TASSQ Dinner Meeting Performance & Automation Testing of Cloud Solutions. March 31 st 2015

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1 TASSQ Dinner Meeting Performance & Automation Testing of Cloud Solutions March 31 st 2015

2 Brief History Of Cloud Computing Even though the cloud has only just recently hit its stride and began to expand the field for data mining and collecting, the concept of computing via a universal database is decades old In the 1950s, "time-sharing" what is now considered the underlying concept of cloud computing was used in academia and large corporations. Several clients needed to access information on separate terminals, but the mainframe technology was costly. To save money, they needed to find a way for multiple users to share CPU time. From there, it was just a hop, step and a jump away to the cloud of today. In 1969, J.C.R. Licklider introduced the idea for an "intergalactic computer network." Licklider developed ARPANET (Advanced Research Projects Agency Network) and hoped one day everyone could access data and programs from anywhere. John McCarthy the father of the term artificial intelligence built upon the cloud idea, and the first uses of cloud data were for censuses and financial transactions (1970 s). In 1997, professor Ramnath Chellappa was the first to use the term cloud computing. Then, in 1999, Salesforce.com became the first site to deliver applications and software over the Internet. Amazon stepped in, in 2002, with "Web Services" (AWS) providing an advanced system of cloud services from storage to computation. And in 2006, Amazon introduced the Elastic Compute cloud (EC2) as a commercial web service. The EC2 let small companies rent computers on which they could run their own computer applications. 1

3 Brief History Of Cloud Computing Salesforce.com expanded its efforts in 2007, with Force.com. The developer-based platform let companies build and run all of their business apps and website through the cloud But it wasn't until 2008, when Google and Microsoft entered the playing field, that cloud use really became a widespread effort to bring the technology mainstream. The Google App Engine brought low-cost computing and storage services, popularizing the concept; by 2009, Google Apps allowed people to store documents within the cloud. Microsoft followed suit withwindows Azure, solidifying the cloud as a market that the tech giants would be expanding and competing in. Salesforce.com continued its growth in 2010 with Database.com again made for developers and the cloud allowed computing services to be used on any device or platform in any programming language. Apple put a stake in the field by developing the icloud, allowing users to sync photos, apps, music and documents across a string of devices. What does the cloud look like today? 2

4 3 Cloud Computing Today

5 Taxonomy and Scope of Discussion Cloud computing is a broad term that encompasses multiple Deployment Models and Types of Services. Deployment Models Examples Types of Services SaaS Software as a Service PaaS Platform as a Service Private Hybrid Public IaaS Infrastructure as a Service SaaS introduces challenges, including: integration with identity systems for single-sign-on data integration with on premises systems or other SaaS applications variable networking performance On-premises application From the deployment channel perspective, coexisting with the Hybrid Cloud, there is often integration of cloud application with one or more on-premises applications in the enterprise IT landscape. Cloud computing in this presentation refers to the SaaS Model, focusing on Hybrid Cloud services 4

6 Taxonomy and Scope of POV Discussion Performance testing determines how a system performs under particular workloads. This presentation refers to the following types of performance tests. Load/ Volume Test Can the system handle a normal load? Stress Test Can the system handle the load of a peak season (e.g. sale transactions on Black Friday, Boxing Day) Endurance Test/ Longevity Test Will it stay up? And how long will it stay up? Break Test How long will it stay up before breaking apart or performance degrades to an unacceptable level? Scalability Test Can the system handle more users / transactions and grow with the enterprise? 5

7 Executive Summary Cloud computing is growing to be of strategic importance in the enterprise, inevitably part of the solution delivered is no longer on-premises, adding a layer of complexity. It is not uncommon to have cloud solutions integrated with on-premises applications in an IT project these days. DIFFERENCE Is performance testing of cloud solutions fundamentally different from that of on-premises applications? Key Questions BEST PRACTICES What are the best practices that works for performance testing of cloud and on-premises solutions? 6

