Large Scale Test Automation
•Download as PPTX, PDF•
0 likes•59 views
This document summarizes a presentation on secrets of successful test automation. It discusses scheduling automated tests to run every night for 10-20 minutes and limiting resources to 1GB RAM, no persistent storage, and 10MB network bandwidth. Standard reports on endpoints, response times, requests, and errors are shown. Measuring UI performance includes page load time with different networks, response traffic size, request counts, and centralized monitoring. Tools mentioned include Selenium Grid, Selenoid, and Grafana for scaling acceptance tests across browsers and versions.
Report
Share
Report
Share
Recommended
Web Real-time Communications
This presentation aggregates common approaches of real-time client-server communications provided by Web Standards. It focuses on comparison of different techniques like polling, comet, Web Sockets, Server-Sent Events.
New Relic_Heroku_Presentation_Dreamforce11
Summary deck of a presentation given at Dreamforce 11 by Brian Doll and Abhishek Goswami of New Relic
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
Streaming engines like Apache Flink are redefining ETL and data processing. Data can be extracted, transformed, filtered and written out in real-time with an ease matching that of batch processing. However the real challenge of matching the prowess of batch ETL remains in doing joins, in maintaining state and to have the data be paused or rested dynamically. Netflix has a microservices architecture. Different microservices serve and record different kind of user interactions with the product. Some of these live services generate millions of events per second, all carrying meaningful but often partial information. Things start to get exciting when we want to combine the events coming from one high-traffic microservice to another. Joining these raw events generates rich datasets that are used to train the machine learning models that serve Netflix recommendations. Historically we have done this joining of large volume data-sets in batch. However we asked ourselves if the data is being generated in real-time, why must it not be processed downstream in real time? Why wait a full day to get information from an event that was generated a few mins ago? In this talk, we will share how we solved a complex join of two high-volume event streams using Flink. We will talk about maintaining large state, fault tolerance of a stateful application and strategies for failure recovery.
Scalable Dynamic Data Consumption on the Web
The document discusses reducing server load for dynamic web data by moving continuous query evaluation from servers to clients. It proposes doing this through three steps: scalable data storage and publication, efficient data transmission using compression and caching, and continuous evaluation on clients. Several research questions are posed around how to combine publication of real-time and historical data to make it queryable efficiently while storing it in a way that allows efficient data transfer and enabling client-side query evaluation over both static and dynamic data. Hypotheses are made that new data can be stored and retrieved linearly based on amounts, and that server costs will be lower than alternatives with data transfer being the main factor influencing query times.
Moving RDF Stream Processing to the Client
Stream-processing SPARQL endpoints hosted on web servers are expensive due to an unknown number of clients, unbounded query complexity, and the server doing all the work while clients wait for results. Publishing dynamic data with Triple Pattern Fragments and making clients contribute more to the processing addresses this by annotating triples with timestamps, having clients re-evaluate queries as needed based on the timestamps, and designing the server interface to handle simple requests while putting most of the work on clients.
Modernizing Cloud and Hyperconverged Infrastructure monitoring
This document discusses monitoring various cloud infrastructure and applications using an end-to-end application performance monitoring solution. It covers monitoring metrics in AWS, GCP, Oracle Cloud Infrastructure, Hyperconverged infrastructure like Nutanix and Cisco UCS, Oracle Autonomous Database, and using trend analysis reports for forecasting and resource planning. Upcoming monitoring enhancements for additional cloud services on AWS, Azure, and GCP are also outlined through 2020.
Create seamless customer experiences
Learn how analyzing key website metrics that are related to user interactions will help you make insightful improvements. Understand how replaying individual customer transactions and analyzing every element of your webpage will help drill down to the root causes issues and create better content strategies respectively.
Deliver seamless digital experience
Learn how to monitor your website URLs and identify components that slow down your website performance.
Recommended
Web Real-time Communications
This presentation aggregates common approaches of real-time client-server communications provided by Web Standards. It focuses on comparison of different techniques like polling, comet, Web Sockets, Server-Sent Events.
