PAC 2019 virtual Federico Toledo

Prometheus is a next-generation monitoring system. It lets you see you not just what your systems look like from the outside, but also gives visibility into the internals and business aspects of your systems. This allows everyone to benefit, including both operations and developers. This talk will look at the concepts behind monitoring with Prometheus, how it's designed, why it's suitable for Cloud Native environments and how you can get involved.

This document provides an overview and analysis of Microsoft BizTalk Human Workflow Services (HWS). It includes sections on vocabulary, common patterns, tasks, and features of HWS. Confidentiality of the document is stressed. The document is intended as a reference for someone looking to become an expert in HWS.

The document summarizes research on assessing the scalability of microservice architectures. It discusses how microservices introduce challenges for monitoring performance and reliability due to their decentralized nature. The researcher aims to develop approaches to identify bottlenecks, anomalies, and anti-patterns in microservices. The document outlines a framework called PPTAM that generates load tests to analyze the performance of different architectural configurations and identifies the most scalable option based on success rates under various workloads. Ongoing work also looks to recognize common anti-patterns that can degrade microservice performance.

The workshop focused on improving testing processes for data intensive environments like business intelligence and data warehousing systems. Participants discussed challenges with the traditional waterfall model and benefits of agile/Scrum approaches. Common problems identified included unstable test data, long test runtimes, lack of automation, and pressure on testers. Potential solutions proposed applying agile practices like continuous integration and regression testing, automating test data generation, deployments, and output validation to make testing more efficient and independent of production systems. The workshop aimed to provide insights into both problems with current testing approaches and actions that could be taken to address them.

Is your environment acting the way you intended it to be, as in do your users see what you wanted them to see?

The Army Research Laboratory is developing a next-generation ballistic vulnerability and lethality modeling system called MUVES 3 using cloud computing. MUVES 3 has a service-oriented architecture built on Java and deployed using the NetBeans IDE. Testing MUVES 3 on Amazon EC2 through Elastic Grid's cloud management platform allows scaling to hundreds of computers for integration testing while avoiding security issues of a public cloud. Further steps include expanding metrics collection and using NetBeans as a client for cloud visualization.

The document discusses strategies for effective test automation. It emphasizes taking a risk-based approach to prioritize what to automate based on factors like frequency of use, complexity of setup, and business impact. The document outlines approaches for test automation frameworks, coding standards, and addressing common challenges like technical debt. It provides examples of metrics to measure the effectiveness of test automation efforts.

One of the most common performance issues in serverless architectures is elevated latencies from external services, such as DynamoDB, ElasticSearch or Stripe. In this webinar, we will show you how to quickly identify and debug these problems, and some best practices for dealing with poor performing 3rd party services.

The document discusses why software developers should use FlexUnit, an automated unit testing framework for Flex and ActionScript projects. It notes that developers spend 80% of their time debugging code and that errors found later in the development process can cost 100x more to fix than early errors. FlexUnit allows developers to automate unit tests so that tests can be run continually, finding errors sooner when they are cheaper to fix. Writing automated tests also encourages developers to write better structured, more testable and maintainable code. FlexUnit provides a testing architecture and APIs to facilitate automated unit and integration testing as well as different test runners and listeners to output test results.

This document discusses virtualizing tier 1 applications. It begins by showing how virtualization adoption has increased significantly for mission critical applications. It then discusses specific steps and considerations for virtualizing tier 1 applications, including: 1. Ensuring the platform (hardware, virtualization software, etc.) can adequately support the application. 2. Ensuring the people and processes are in place to design, implement, operate and troubleshoot the virtualized application. This includes discussing skills, support models, change management and monitoring. 3. Reviewing the application itself and existing reference architectures to understand virtualization best practices and sizing for that application. The goal is to virtualize at the application layer rather than physical server layer.

The document provides an overview of key areas to review for production readiness including architecture design, monitoring, logging, documentation, alerting, service level agreements, expected throughput, testing, and deployment strategy. It summarizes best practices and considerations for each area such as using circuit breakers in monitoring, consistent logging formats, storing documentation near code, automating level 1 operations, and strategies for testing, deployments, and managing error budgets.

