effektives consulting - performance engineering

Effektives Consulting

PERFORMANCE ENGINEERING (PE) PORTFOLIO

Dedicating kitchen experiences to quality, optimization and innovation across Domains and IT Environments ...

New Trends - Augmented Reality Applications

CLOUD BASED ECOMMERCE FEED RECOMMENDATIONS

ZERO TOUCH DEPLOYMENT ( CA – NALIO/Puppet PLATFORM)

User Experience and Web Performance Management (Web/Mobile and Offline Channels)

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EFFEKTIVES PE COVERAGE User Experience and Web Performance Management

(Web/Mobile and Offline Channels) Cloud Based Commerce Feed Recommendations Zero Touch Deployments New Trends for Better Conversions – Augmented Reality

Apps

* We shall focus on Web Performance Management in this presentation which will cover approach towards Infrastructure Capacity Planning, Performance Testing Approach, Profiling and Reporting Areas. *


Web Performance Management– Key Aspects Infrastructure Capacity Planning Performance Testing Environment and Approach Profiling of Apps Reporting


Infrastructure Capacity Planning For capacity-planning-type tests, goal is to show how far a given application can scale under a

specific set of circumstances. Reproducibility is not as important here as in benchmark testing because there will often be a randomness factor in the testing. This is introduced to try to simulate a more customer-like or real-world application with a real user load. Often the specific goal is to find out how many concurrent users the system can support below a certain server response time.

The next thing you need to know is what the think-time or time between requests for each user will be. This is critical because the lower the think-time, the fewer concurrent users the system will be able to support. For example, a system that has users with a 1-second think-time will probably be able to support only a few hundred concurrently. However, a system with a think-time of 30 seconds will be able to support tens of thousands (given that the hardware and application are the same). In the real world, it is often difficult to determine exactly what the think-time of the users is.

This is where randomization comes into play. If you know your average user has a think-time of 5 seconds give or take 20 percent, then when you design your load test, ensure that there is 5 seconds +/- 20 percent between every click. Additionally, the notion of "pacing" can be used to introduce more randomness into your load scenario. It works like this: After a virtual user has completed one full set of requests, that user pauses for either a set period of time or a small, randomized period of time (say, 2 seconds +/- 25 percent), and then continues on with the next full set of requests. Combining these two methods of randomization into the test run should provide more of a real-world-like scenario.


Performance Testing – Limitations in On-Premise Performance Testing ApproachLimitations of ON-PREMISE

Performance Testing Approach Test environment set up Hardware and software

procurement Maintenance Variation in load generators Shared between teams Simulating realistic tests Unexpected surge in demand


Cloud Based Performance Testing Approach

Test Environment Setup – Automatic provisioning of Load Generators

Affordable – Pay as you go model Scalability and Elasticity – On Demand Load

Generation Tests entire delivery chain Simulate multiple geographies Realistic network conditions Anytime, anywhere access Challenges

Data security Testing intranet applications Repeatability of tests Identifying bottlenecks and debugging

capabilities Application monitoring

Performance Testing – Two Staged Hybrid Process A two-stage process for load testing enables engineers to employ internal and cloud

testing in the situations for which they are most effective and appropriate. In the first stage of the process you conduct internal tests with a medium load to quickly identify and resolve preliminary performance issues. Then, increase the load incrementally with one or more load generators in the test lab.

After the performance has been validated internally, proceed to the second stage, cloud-based load testing, for large scale tests that validate the entire delivery chain of the application.

o This hybrid approach addresses the key challenges facing organizationso It enables teams to isolate problems -The source of any performance issue identified in the first stage is

clearly within the firewall (because no other systems are involved in the test). It's easier to pinpoint and fix internal problems when they are not being compounded by other issues that originate outside the firewall.

o It enables earlier testing - With the two-stage process, you don't have to wait for the application to be deployed and accessible from the Internet to test it. You can test internally earlier in the application lifecycle, when defects are easier and less expensive to fix.

o It enables reproducible tests -With internal testing you have much more control over the environment, so you can precisely measure the effect of code or configuration changes on application performance.

o It provides a better understanding of each major area of the delivery chain - You can compare the results of the same test scenario run internally and from the cloud, to get a clearer picture of how the application server and network infrastructure contribute to overall response times.

o It lowers costs - Cloud testing is based on a pay-per-use model. When you can test internally on hardware you already have, you can reduce the amount of testing that you need to perform from the cloud and cut costs that attempt to rely on testing from the cloud only:


Performance Testing – Two Staged Hybrid Process


CLOUD

ON PREMISE

Recorder Controller

Test Scripts Monitoring

Web Server

APP Server

DB Server

Commands Load Generation

Dedicating kitchen experiences to quality, optimization and innovation across Domains and IT Environments ...


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effektives consulting - performance engineering

Technology

cloud testing

cloudbased load testing

earlier testing

benchmark testing

real user load

load scenario

load generatorsscalability

medium load