New Relic_Heroku_Presentation_Dreamforce11
Summary deck of a presentation given at Dreamforce 11 by Brian Doll and Abhishek Goswami of New Relic
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
Streaming engines like Apache Flink are redefining ETL and data processing. Data can be extracted, transformed, filtered and written out in real-time with an ease matching that of batch processing. However the real challenge of matching the prowess of batch ETL remains in doing joins, in maintaining state and to have the data be paused or rested dynamically. Netflix has a microservices architecture. Different microservices serve and record different kind of user interactions with the product. Some of these live services generate millions of events per second, all carrying meaningful but often partial information. Things start to get exciting when we want to combine the events coming from one high-traffic microservice to another. Joining these raw events generates rich datasets that are used to train the machine learning models that serve Netflix recommendations. Historically we have done this joining of large volume data-sets in batch. However we asked ourselves if the data is being generated in real-time, why must it not be processed downstream in real time? Why wait a full day to get information from an event that was generated a few mins ago? In this talk, we will share how we solved a complex join of two high-volume event streams using Flink. We will talk about maintaining large state, fault tolerance of a stateful application and strategies for failure recovery.
Scalable Dynamic Data Consumption on the Web
The document discusses reducing server load for dynamic web data by moving continuous query evaluation from servers to clients. It proposes doing this through three steps: scalable data storage and publication, efficient data transmission using compression and caching, and continuous evaluation on clients. Several research questions are posed around how to combine publication of real-time and historical data to make it queryable efficiently while storing it in a way that allows efficient data transfer and enabling client-side query evaluation over both static and dynamic data. Hypotheses are made that new data can be stored and retrieved linearly based on amounts, and that server costs will be lower than alternatives with data transfer being the main factor influencing query times.
Moving RDF Stream Processing to the Client
Stream-processing SPARQL endpoints hosted on web servers are expensive due to an unknown number of clients, unbounded query complexity, and the server doing all the work while clients wait for results. Publishing dynamic data with Triple Pattern Fragments and making clients contribute more to the processing addresses this by annotating triples with timestamps, having clients re-evaluate queries as needed based on the timestamps, and designing the server interface to handle simple requests while putting most of the work on clients.
Modernizing Cloud and Hyperconverged Infrastructure monitoring
This document discusses monitoring various cloud infrastructure and applications using an end-to-end application performance monitoring solution. It covers monitoring metrics in AWS, GCP, Oracle Cloud Infrastructure, Hyperconverged infrastructure like Nutanix and Cisco UCS, Oracle Autonomous Database, and using trend analysis reports for forecasting and resource planning. Upcoming monitoring enhancements for additional cloud services on AWS, Azure, and GCP are also outlined through 2020.
Create seamless customer experiences
Learn how analyzing key website metrics that are related to user interactions will help you make insightful improvements. Understand how replaying individual customer transactions and analyzing every element of your webpage will help drill down to the root causes issues and create better content strategies respectively.
Deliver seamless digital experience
Learn how to monitor your website URLs and identify components that slow down your website performance.
Visibility-from web application interface to the database
Learn how to identify transactions that are slowing down your applications and how analyzing error traces can help you eliminate performance barriers.
SignalR Overview
SignalR is an ASP.NET library that allows real-time communication between web servers and clients. It uses various techniques like websockets, server-sent events, and long polling to provide a persistent connection. SignalR supports .NET, JavaScript, and mobile clients and can scale out to server farms using backplanes like SQL Server, Service Bus, and Redis. It includes hubs for calling methods between clients and servers and sending messages. Examples of SignalR in use include chat applications and games.
Continuous Self-Updating Query Results over Dynamic Linked Data
This document discusses continuously updating query results over dynamic linked data. It proposes moving continuous query evaluation from the server to the client to lower server load. Key points:
- Dynamic linked data streams, like sensor data, add new triples over time and typically query the current value.
- Existing approaches have servers continuously evaluate queries, causing low availability due to high load.
- The document proposes storing dynamic data efficiently, transmitting it to clients, and having clients evaluate queries to reduce server load.
- A preliminary test showed the proposed approach moves more load to clients but scales server load better than existing solutions that rely on continuous server-side query evaluation.