Software performance testing is an important activity to ensure quality in continuous software development environments. Current performance testing approaches are mostly based on scripting languages and framework where users implement, in a procedural way, the performance tests they want to issue to the system under test. However, existing solutions lack support for explicitly declaring the performance test goals and intents. Thus, while it is possible to express how to execute a performance test, its purpose and applicability context remain implicitly described. In this work, we propose a declarative domain specific language (DSL) for software performance testing and a model-driven framework that can be programmed using the mentioned language and drive the end-to-end process of executing performance tests. Users of the DSL and the framework can specify their performance intents by relying on a powerful goal-oriented language, where standard (e.g., load tests) and more advanced (e.g., stability boundary detection and configuration tests) performance tests can be specified starting from templates. The DSL and the framework have been designed to be integrated into a continuous software development process and validated through extensive use cases that illustrate the expressiveness of the goal-oriented language, and the powerful control it enables on the end-to-end performance test execution to determine how to reach the declared intent. My talk from The 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018). Cite us: https://dl.acm.org/citation.cfm?id=3184417

The document provides an overview and agenda for a LoadRunner training course. It introduces LoadRunner and its components, including VuGen for recording scripts, the Controller for managing tests, and Analysis for reporting. It discusses the LoadRunner workflow and how it emulates real users to load test applications. Key topics covered include virtual users (Vusers), scripts, scenarios, protocols, and runtime settings.

Slides from the July 31st, 2013 webinar "Preparing for Enterprise Continuous Delivery - 5 Critical Steps" by XebiaLabs

This document summarizes a presentation about stateful patterns in Azure Functions using Durable Functions. The presentation introduces Durable Functions as a way to add state management to Azure Functions. It discusses common stateful patterns like function chaining, fan-in/fan-out, and human interaction and how Durable Functions addresses issues with implementing these patterns with regular stateless functions through orchestrations, activities, and entities. The presentation concludes by emphasizing how Durable Functions solves concurrency issues but may not always be the right choice depending on requirements around latency.

In part 3 of the materials from the July 8 AWS RoadShow in Manchester we discuss best practices for getting started with AWS and the next steps you can take to learn more about AWS and begin to use it to run your applications and other IT workloads.

In part 2 of the materials from the July 10 AWS RoadShow in Bristol we discuss best practices for getting started with AWS and the next steps you can take to learn more about AWS and begin to use it to run your applications and other IT workloads.

The document discusses continuous performance optimization of Java applications. It proposes adding an optimization step to the continuous integration pipeline to evolve performance testing beyond just finding regressions. This would allow configurations to be adapted to new application features and releases to find performance improvements. The approach is demonstrated on a flight search microservice, where different garbage collection algorithms and configuration parameters are evaluated to optimize throughput, response times, memory usage and stability under increasing load.

The document discusses the concept of observability in performance engineering and its importance for understanding application performance. It defines observability as watching application behavior using response metrics and resource utilization metrics to understand the digital user experience. The document provides examples of integrating load testing tools with application performance monitoring tools to actively monitor applications in production and observe performance across releases. It emphasizes the need to analyze raw metrics from multiple perspectives to gain useful insights.

This document discusses measuring and addressing CPU throttling in containerized environments. It describes how CPU limits work at the kernel level and how to measure throttling using cgroup metrics. The author proposes adding 1.3 times the maximum throttled CPUs to the container's CPU limit to eliminate throttling. A case study shows this approach reduced response times and garbage collection pauses. The document also discusses how the JVM can increase demand as CPU limits increase and the importance of tailoring limits to workloads.

This document discusses using Keptn to automate service level indicator (SLI) evaluation and performance validation with service level objectives (SLOs). It describes two use cases: 1) automating SLI evaluation over a timeframe, and 2) integrating performance validation as a self-service capability. The document outlines how Keptn works underneath, including defining SLIs and SLOs in YAML and scoring SLIs against SLO criteria. It demonstrates integrating Keptn with existing pipelines and monitoring tools. Finally, it discusses options for installing only the Keptn quality gate functionality or the full Keptn platform.

The document discusses how to automate performance testing in DevOps. It outlines an automated analysis workflow involving defining metrics, comparing metrics to thresholds and baselines, pattern analysis, and test results. It also discusses script automation, reducing false positives, and integrating different types of performance tests like load, stress, and spike tests. The goal is to automate performance testing to support the rapid delivery cycles of DevOps.

