PAC 2020 Santorin - Gopalkrishnan Yadav

In this presentation I will speak how are the SRE and DevOps, what is a reliability. Also about the reliability approach in Competitive Gaming in Wargaming and show a few cases.

The document discusses two immutable rules for observability: 1. Observability solutions should use all available data to avoid blind spots and issues with sampling data. 2. Observability solutions should operate at the speed and resolution of the applications and infrastructure being monitored to avoid losing precision or missing ephemeral events. It notes challenges with cloud infrastructure like microservices creating complex interactions, and failures not repeating exactly. Observability is presented as a solution to aid in detecting, investigating, and resolving unknown issues.

Join us to learn how to tune your web performance by combining synthetic, real-user, and competitive benchmarking metrics to give you the most complete dataset needed to optimize your site – and beat your competitors. You will learn: -Choosing the right tool for the job -Using competitive benchmarking data -Mine key performance analytics that matter -Putting performance in the context of your business

The document discusses three major problems in verification: specifying properties to check, specifying the environment, and computational complexity. It then presents several approaches to addressing these problems, including using coverage metrics tailored to detection ability, sequential equivalence checking to avoid testbenches, and "perspective-based verification" using minimal abstract models focused on specific property classes. This allows verification earlier in design when changes are more tractable and catches bugs before implementation.

Penske, a $26 billion transportation company, needed a better system to collect inspection data from its 700+ locations to identify operational issues and opportunities for improvement. The previous paper-based system took up to two weeks to provide information to management. Penske implemented an inspection management software called ECAT to digitally collect real-time data multiple times per day from 1,000+ employees. This new system reduced the audit process from 10 hours to 2 hours and provided instant data analysis to help Penske standardize processes and improve operations.

These are the slides of my JavaOne presentation. The abstract goes like this: How do companies developing business-critical Java enterprise Web applications increase releases from 40 to 300 per year and still remain confident about a spike of 1,800 percent in traffic during key events such as Super Bowl Sunday or Cyber Monday? It takes a fundamental change in culture. Although DevOps is often seen as a mechanism for taming the chaos, adopting an agile methodology across all teams is only the first step. This session explores best practices for continuous delivery with higher quality for improving collaboration between teams by consolidating tools and for reducing overhead to fix issues. It shows how to build a performance-focused culture with tools such as Hudson, Jenkins, Chef, Puppet, Selenium, and Compuware APM/dynaTrace

The FDA is advising use of data standards as early as possible in the study lifecycle. As a result, Data Management centers are using the Study Data Tabulation Model (SDTM) to drive operations from First Patient In till Database Lock. Many tools on the market allow for the creation of SDTM datasets via intuitive user interfaces. However, targeted tools are needed to manage nightly jobs taking care of data source downloads (eCRF, ePRO, Lab, etc), data uploads in a staging database, converting to SDTM and running edit checks before the Clinical Data Manager arrives in the morning.

The document proposes a replicated Siamese LSTM model for semantic textual similarity (STS) and information retrieval (IR) in an industrial diagnostic ticketing system. The system aims to retrieve relevant solutions from a knowledge base of tickets given a query. However, the text pairs in the system are often asymmetric in length and content. The proposed model addresses this by learning complementary representations of text pairs in a highly structured latent space using a replicated Siamese LSTM architecture and multi-channel Manhattan metric. It aims to capture similarity at both coarse-grained topic and fine-grained semantic levels to better handle asymmetric texts. The model is evaluated on STS and IR tasks for the industrial ticketing system.

This presentation was given at StarWest 2013 in Anaheim, CA and also broadcasted through the Virtual Conference. It shows how important it is to focus on performance throughout continuous delivery in order to avoid the most common performance problem patterns that still cause applications to crash and engineers spending their weekends and nights in a firefighting/war room situation

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

Real-time Anomaly Detection for Real-time Data Needs: Much of the world’s data is becoming streaming, time-series data, where anomalies give significant information in often-critical situations. Examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. Are there algorithms up for the challenge? Which are the most capable? The Numenta Anomaly Detection Benchmark (NAB) attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. The perfect detector would detect all anomalies as soon as possible, trigger no false alarms, work with real-world time-series data across a variety of domains, and automatically adapt to changing statistics. These characteristics are formalized in NAB, using a custom scoring algorithm to evaluate the detectors on a benchmark dataset with labeled, real-world time-series data. We present these components, and describe the end-to-end scoring process. We give results and analyses for several algorithms to illustrate NAB in action. The goal for NAB is to provide a standard, open-source framework for which we can compare and evaluate different algorithms for detecting anomalies in streaming data.

Semiconductor test engineering is the process of screening semiconductor devices to remove defective parts before shipment. This is done through testing to detect defects rather than prove the devices work as intended. The goal is to ensure high quality by catching manufacturing defects. If untested devices were shipped, many faulty ones could reach customers. Test engineering develops programs and hardware to efficiently test large volumes of devices in parallel while subjecting them to stress conditions to reveal marginal defects. It is important for achieving high yield and low cost.

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 the evolution from performance testing to performance engineering. Performance engineering is a proactive, shift-left approach that includes systematic techniques and activities in each sprint to meet performance needs. It focuses on design principles, architecture, and detecting bottlenecks early. Performance engineering requires skills in application diagnosis, infrastructure optimization, threading, concurrency, databases, and networks. It aims to deliver fast, efficient systems through a culture where performance is a shared responsibility. Continuous performance testing is important for technical agility and reducing business risks.

This document summarizes Matt Tesauro's presentation "Taking AppSec to 11" given at Bsidess Austin 2016. The presentation discusses implementing application security (AppSec) pipelines to improve workflows and optimize critical resources like AppSec personnel. Key points include automating repetitive tasks, driving consistency, increasing visibility and metrics, and reducing friction between development and AppSec teams. An AppSec pipeline provides a reusable and consistent process for security activities to follow through intake, testing, and reporting stages. The goal is to optimize people's time spent on customization and analysis rather than setup and configuration.