Flink Forward San Francisco 2019: Real-time Processing with Flink for Machine...
eal-time Processing with Flink for Machine Learning at Netflix
Machine learning plays a critical role in providing a great Netflix member experience. It is used to drive many parts of the site including video recommendations, search results ranking, and selection of artwork images. Providing high-fidelity, near real-time data is increasingly important for these machine learning pipelines, especially as multi-armed bandit and reinforcement learning techniques, in addition to more ""traditional"" supervised learning, become more prevalent. With access to this data, models are able to converge more quickly, features can be updated more frequently, and analysis can be done in a more timely manner.
In this talk, we will focus on the practical details of leveraging Flink to process trillions of events per day, work with the time dimension, and manage large and frequently-changing state. We will discuss different processing schemes and dataflows, scalability and resiliency challenges we tackled, operational considerations, and instrumentation we added for monitoring job health in production.
Consul scale
The document summarizes the growth of a Consul cluster from a few dozen agents to thousands of agents across multiple data centers. As the cluster scaled up, it began experiencing performance and stability issues like IO wait, slow key-value updates, and DNS query failures. The operators addressed these issues by upgrading Consul versions, improving monitoring, stabilizing join/leave operations, and using DNS caching. They also detailed their process for a successful large-scale Consul upgrade with minimal downtime.
Server side scalable web performance
This document discusses server-side scalable web performance. It introduces key performance indicators (KPIs) and discusses scaling techniques including using multiple servers and content delivery networks. It also covers organizational considerations like using specialist versus organizational teams and ensuring good boundaries between teams to minimize cognitive load. Performance metrics like time to first byte and time to interactive are explained. HTTP caching, HTTP/2, and edge computing are presented as ways to improve performance.
Building the right website monitoring strategy
This document discusses website monitoring strategies including tracking key metrics of web servers like Apache, IIS and Nginx; optimizing individual URLs for user experience; using synthetic monitoring to simulate web transactions; and detecting unauthorized content changes. It provides overviews of monitoring various web servers and their key performance indicators. It also describes optimizing the user experience by monitoring URL sequences, implementing real browser monitoring, and using web transaction recording. Finally, it discusses monitoring website content to detect hacks and defacement.
Apinizer - Full API Lifecycle and Integration Platform
A brief presentation about Apinizer, which has API Gateway, Instant API Creator, API Monitor, API Tester, API Designer, API Analytics and API Portal modules
Real-time Communications with SignalR
This document discusses building real-time web applications and introduces ASP.NET SignalR. It defines real-time functionality as the ability for server code to instantly push content to connected clients. It then reviews common real-time examples and various techniques for implementing real-time communications like periodic polling, long polling, and web sockets. The document presents SignalR as an abstraction that handles connection management and negotiates the optimal transport, allowing for easy implementation of real-time functionality like server-to-client push and RPC calls across browsers.
Continuously Updating Query Results over Real-Time Linked Data
This document discusses continuously updating query results over real-time linked data. It proposes moving continuous query evaluation from the server to the client to lower server load. Dynamic data is represented in RDF and annotated with time validity using methods like reification, graphs or implicit graphs. A query streamer engine exposes dynamic data through a Triple Pattern Fragments interface and sends query results to clients, offloading work from the server. An evaluation compares annotation methods, measures query execution times and server CPU usage, finding the query streamer approach has better scalability by distributing load to clients.
Intro to signalR
This presentation introduces ASP.NET SignalR, a library for adding real-time functionality to web applications. SignalR allows for bidirectional communication between server and client via persistent connections. It was explained that SignalR chooses the best network transport based on client capabilities, with WebSockets being the ideal method. The presentation covered the SignalR API, including hubs for high-level communication and persistent connections for lower-level access. A demo of a SignalR chat application was shown to demonstrate its use. Security concerns with SignalR were also briefly discussed.
Server Monitoring 101
This document discusses server monitoring and provides examples of Amazon CloudWatch and Datadog as monitoring services. It covers why monitoring is needed to ensure applications are available and performing well for users. Specifically, it discusses monitoring infrastructure, applications, logs, errors and other metrics. It then demonstrates how to set up and use CloudWatch to collect metrics and logs and set alarms. Finally, it reviews Datadog as an alternative open source monitoring service and how to install its agent and integrate with Slack for notifications.