This document discusses monitoring the performance of Azure data pipelines. It recommends using Azure Log Analytics to monitor pipelines near real-time, as traditional testing tools don't support log analytics. It provides steps to configure a Log Analytics workspace and enable diagnostics logging. Sample queries are shown to monitor metrics like pipeline durations, activity times and throughput. Monitoring pipelines helps assess performance, stability and confidence in data processing.

This document discusses common performance myths and challenges related to cloud migrations, SaaS deployments, and cloud native applications. It provides approaches to assess performance for each scenario. Some key points covered include: - Common myths like unlimited scaling without effort, cost savings from cloud alone, and SaaS vendors handling all performance. - Challenges for migrations include capacity planning, auto-scaling issues, and latency. For SaaS, risks include customization impacts and integration issues. Cloud native challenges are distributed architectures and configuration problems. - The document recommends approaches like baseline benchmarking, resilience testing, and 360-degree validation across user, cloud, data center and external layers to overcome myths and address challenges.

Joerek van Gaalen discussed his experience conducting large-scale performance tests, including a test with 2 million virtual users. He explained how to simulate that level of load using 800 load generators across different cloud providers. He emphasized optimizing tests by tuning scripts, controllers, and agents to minimize resources. Van Gaalen also stressed the importance of making tests realistic by mimicking production usage patterns and balancing load. Specific issues like CDN performance limitations and uneven load distribution were also addressed.

Ankur Jain presented on using the User Timing API to measure user perceived performance of single-page applications without an APM tool. The User Timing API allows developers to mark milestones and measure the time between them to understand performance. While it requires code changes, it provides accurate, real-user monitoring of applications across browsers. Some limitations are that it requires knowledge of the application's user flow and code access to implement the markers.

The document summarizes a presentation given at the Performance Advisory Council in Santorini, Greece in February 2020. The presentation advocated for using cloud-based performance engineering tools for their ease of use, ability to automatically correlate data, and to scale testing on demand. It cautioned that adopting new tools requires maintaining the same performance testing culture to avoid generating misleading or inaccurate results.

The document discusses the flawed approach of "racing performance" without proper preparation. It argues that jumping straight into performance testing without checking components like tires, fuel levels, and ensuring the system is functioning properly leads to "fatal consequences" like crashes. Some reasons this approach still occurs include a lack of performance culture, confusion between performance and load testing, and viewing all problems as requiring load automation. The document promotes better approaches like verifying components, defining standards, stress testing parts individually and together before integration, using meters to monitor system functions, conducting lab tests, and test driving the system before attempting high-stakes races.

This document discusses automating performance testing pipelines. It covers value stream mapping to identify tasks that can be removed, simplified or automated. Automating testing provides benefits like reduced time, allowing specialists to focus on higher-value work, and empowering others to run tests. The document demonstrates automating a JMeter load test, providing tips like using JMeter projects and scripts. It notes that significant time savings are possible from automating not just test execution but test development as well through techniques like UI automation.

This document provides an overview of a presentation on the economic impacts of performance testing practices. It discusses two examples: 1) Ford's Pinto car that had a design flaw causing fuel tanks to explode in rear-end collisions. Analyzing the options of fixing the flaw vs paying fines, fixing it would have cost $137 million but saved much higher future costs. 2) An example project prioritizing what processes to automate for performance testing. Following the Pareto principle, automating the top 20% of processes that generate 80% of the load yields far better results than automating all processes, saving significant time and resources. The presentation emphasizes how taking a long-term view of costs and following

This document discusses web performance optimization techniques. It is a summary of rules for web performance by Mark Tomlinson, who has 27 years of experience in performance. Some of the key techniques discussed include reducing HTTP requests, optimizing file compression, minimizing code, improving web font and image performance, prefetching resources, avoiding unnecessary redirects, and optimizing infrastructure and databases. The document emphasizes measuring performance through load testing and monitoring to identify bottlenecks.

