Capacity Planning in J2EE Environments

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1 Alexandra Vogler Master Thesis in Communication Sciences 17 March September 2003 Capacity Planning in J2EE Environments Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland Royal Institute of Technology (KTH), Stockholm, Sweden R2Meton AB (R2M), Kista, Sweden

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3 Abstract Capacity Planning is planning efficient resource use by applications during the development and deployment of an application, but also when it is operational. It addresses considering how different resources can be accessed simultaneously by different applications, but also knowing when it is done in an optimal way. This master thesis answers the following questions: How is capacity planning performed in Java to Enterprise Edition (J2EE) environments? How must J2EE be used and tested for good capacity planning? Big organisations and operational environments have high expectations in means of capacity planning. The J2EE architecture is still young and no established guidelines for capacity planning exist to this day. The thesis considers which type of configurations (clustered, unclustered, etc.) should be taken into consideration, which types / forms / categories of applications can run on the same server / cluster, but also what should be avoided when planning for capacity. It gives the guidelines for carrying out capacity planning tests. These were put into practice in a J2EE environment with Oracle as database, WLS as a server and the Grinder as performance tuning tool. Key words: J2EE, Capacity Planning, Tuning, Benchmarking, Profiling, Load Testing, the Grinder 3

4 Acknowledgements To my sisters, Catherine Vogler and Laura Vogler, for their language corrections and their support. To my colleague Christopher Suarez, for all his precious help. To my project managers Magnus Kling and Per Lindström for their precious advise. To my parents, Atie Vogler and Peter Vogler for their support and encouragements throughout my studies. And a very special thanks to Camille Boucarut, Géraud Mudry and David Poirier- Quesnel. And last but not least to all my friends without whom these master studies would never have been such a great experience. 4

5 Practical information Student: Alexandra Vogler Title: Capacity Planning in J2EE Environments Form: Done in the industry in partnership with foreign institution Begin: End: Company: R2M, Kista, Sweden Project managers: Per Lindström / Magnus Kling Foreign Institution: NADA (Numerical Analysis and Computer Science), KTH (Royal Institute of Technology), Stockholm, Sweden Supervisor: Serafim Dahl Responsible professor: Stefan Arnborg Home institution: LSIR (Distributed Information Systems Laboratory), EPFL (Swiss Federal Institute of Technology), Lausanne, Switzerland Responsible professor: Karl Aberer 5

6 Time plan Definition of subject. Information collecting period Start of diploma work. Meetings with Ulf Börjesson, Per Lindström, Magnus Kling and Serafim Dahl Description sent to Karl Aberer Meeting with PL and MK First version (skeleton) of report sent to UB, PL, MK, SD and KA BEA & R2M seminar BEA, Oracle & Sun seminar Report to be sent to UB, PL, MK, SD and KA with subject clearly defined and all references used till date mentioned. Background part well ahead. Time specification finished Learning use of capacity planning tools Correction of report Correction of report. Meeting with PL and MK Deadline for handing in time specification for KTH Participation in capacity planning tests for one of R2M s clients Report to be sent to UB, PL, MK, SD and KA. Part about capacity planning tools in practice well ahead Mandatory course in report writing for master thesis students at KTH Additional 2 weeks of information collecting Report to be sent to UB, PL, MK, SD and KA. Part about capacity planning tools in practice finished Practical tests in the industry and research on mathematical models for capacity planning Report to be sent to UB, PL, MK, SD and KA. Background part finished. Practical tests in the industry part started Holidays Holidays Report writing & correction Finalisation of report for KTH Research on other capacity planning models to add to report for EPFL and R2M Report to be sent to UB, PL, MK, SD and KA. Analysis and conclusion finished Preparation of presentation at KTH and R2M Deadline to send report from Sweden to be received on time at EPFL Presentation of master thesis at R2M Deadline at EPFL to receive report Examination at KTH Preparation of presentation at EPFL. Examination at EPFL 6

7 Table of Contents Abstract...3 Acknowledgements...4 Practical information...5 Time plan...6 Table of Contents Introduction Motivation R2Meton AB Definition of the study field Objectives Limitations of the study Method Outline of the report Optimisation of applications and capacity planning J2EE Software performance theory Hardware criteria Performance tuning Capacity Planning Samuel Kounev s Capacity Planning Methodology Limitations and critical success factors Limitations Optimisation Smaller scale testing Critical success factors Platform specificity Subjectivity of capacity planning Capacity planning in J2EE environments Survey of former capacity planning tools and methodologies The Grinder Introduction Using the Grinder Properties Communication between the Console, agent and worker processes HTTP plug-ins The TCPSniffer Output The Console How we used the Grinder Kooperativa Förbundet Project About Kooperativa Förbundet R2M s project for Kooperativa Förbundet Testing of Kooperativa Förbundet s EAI with the Grinder Testing in practice Testing environment Script recording Grinding Analysis of results