Cloud applications monitoring in digital transformation era
Understand the intricacies of cloud monitoring. Learn how to leverage cloud analytics for effective resource planning.
Introduction to SignalR
An introduction to SignalR
This deck was part of my presentation to Virtusa employees on an ASP.NET asynchronous, persistent signaling library known as SignalR
There is also a slide on how to use SignalR with SharePoint.
Date: August 2013
Follow / Tweet me: @ShehanPeruma
signalr
SignalR is a library for adding real-time web functionality to applications. It uses web sockets, server-sent events, and long-polling to enable real-time functionality between a server and client browsers. SignalR supports cross-platform connections from JavaScript and .NET clients to any backend. Hubs provide a simple abstraction for defining real-time endpoints and handling connections that can scale across servers. A backplane like Redis can be used to enable real-time connections across servers for scaling out applications. SignalR can also be self-hosted outside of IIS.
Introduction to web socket
This document provides an introduction to WebSockets, including:
1) An overview of the OSI model and differences between TCP and UDP protocols.
2) A description of the HTTP protocol and how WebSockets provide bidirectional communication over a single TCP connection.
3) An example WebSocket demo using AWS services like API Gateway, Lambda, and DynamoDB to build a highly scalable serverless application.
SignalR for ASP.NET Developers
This document provides an overview of ASP.NET SignalR, a library for building real-time web functionality. It discusses traditional web application approaches using request-response, defines what "real-time" means in terms of pushing data from server to client. It introduces SignalR as a library that uses push technology to provide persistent connections and real-time functionality. It also covers SignalR's transport techniques including websockets, server-sent events, forever frames, and long polling, as well as the types of connections in SignalR including persistent connections and hubs.
Building Realtime Web Applications With ASP.NET SignalR
This document discusses building real-time web applications and introduces SignalR as a library that simplifies creating persistent HTTP connections for real-time functionality. It describes how older techniques like polling and long polling have disadvantages, while newer techniques like web sockets have limited browser support. SignalR abstracts these complexities by automatically negotiating the best transport and allowing real-time functionality to be easily added to applications. It also enables scaling out applications across servers and backing services. Demos and components of SignalR are presented, along with how it provides a fallback mechanism to support older servers and clients.
Analysing high throughput data in real time
In slide talks about the promises and use of Stream processing over Kafka Streams leveraging KSQL to analyse the ingested events to solve certain real-time use cases such as Playback Failure Rate which is a fundamental metric for over the top media streaming platforms.
A Modern Approach to Performance Monitoring
Akamai Edge 14 - Discussion on RUM, Synthetic and setting realistic and meaningful performance goals.
SharePoint 2013 Performance and Capacity Management
John Naguib session in the SPSUnity event presented SharePoint 2013 Performance and Capacity Management
[Season - 3 Free OpManager Training] Monitoring Server Performance
Manage physical and virtual servers with OpManager. Monitor availability, health and performance while simultaneously planning for capacity and better resorce allocation.
More Related Content
What's hot
Visibility-from web application interface to the database
Learn how to identify transactions that are slowing down your applications and how analyzing error traces can help you eliminate performance barriers.
SignalR Overview
SignalR is an ASP.NET library that allows real-time communication between web servers and clients. It uses various techniques like websockets, server-sent events, and long polling to provide a persistent connection. SignalR supports .NET, JavaScript, and mobile clients and can scale out to server farms using backplanes like SQL Server, Service Bus, and Redis. It includes hubs for calling methods between clients and servers and sending messages. Examples of SignalR in use include chat applications and games.
Continuous Self-Updating Query Results over Dynamic Linked Data
This document discusses continuously updating query results over dynamic linked data. It proposes moving continuous query evaluation from the server to the client to lower server load. Key points:
- Dynamic linked data streams, like sensor data, add new triples over time and typically query the current value.
- Existing approaches have servers continuously evaluate queries, causing low availability due to high load.
- The document proposes storing dynamic data efficiently, transmitting it to clients, and having clients evaluate queries to reduce server load.
- A preliminary test showed the proposed approach moves more load to clients but scales server load better than existing solutions that rely on continuous server-side query evaluation.