8 Difference Is performance testing of cloud solutions immune to key challenges typical in any on-premises solutions? 1 Challenge Late initiation of performance test strategy. Typical Issue Typically performance testing is executed towards the end of the project before go-live. Hence, project leadership often initiates the formulation of performance test strategy late in the game. Impact / Risk to Project Delivery One of the key risks that performance test aims to uncover is the architectural / solution design flaw early in the project so that these issues can be remediated before it is too late. 7

9 Difference Is performance testing of cloud solutions immune to key challenges typical in any on-premises solutions? 2 Challenge Poorly defined performance requirements. Typical Issue Typical quality issue in performance requirements definition include statistically unquantifiable, unrealistic, vague and inaccurate definitions. Impact / Risk to Project Delivery Having well-defined performance requirements is a key success factor to assure we are building the right solution. Without well defined performance requirements we introduce risks that might lead to us not building the right solution. 8

10 Difference Is performance testing of cloud solutions immune to key challenges typical in any on-premises solutions? 3 Challenge Incomplete performance requirements. Typical Issue There are 3 independent categories of performance requirements that constitutes their completeness: Response/ Processing times - define how fast requests would be processed in interactive online transactions or batch jobs. Thirty minutes may be excellent for a big batch job, but unacceptable for loading a web page. Throughput - the rate at which incoming requests are completed. It defines load on the system and is measured in operations per time unit (transactions/sec or number of adjudicated claims/hr. Concurrency - the number of users or threads working simultaneously. Very often, one or more categories of performance requirements are missing. Impact / Risk to Project Delivery Missing requirements is the root cause of missing test scenarios. Missing performance scenarios introduce negative user experience from the performance perspective. 9

11 Difference Is performance testing of cloud solutions immune to key challenges typical in any on-premises solutions? 4 Challenge Lack of sound data seeding strategy in the overall performance test strategy. Typical Issue Due to increased reliance on having someone else providing the right volume of data to execute performance tests, little consideration is given to developing a sound data seeding strategy as part of the performance test. For projects involving data migration from a legacy application to a cloud based application, we often wait for the availability of converted data to begin conducting the performance test, which is often too late in the game. Impact / Risk to Project Delivery Without the right mix of data (types and distribution) and appropriate volume, performance test results become non-reliable or trustworthy. 10

12 Difference Is performance testing of cloud solutions immune to key challenges typical in any on-premises solutions? 5 Challenge Lack of investment in Application Performance Management (APM) tools to monitor and perform proactive diagnostics in performance tests. Typical Issue Application Performance Management (APM) tools for monitoring and diagnosis are often not available for use in performance testing. Impact / Risk to Project Delivery Without the proper APM tools available to collect technical metrics across tiers of the application architecture, development teams will take significant level effort to troubleshoot, diagnose and pin-point root cause for performance defects, causing lengthy delays in the project timeline. 11

13 The Answer Is performance testing of cloud solutions immune to key challenges typical in any on-premises solutions? No definitely NOT Performance Testing of cloud solutions is NOT immune to the typical challenges in any solutions traditionally hosted on-premises. Key success factors for performance testing of cloud solutions include: Early initiation of performance test strategy Having a sound data seeding strategy in the overall performance test strategy Well defined and complete performance requirements Investment in APM tools 12

14 Best Practices - The big picture With the agility and flexibility in the development and deployment of cloud application it s a natural fit to apply agile development methodology in the SDLC Planning Release Planning Preparation Execution Waterfall The traditional three-step testing process in the waterfall model will resemble the following under Agile Development methodology / framework whereby early feedback is built-into the process. Hence early performance testing is a natural fit for cloud solutions. Sprint 1 Planning Preparation Execution Sprint2 Planning Preparation Execution Preparation Execution Sprint N Planning Agile 13