8 4.4.5 Changes made to the Grinder source code Guidelines for efficient capacity planning Application of the method Shop Express Modification of Grinder use Testing environment Use cases Carrying out the tests Collecting data Analysis Analysis Validity of capacity planning Interest of capacity planning Tools Does Capacity Planning have a future? Conclusion References Table of Figures Table of Tables Appendixes Appendix A: Grinder scripts tomodifygrinder.properties constant.properties buildproperties.sh grinder.properties snifferscript.sh grind.sh console.properties buildconsoleproperties.sh grindconsole.sh Appendix B: Practical industry test scripts grind.properties datagenerator.properties GetEngagementPerformanceTestBean.java DataGenerator.java _1_getaddengagement.bean e1_1_engagement.mmkffepf http-plugin-sniffer-post-addengagement http-plugin-sniffer-post-addengagement_modified

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11 1 Introduction This introduction first of all aims at explaining the motivations for studying capacity planning in Java 2 Enterprise Edition (J2EE). It will then briefly present R2Meton AB, the company in which I did my master thesis, and then define the study field. It will thereafter give the main objectives of the study, before giving its limitations, to finally tell how I got along with the study, before giving the reports outline. 1.1 Motivation In the academic world one mostly considers theory, practical details are often mentioned but not really studied. During various experiences gained during summer jobs or even while doing projects in an academic frame, I noticed practical details are often overwhelming and strongly influence the development of applications. For my master thesis I thus chose to focus on a practical subject, which at the same time is a quite unexplored research field. Large-scale projects cannot be efficiently pursued without some preliminary planning, be it software development planning or planning how an application will work once it is put into use. A crucial point is taking into account available hardware resources, and having in mind that the prototype of an application might be very efficient at a small scale, but that performance can considerably deteriorate when the whole system is deployed in its operational form. Therefore, when developing or deploying an application for specific needs, one should not only look at how it scales when it is deployed but also start planning for capacity during the development of the application. Since this subject is quite unexplored but more than useful for the industrial or academic implementation of any kind of application, I chose to devote my master thesis to finding guidelines for efficient capacity planning, and this in the growing world of J2EE. 1.2 R2Meton AB R2Meton AB, also known as R2M, is a Swedish IT-company based in Kista, Stockholm s silicon valley. Their motto is R2M Optimising business activities. R2M helps optimising business activities through the following channels: project management (offering Enterprise Application Integration (EAI) solutions), modelling (member of DSDM Consortium, an international organisation for systems development with a business utility and user focus), tools (to support iterative and incremental development methods) and training (offering teachers and course material). R2M has partnerships with among others Oracle, BEA Systems, Sun Microsystems, Hewlett Packard and RSA Security. 11

12 The different areas R2M has specialised in are: Operational support for J2EE applications, including technical reviewing of development, testing and operational environments, as well as performance and architectural optimisation, finding errors and testing, putting in place operational frames in order to give quality assurance of the administrative processes, and installing J2EE clusters. Enterprise Application Integration (EAI) Process mapping using the Process Workshop method to model processes Multitier architectures including middleware, using among others Enterprise Java Beans (EJB), Oracle databases, WebLogic Server (WLS) and Tuxedo [1]. My master thesis relates to them both by its field in J2EE, one of their main capabilities, and by being one of their main concerns, that is optimisation and forehand planning before deploying an application. Capacity planning in general and especially in J2EE is a quite unexplored field. Giving guidelines to a proper way of including capacity planning in all kinds of planning could reveal useful to future services they propose. Besides, doing my master thesis in partnership with R2M gave me opportunities to see how it is applied in other domains than J2EE and applying it in practice for one of their clients. 1.3 Definition of the study field Performance and scalability issues in e-business systems are gaining in importance as we move from prototypes to real operational systems [2]. After a paragraph about J2EE s specificities, the rest of this section will give an accurate definition of capacity planning. Over the past couple of years, the Java 2 Enterprise Edition (J2EE) Platform has established itself as the technology of choice for developing modern e-business solutions. A large part of this success is due to the fact that J2EE is not a proprietary product, but rather an industry standard, developed as the result of a large industry initiative led by Sun Microsystems. The goal of this initiative was to establish a standard middleware framework for developing enterprise-class distributed applications in Java. Over 30 enterprise software vendors have participated in this effort and have come up with their own implementations of J2EE. These products are all based on a common standard and this standard itself is based on Java, which ensures J2EE applications to be not only operating system independent, but also portable across a wide range of middleware platforms - J2EE implementations. This enables customers to develop their applications in a platform-independent manner and gives them a wide selection of products where they can later deploy them on. Giving customers this freedom of choice encourages best-of-breed products to compete and establish themselves in the market. However, once product functionality is standardised, the focus is placed on the performance and scalability of the underlying platforms. It is exactly here that companies strive to distinguish their products in the market and gain advantages over their competitors [2]. 12