Flink Forward San Francisco 2019: Real-time Processing with Flink for Machine...
eal-time Processing with Flink for Machine Learning at Netflix
Machine learning plays a critical role in providing a great Netflix member experience. It is used to drive many parts of the site including video recommendations, search results ranking, and selection of artwork images. Providing high-fidelity, near real-time data is increasingly important for these machine learning pipelines, especially as multi-armed bandit and reinforcement learning techniques, in addition to more ""traditional"" supervised learning, become more prevalent. With access to this data, models are able to converge more quickly, features can be updated more frequently, and analysis can be done in a more timely manner.
In this talk, we will focus on the practical details of leveraging Flink to process trillions of events per day, work with the time dimension, and manage large and frequently-changing state. We will discuss different processing schemes and dataflows, scalability and resiliency challenges we tackled, operational considerations, and instrumentation we added for monitoring job health in production.
Consul scale
The document summarizes the growth of a Consul cluster from a few dozen agents to thousands of agents across multiple data centers. As the cluster scaled up, it began experiencing performance and stability issues like IO wait, slow key-value updates, and DNS query failures. The operators addressed these issues by upgrading Consul versions, improving monitoring, stabilizing join/leave operations, and using DNS caching. They also detailed their process for a successful large-scale Consul upgrade with minimal downtime.
Server side scalable web performance
This document discusses server-side scalable web performance. It introduces key performance indicators (KPIs) and discusses scaling techniques including using multiple servers and content delivery networks. It also covers organizational considerations like using specialist versus organizational teams and ensuring good boundaries between teams to minimize cognitive load. Performance metrics like time to first byte and time to interactive are explained. HTTP caching, HTTP/2, and edge computing are presented as ways to improve performance.
Building the right website monitoring strategy
This document discusses website monitoring strategies including tracking key metrics of web servers like Apache, IIS and Nginx; optimizing individual URLs for user experience; using synthetic monitoring to simulate web transactions; and detecting unauthorized content changes. It provides overviews of monitoring various web servers and their key performance indicators. It also describes optimizing the user experience by monitoring URL sequences, implementing real browser monitoring, and using web transaction recording. Finally, it discusses monitoring website content to detect hacks and defacement.
Apinizer - Full API Lifecycle and Integration Platform
A brief presentation about Apinizer, which has API Gateway, Instant API Creator, API Monitor, API Tester, API Designer, API Analytics and API Portal modules
Real-time Communications with SignalR
This document discusses building real-time web applications and introduces ASP.NET SignalR. It defines real-time functionality as the ability for server code to instantly push content to connected clients. It then reviews common real-time examples and various techniques for implementing real-time communications like periodic polling, long polling, and web sockets. The document presents SignalR as an abstraction that handles connection management and negotiates the optimal transport, allowing for easy implementation of real-time functionality like server-to-client push and RPC calls across browsers.
Continuously Updating Query Results over Real-Time Linked Data
This document discusses continuously updating query results over real-time linked data. It proposes moving continuous query evaluation from the server to the client to lower server load. Dynamic data is represented in RDF and annotated with time validity using methods like reification, graphs or implicit graphs. A query streamer engine exposes dynamic data through a Triple Pattern Fragments interface and sends query results to clients, offloading work from the server. An evaluation compares annotation methods, measures query execution times and server CPU usage, finding the query streamer approach has better scalability by distributing load to clients.
Intro to signalR
This presentation introduces ASP.NET SignalR, a library for adding real-time functionality to web applications. SignalR allows for bidirectional communication between server and client via persistent connections. It was explained that SignalR chooses the best network transport based on client capabilities, with WebSockets being the ideal method. The presentation covered the SignalR API, including hubs for high-level communication and persistent connections for lower-level access. A demo of a SignalR chat application was shown to demonstrate its use. Security concerns with SignalR were also briefly discussed.
Server Monitoring 101
This document discusses server monitoring and provides examples of Amazon CloudWatch and Datadog as monitoring services. It covers why monitoring is needed to ensure applications are available and performing well for users. Specifically, it discusses monitoring infrastructure, applications, logs, errors and other metrics. It then demonstrates how to set up and use CloudWatch to collect metrics and logs and set alarms. Finally, it reviews Datadog as an alternative open source monitoring service and how to install its agent and integrate with Slack for notifications.
