Data-Intensve Text Processng wth MapReduce. Unversty of Maryland, College Park, Manuscrpt of a book n the Morgan & Claypool Synthess Lectures on Human Language Technologes. Me, Y., L. Lu, et al. (2010). Performance Measurements and Analyss of Network I/O Applcatons n Vrtualzed Cloud. IEEE Internatonal Conference on Cloud Computng, CLOUD 2010, Mam, FL, USA, IEEE. Mell, P. and T. Grance (2011). The NIST Defnton of Cloud Computng. Gathersburg, MD, USA, Informaton Technology Laboratory, Natonal Insttute of Standards and Technology: 2-3. Taguch, G., S. Chowdhury, et al. (2005). Taguch's Qualty Engneerng Handbook, John Wley & Sons, New Jersey. Trved, K. S. (2002). Probablty and Statstcs wth Relablty, Queung and Computer Scence Applcatons. New York, U.S.A., John Wley & Sons, Inc. 386

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