13 Capacity planning is about how many resources an application uses. It implies knowing a system s profile. For instance, if you have two applications, e.g. application A and application B, each known to use certain values of Central Processing Unit (CPU), memory, disk and network resources when being the single application running on your machine, but you only have one machine, you would of course be interested by running these applications on the same machine. Figure 1 gives an illustration of this example. If application A uses only little of one resource and application B much of the same one, can they easily live together? This is a simple case of capacity planning, and one must have in mind that when the applications are executed in parallel on the machine, the total resource usage is no simple addition of the sole execution of each one of them. There could for instance be overlapping of memory portions, which would make parallel execution impossible, and re-writing the code would be necessary to empower this. Used percentage of resource Application A Application B CPU Memory Disc Network [Fig. 1] Resource usage of different applications But further than this simple example, how does this idea scale with a complete, large-sized machine park? Software development is followed by deployment of the application that is then put into operation. We will see capacity planning can be applied at these different stages, because one would of course like to know as soon as possible how many machines are needed for an application to run properly, or if a system should be wholly changed. The process of determining what type of hardware and software configuration is required to adequately meet application needs is called capacity planning [3]. Because the needs of an application are determined among others by the number of users, in other words the number of parallel accesses, capacity planning could also be defined as answering the following question: how many users can a system handle before changes need to be made? Thus, when an application is deployed, one should have in mind how large it will be at first, but also how fast the number of users/servers/machines will increment, so that enough margin is left and 13

14 one must not wholly change an application because of e.g. the addition of a single user. To perform capacity planning, essential data is collected and analysed to determine usage patterns and to project capacity requirements and performance characteristics, especially in a J2EE environment. Tools are used to determine optimum hardware/software configurations. Thereafter, the technologies used for workload management of web access of enterprise applications can be reviewed [4]. If accurate capacity planning is performed, we get another way of achieving scalability in J2EE. 1.4 Objectives This master thesis focuses on how J2EE must be used and tested for good capacity planning. Since capacity planning is about planning efficient resource use by applications it implies answering the following questions: how can different resources be accessed simultaneously by different applications (it is of course not the simple addition of sole access)? And how do you know when this is done in an optimal way? Big organisations and operational environments have high expectations in means of capacity planning. The J2EE architecture is still young and as far as known no established guidelines for capacity planning exist to this day. I strived to give guidelines to an optimal way of doing this. Of course, capacity planning must be linked to operational planning. Initial questions to be answered in connection with the subject were (presented here without any priority or mutual cohesion): Which type of configurations (clustered, unclustered, etc.) should be taken into consideration? Which types / forms / categories of applications can run on the same server / cluster? What should be avoided? How should capacity tests be carried out (guidelines to be given)? How / when can capacity planning be used for operation, early warning, etc. 1.5 Limitations of the study Every J2EE application is truly unique, is it thus difficult to extrapolate performance measurements [6]. Giving precise guidelines that can be used for capacity planning any kind of application thus revealed quite difficult. The guidelines I elaborated, which are stated in chapter 5 are thus quite general. Other limitations of the study are specified in chapter 3. 14