This document discusses using R for exploratory result analysis of load testing data from JMeter. It provides an introduction to R and highlights benefits like it being programming based, developed for data analysis, supporting exploratory analysis, and including visualization libraries. It also gives examples of base R functions for data manipulation and visualization including aggregate, subset, ifelse, scatter plots, and using color. Finally, it discusses using R to create interactive dashboards for reporting load testing results.

The document discusses observability in systems and discusses how logs, metrics, and traces can provide context and help observe internal system states through external outputs. It notes there are trade-offs to consider in collecting, storing, detecting, alerting, visualizing, and configuring observability data. The document also briefly touches on using AI in observability and the human element.

- Automating performance tests through continuous integration can provide direct feedback on performance changes after code releases and infrastructure changes. It allows performance issues to be detected and addressed earlier. - Key best practices include starting with a single important test scenario, focusing on robustness over realism, visualizing trend data over time, and analyzing results to update thresholds and catch regressions. - The goal is to continuously monitor performance through the pipeline and in production to better understand impacts of changes and flag any performance issues for further investigation. Automated tests complement but do not replace thorough acceptance testing.

This document discusses using machine learning algorithms for predictive performance modeling of IT systems. It explains that ML can be used to predict quality of service metrics like response time and server utilization for different conditions, such as increased user load or hardware configurations, based on past production and test data. This helps with data-driven decision making for hardware procurement and effective utilization, and can reduce the cost and time of performance testing by complementing benchmarking and application tuning. The key is having sufficient historical data to train accurate models, with various techniques available along a cost-accuracy spectrum, from linear projections to simulation to machine learning.

This document describes a performance automation solution using load testing scripts to continuously monitor application performance. The solution uses scripts to test functionality, availability, response times, and end-to-end workflows. Load testing engines run the scripts on a periodic schedule and store results. An alerting system analyzes results and sends alerts if response times exceed thresholds or tests fail to run. The system is containerized using Docker for scalability. Potential customers include project managers who need regression testing, monitoring of production applications, and emergency alerts about degradations or failures.

The document provides guidance on developing reliable load test scripts and scenarios. It discusses test data requirements, parameterizing dynamic values, proper use of HTTP protocol versus GUI-level scripting, handling asynchronous requests, implementing think time and pacing, validating scripts, and best practices for load scenario configuration. The goal is to explore main points around scripting best practices, validating load scripts thoroughly, and configuration best practices to build effective performance tests.

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This study primarily aimed to determine the best practices of clothing businesses to use it as a foundation of strategic business advancements. Moreover, the frequency with which the business's best practices are tracked, which best practices are the most targeted of the apparel firms to be retained, and how does best practices can be used as strategic business advancement. The respondents of the study is the owners of clothing businesses in Talavera, Nueva Ecija. Data were collected and analyzed using a quantitative approach and utilizing a descriptive research design. Unveiling best practices of clothing businesses as a foundation for strategic business advancement through statistical analysis: frequency and percentage, and weighted means analyzing the data in terms of identifying the most to the least important performance indicators of the businesses among all of the variables. Based on the survey conducted on clothing businesses in Talavera, Nueva Ecija, several best practices emerge across different areas of business operations. These practices are categorized into three main sections, section one being the Business Profile and Legal Requirements, followed by the tracking of indicators in terms of Product, Place, Promotion, and Price, and Key Performance Indicators (KPIs) covering finance, marketing, production, technical, and distribution aspects. The research study delved into identifying the core best practices of clothing businesses, serving as a strategic guide for their advancement. Through meticulous analysis, several key findings emerged. Firstly, prioritizing product factors, such as maintaining optimal stock levels and maximizing customer satisfaction, was deemed essential for driving sales and fostering loyalty. Additionally, selecting the right store location was crucial for visibility and accessibility, directly impacting footfall and sales. Vigilance towards competitors and demographic shifts was highlighted as essential for maintaining relevance. Understanding the relationship between marketing spend and customer acquisition proved pivotal for optimizing budgets and achieving a higher ROI. Strategic analysis of profit margins across clothing items emerged as crucial for maximizing profitability and revenue. Creating a positive customer experience, investing in employee training, and implementing effective inventory management practices were also identified as critical success factors. In essence, these findings underscored the holistic approach needed for sustainable growth in the clothing business, emphasizing the importance of product management, marketing strategies, customer experience, and operational efficiency.