15 Best Practices 1. Conduct early performance testing A Start developing performance testing strategy early in the project. D Ensure you have access to service layer testing tool that meet your project need. B C Conduct early performance testing iteratively or in short sprints to gather early feedback. Introduce performance testing at service layer, with special attention to change control in the service interface. E Engagement and Collaboration with the architecture team and performance testing team is a key success factor start early so as to strategize with the teams and gain support. Sprint 1 Planning Preparation Execution Sprint2 Planning Preparation Execution Preparation Execution Sprint N Planning Agile 14 Early Performance Testing

16 Best Practices 2. Measure GUI and network performance metrics A Ensure end-to-end user experience including GUI and network performance metrics are included. Partially outsource performance testing to the cloud solution provider whenever practical and/or feasible. The three components of performance metrics which include the server, network, and GUI need to be measured separately to compare against the end-to-end user experience. Most cloud vendors provide server performance testing services. Very few provide GUI and network performance metrics as these items are considered as out of scope items. Measurement provided by vendor as relevant performance metrics 15

17 Best Practices Network Latency and the TCP / IP Stack A TCP / IP STACK It is important to remember that Latency directly effects the USER EXPERIENCE via LOAD TIME For every 20 millisecond improvement in latency, we have a linear improvement in page loading times 16

18 Best Practices 3. Tool Selection A Validate new tools with Proof of Concepts (POC s) during the formulation stage of the strategy. Incorporate tool selection with POCs in performance test strategy make sure the tool works for you there is not a one size fits all solution. Testing Activity Quality / Test management Test automation (functional and reggression) Service virtualization Load / Performance testing Description Tools for managing testing strategy, plans, test cases. scripts, testing processes, exploratory testing, defect management, status reporting, executive dashboard Frameworks or tools for automating functional tests (GUI and API Tests*) Frameworks or tools for integrating components not available for at the time of testing Tools for testing load and performance Integration with other tools Performance Test automation Service virtualization Performance X X Test automation X X Service virtualization X X 17 * API Test examples Web Services (SOAP and REST), Databases, FTP, Message Queue

19 Best Practices 4. Considerations for selecting the right tools A Level of programming effort required in the tool to create the performance test scripts - some are easier than others. B C D Validate if service virtualization can eliminate or alleviate the bottleneck of integration of critical interfaces required in the performance scenario. Ability to support a variety of protocol and technologies needed in your projects ranges from mainframe to middleware to intelligent web 2.0 technologies. Consider budget, support, learning curve Examples Open Source Commercial Edition Select the right tools Cloud On-Premises - JMeter - HP Performance Center - Blazemeter - JMeter - HP LoadRunner - Microsoft Visual Studio 18

20 Best Practices 5. Automate injection of test data A Ensure the feasibility of automating the injection of large volume of test data. B Automate the data seeding required in the performance test, that means that the performance testing tool and the data seeding tool are related. The interoperability of the data seeding automation tool and the performance testing tool should be taken into consideration. 19

21 Best Practices 6. Reuse Existing Automated Scripts A If possible, try and use existing automation scripts to mimic user FLOWS. B Here we are passing data VIA JSON Use the SERVICES library Services.StartTransaction Services.EndTransaction 20

22 Best Practices 6. Integrate full performance testing lifecycle Test planning Define business requirements for application performance and asses the impact of architecture, design and security on performance. Test preparation Start performance testing early as part of an agile process. Execute end-to-end testing in complex environments. Coordinate testing with software changes, configuration management and version control. Feedback Test execution Translate user requirements into load testing objectives. Create virtual user scripts. Define and configure user behavior. Understand network impact within application. Continuous testing Test analysis Iteratively employ static/dynamic analysis for software quality analysis and measurement and security analysis. Monitoring in Production Monitor applications and end-user experience and perform root cause analysis. 21

23 Contact Information Brian Grieve CTO, Cloudpipe Inc. (416) mobile Edwin Chan, CSTE, PMP QA Manager, Deloitte Consulting (416) mobile 22

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