15 1.6 Method The study first consisted of a six-week reading part about the topic, gathering information from various sources. It then broke down to finding relevant existing solutions for capacity planning. Since no existing solution fulfilled the requirements, I established own guidelines for capacity planning. I then first studied the Grinder, a tool that could reveal to be useful for capacity planning in the development of most J2EE applications. Later on, practical capacity planning tests were pursued at R2M for one of their clients. These tests were done for a J2EE platform, using the Grinder, BEA s WebLogic Server (WLS) and Oracle as a database, and follow the guidelines I established. 1.7 Outline of the report Chapter 2 focuses on optimisation of applications and capacity planning. It first recalls some facts about J2EE, before distinguishing software criteria from hardware criteria. It then defines vocabulary used in relationship with capacity planning, in order top give a more precise definition of it. It finally presents a survey of existing capacity planning strategies. Chapter 3 is a prelude to chapter 4, it is about limitations and critical success factors that must be taken into account when planning for capacity. It lists views of the problem that were not treated and why they were not considered, and in which frames and under which restrictions the actual problem is dealt with. Chapter 4 lists former methods for capacity planning in J2EE and explains how the Grinder tool should be used. Chapter 5 is the main chapter of the report, it gives the guidelines I elaborated. My method was applied in test using the Grinder; the testing process and test results and analysis are given in chapter 6. Chapter 7 is an analysis about the current situation of capacity planning. Chapter 8 concludes the report in a more personal way. 15

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17 Optimisation of applications and capacity planning 17

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19 2 Optimisation of applications and capacity planning This master thesis considers capacity planning in the specific case of J2EE environments. J2EE being an extension of platform-portable Java, it offers the advantage that a same software program can be used on different hardware configurations. Whereas software performance theory requirements and guidelines have been discussed many times, among others in what one would call the bible of software engineering, or Ian Sommerville s Software Engineering [7], much less has been written on joint hardware and software planning and testing. Whereas software engineering theory can be considered quite independently from the language, when talking about hardware, even though some rough ideas apply to all kind of architectures, in this field, theory is much more linked to a specific platform and the actual hardware structure. Capacity planning is a quite unknown field. Besides, much confusion is made when defining performance testing, benchmarking and profiling and tuning, and distinguishing these from optimising. Recalling the exact meanings of these terms will enable giving a clearer definition of capacity planning. This chapter focuses on optimisation of applications and capacity planning in J2EE, first giving a review about J2EE, and then recalling the main ideas of software performance theory. Software criteria will then be distinguished from hardware criteria. Thereafter a clear definition of the different stages of performance tuning will be given, before defining capacity planning and listing its challenges. This chapter ends with a presentation of Samuel Kounev s capacity planning methodology. 2.1 J2EE The J2EE Platform for e-business is an industry standard defined by Sun Microsystems, Inc. The importance of J2EE on the market is constantly growing, as the following statistics from May 2002 about the use of technologies by e-business systems show: 80% of all enterprises use the Java language (statistics from Gartner, an Information Technologies (IT) consulting company), 92% of companies that choose J2EE for enterprise computing are happy with their choice (statistics from Forrester, another IT consulting company) and 78% of executives view J2EE as the most effective platform for building and deploying web services. Economically, the J2EE Platform market is 2 milliard US dollars (about 2.6 milliard Swiss francs or 15.7 milliard Swedish crowns) strong and growing. Even though the numerical values might have varied, the overall idea to keep in mind is Java and especially the J2EE extension are today's number one technology for e-business [8]. Over 35 implementations of J2EE exist on the market, so called application servers. The latter provide a range of middleware services crucial for today s e- commerce systems. Since functionality is standardized, the distinguishing factor 19

20 becomes performance. Therefore, standard industry benchmarks are needed to measure performance and scalability of application servers. To avoid speculations and misuse of results testing should be monitored and controlled [8]. But throughout this report, one should have in mind that every J2EE application is truly unique, and that it is thus difficult to extrapolate performance measurements from one application to another [6]. 2.2 Software performance theory Relying on Ian Sommerville s Software Engineering [7], software performance testing should be done in the following ways: Verification and validation, for which the testing process, test planning and testing strategies (top-down, bottom-up, thread, stress and back-toback testing) are clearly defined. Defect testing, which can be done either by black-box testing, structural testing or interface testing. Static verification, which can be done by program inspections or mathematically based verification, using static analysis tools. Guidelines for static verification techniques have been formally specified in among others the Cleanroom software development [Mills, 1987; Cobb and Mills, 1990; Linger, 1994]. But these testing strategies do not take the underlying hardware configuration into account. Therefore, the capacity planning guidelines of chapter 5 are of course inspired by and reuse quite many of the software testing concepts, but also complete them, since they also take into account the underlying hardware mechanisms. 2.3 Hardware criteria Code optimisation should be distinguished from overall performance. Indeed, an application optimised in terms of software can see its performance deteriorate if the hardware upon which the application is run is not optimised for the application. Hardware factors are not considered in software testing but must be in capacity planning, since the correspondence between hardware and software is what capacity planning is all about. Therefore, capacity planning also defines some hardware criteria. Defining hardware criteria necessarily implies a good knowledge of used hardware [5]. But when talking about an application, one should also know which underlying mechanisms the application builds on, i.e. which type of servers and databases, and on which hardware these mechanisms run. One can then document oneself on the exact versions of the different hardware components that are used, how databases or servers should be tuned in accordance, and then run the application on top. When tuning the application with knowledge of the hardware s limitations, one 20