Cloud applications monitoring in digital transformation era
Understand the intricacies of cloud monitoring. Learn how to leverage cloud analytics for effective resource planning.
Introduction to SignalR
An introduction to SignalR
This deck was part of my presentation to Virtusa employees on an ASP.NET asynchronous, persistent signaling library known as SignalR
There is also a slide on how to use SignalR with SharePoint.
Date: August 2013
Follow / Tweet me: @ShehanPeruma
signalr
SignalR is a library for adding real-time web functionality to applications. It uses web sockets, server-sent events, and long-polling to enable real-time functionality between a server and client browsers. SignalR supports cross-platform connections from JavaScript and .NET clients to any backend. Hubs provide a simple abstraction for defining real-time endpoints and handling connections that can scale across servers. A backplane like Redis can be used to enable real-time connections across servers for scaling out applications. SignalR can also be self-hosted outside of IIS.
Introduction to web socket
This document provides an introduction to WebSockets, including:
1) An overview of the OSI model and differences between TCP and UDP protocols.
2) A description of the HTTP protocol and how WebSockets provide bidirectional communication over a single TCP connection.
3) An example WebSocket demo using AWS services like API Gateway, Lambda, and DynamoDB to build a highly scalable serverless application.
SignalR for ASP.NET Developers
This document provides an overview of ASP.NET SignalR, a library for building real-time web functionality. It discusses traditional web application approaches using request-response, defines what "real-time" means in terms of pushing data from server to client. It introduces SignalR as a library that uses push technology to provide persistent connections and real-time functionality. It also covers SignalR's transport techniques including websockets, server-sent events, forever frames, and long polling, as well as the types of connections in SignalR including persistent connections and hubs.
Building Realtime Web Applications With ASP.NET SignalR
This document discusses building real-time web applications and introduces SignalR as a library that simplifies creating persistent HTTP connections for real-time functionality. It describes how older techniques like polling and long polling have disadvantages, while newer techniques like web sockets have limited browser support. SignalR abstracts these complexities by automatically negotiating the best transport and allowing real-time functionality to be easily added to applications. It also enables scaling out applications across servers and backing services. Demos and components of SignalR are presented, along with how it provides a fallback mechanism to support older servers and clients.
Analysing high throughput data in real time
In slide talks about the promises and use of Stream processing over Kafka Streams leveraging KSQL to analyse the ingested events to solve certain real-time use cases such as Playback Failure Rate which is a fundamental metric for over the top media streaming platforms.
What's hot (19)
Visibility-from web application interface to the database
Visibility-from web application interface to the database
Continuous Self-Updating Query Results over Dynamic Linked Data
Continuous Self-Updating Query Results over Dynamic Linked Data
Flink Forward San Francisco 2019: Real-time Processing with Flink for Machine...
Flink Forward San Francisco 2019: Real-time Processing with Flink for Machine...
Apinizer - Full API Lifecycle and Integration Platform
Apinizer - Full API Lifecycle and Integration Platform
Continuously Updating Query Results over Real-Time Linked Data
Continuously Updating Query Results over Real-Time Linked Data
Cloud applications monitoring in digital transformation era
Cloud applications monitoring in digital transformation era
Building Realtime Web Applications With ASP.NET SignalR
Building Realtime Web Applications With ASP.NET SignalR
Similar to Large Scale Test Automation
A Modern Approach to Performance Monitoring
Akamai Edge 14 - Discussion on RUM, Synthetic and setting realistic and meaningful performance goals.
SharePoint 2013 Performance and Capacity Management
John Naguib session in the SPSUnity event presented SharePoint 2013 Performance and Capacity Management
[Season - 3 Free OpManager Training] Monitoring Server Performance
Manage physical and virtual servers with OpManager. Monitor availability, health and performance while simultaneously planning for capacity and better resorce allocation.
Microsoft Infrastructure Monitoring using OpManager
Microsoft is a well known vendor in Enterprise IT market. This presentation explains how to monitor Microsoft products using ManageEngine OpManager. and basic of Microsoft infrastructure monitoring.