21 will not try to meet requirements that are not achievable. This is thus a step towards correctly performed capacity planning. 2.4 Performance tuning As further sections will make clearer, performance tuning is part of capacity planning, since it comprises load management, one of capacity planning s main concerns. Many expressions are commonly used in relation to the performance of a software application: testing, benchmarking, profiling and tuning to name but a few. Each of these terms has an explicit meaning, although they often seem to be used interchangeably. Everyone having their own ideas about which aspect of performance is most relevant to their work at a given moment partly explains why such confusions are often made [6]. I would like to reuse the definitions of Peter Zadrozny in J2EE Performance Testing with BEA WebLogic Server [6] throughout this report for a clear terminology definition. It states: Performance testing: using a defined methodology in order to collect performance statistics for an application. Benchmarking: generally speaking, benchmarking is the process of recording the performance of an application in various environments and under various workloads. Profiling: this involves investigating exactly where an application is spending most of its compute cycles and how efficiently it uses system resources. Tuning: testing, benchmarking, and profiling all feed the process of tuning, which is the optimisation of an application and its environments for maximum performance. One should differentiate performance testing from designing for performance [6]. Whereas performance testing is part of the tuning process, that is, the optimisation of an application and its environments for maximum performance, designing for performance is the creation of powerful programs. Performance testing actually breaks down to defining why, how, where and with what means an application should be tested. Benchmarking J2EE Performance is not easy. The Standards Performance Evaluation Corporation (SPEC) has taken the problem seriously and is soon going to provide the right benchmarks and tools. Performance prediction for sizing and capacity planning is an extremely difficult and challenging area [2,8]. This development has led to the emergence of standard benchmarks for measuring performance and scalability of middleware products. A typical example is the ECperf benchmark, which was prototyped and built by Sun in conjunction with J2EE application server vendors under the Java Community Process. Server vendors can use ECperf to measure, optimise and showcase their product s performance and scalability. Users, on the other hand, can use it to gain a better understanding and insight into the tuning and optimisation issues surrounding the development of 21

22 modern J2EE-based applications. ECperf can be deployed on a BEA WebLogic Server and experiments and tests conducted with it [2]. Nevertheless, I did not use ECPerf when performing tests for R2M. J2EE Benchmarking allows measuring and comparing the performance and scalability of J2EE-based environments. However, it does not address the problem of predicting performance for the purposes of system sizing and capacity planning. But a benchmarking tool is necessary for accurately performing capacity planning. Various tools exist for profiling all kinds of systems, and can of course be used in J2EE environments as well. Once one has defined a performance testing methodology and has benchmarked and profiled an application, the application is tuned in its environment, i.e. on the different servers and hardware components the application runs on. The benchmarking and profiling actions are then repeated in order to see if the changes improved the application s velocity or load management for instance, or any other previously expressed capacity planning requirement. Once the capacity planning requirements are attained, the system s limitations are known, and the performance level reached is considered acceptable, one has planned for capacity. 2.5 Capacity Planning The issues of sizing and capacity planning are gaining in importance as the complexity and size of modern e-business applications increase [8]. The process of determining what type of hardware and software configuration is required to meet application needs adequately is called capacity planning. Capacity planning is not an exact science. Every application is different and every userbehaviour is different. When talking about capacity planning, one must consider a number of so called capacity planning factors: different factors influence how much capacity a given hardware configuration will need in order to support a given application and the servers and databases it uses. One should consider how each of these factors applies to a specific configuration and application. Understanding these factors and considering the requirements of the given application will aid generating capacity planning requirements [3]. The following questions, which system developers are often faced with are the main sizing and capacity planning issues: What are the maximal load levels that the system can handle? What would the average response time, throughput and CPU utilisation be for a particular workload? How much resources (servers, CPU, memory) would be required to meet the Service Level Agreements (SLAs)? Service Level Agreements form a static, manually defined contract that specifies what service is performed during which period at what cost by interconnected Internet Service Providers (ISPs) [9]. 22

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