Edge 2014: A Modern Approach to Performance Monitoring
A Modern Approach to Performance Monitoring by Cliff Crocker, VP of Product Management, SOASTA
"How fast are you? How fast should you be? How do you get there? In this talk Cliff will discuss traditional approaches to performance measurement and introduce a ""RUM First"" methodology. This approach begins with capturing performance directly from the end user as the single source of truth for cross-functional organizations focused on performance.
Along the way, you will discover the relationship between RUM and synthetic monitoring, learn what to measure and how to capture it and finally how perceived performance impacts human behavior and your bottom line.
Akamai Edge is the premier event for Internet innovators, tech professionals and online business pioneers who together are forging a Faster Forward World. At Edge, the architects, experts and implementers of the most innovative global online businesses gather face-to-face for an invaluable three days of sharing, learning and together pushing the limits of the Faster Forward World. Learn more at: http://www.akamai.com/edge
Performance testing in scope of migration to cloud by Serghei Radov
This document discusses performance testing considerations for migrating an application to the cloud. It covers cloud computing principles like multi-tenancy and horizontal scalability. Challenges like over-provisioning and network issues are addressed. Effective provisioning using predictive auto-scaling is recommended. Tools for monitoring, load testing, and analyzing results are presented, including New Relic, DataDog, Flood.io, and JMeter. The document emphasizes defining acceptance criteria, workload characterization, and iterating on tests to analyze and scale resources. Costs of various performance testing tools on cloud providers are compared.
Metrics, metrics everywhere (but where the heck do you start?)
You want a single, unicorn metric that magically sums up the user experience, business value, and numbers that DevOps cares about, but so far, you're just not getting it. So where do you start? In this talk at the 2015 Velocity conference in Santa Clara, Cliff Crocker and I walked through various metrics that answer performance questions from multiple perspectives -- from designer and DevOps to CRO and CEO.
Metrics, Metrics Everywhere (but where the heck do you start?)
Not surprisingly, there’s no one-size-fits-all performance metric (though life would be simpler if there were). Different metrics will give you different critical insights into whether or not your pages are delivering the results you want — both from your end user’s perspective and ultimately from your organization’s perspective. Join Tammy Everts, and walk through various metrics that answer performance questions from multiple perspectives. You’ll walk away with a better understanding of your options, as well as a clear understanding of how to choose the right metric for the right audience.
Metrics, Metrics Everywhere (but where the heck do you start?)
Not surprisingly, there’s no one-size-fits-all performance metric (though life would be simpler if there were). Different metrics will give you different critical insights into whether or not your pages are delivering the results you want — both from your end user’s perspective and ultimately from your organization’s perspective. Join Tammy Everts, and walk through various metrics that answer performance questions from multiple perspectives. You’ll walk away with a better understanding of your options, as well as a clear understanding of how to choose the right metric for the right audience.
Web Performance Testing
Its about how to test the web performance for an application using selenium with sample code snippets
Season 4 [Free OpManager training] Part2- Monitoring Server Performance
Season 4 [Free OpManager training] Part2- Monitoring Server PerformanceManageEngine, Zoho Corporation
Learn about the following topics:
1) Monitoring the availability of network devices that are ping-able or SNMP enabled. Also, learn how to monitor the performance metrics of these devices.
2) Creating custom WMI monitors.
3) Creating scripts to monitor performance metrics that are not available in the OID.
4) Monitoring the log files and folders based on its content, size, etc.Samza at LinkedIn
This document summarizes a presentation about stream processing at LinkedIn using Apache Samza. It discusses how LinkedIn uses Samza for several use cases, including notifications, viewport tracking, and others. It provides details on how notifications are handled through a stream processing-based ATC (Air Traffic Controller) system. It also describes how viewport tracking works to power relevant content on LinkedIn's feed by processing billions of client-side tracking events daily through Samza.
Faster on Rails
This document discusses ways to improve the speed of Rails applications. It covers topics like web servers, Rack web servers, the backend, and the frontend. It analyzes how small delays can negatively impact users and recommends measuring site speed, analyzing bottlenecks, and optimizing performance. Specific technologies mentioned include Nginx, Unicorn, SPDY, database queries, and client-side optimizations. The overall message is that scaling a Rails application is challenging but important for providing a good user experience.
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...
Abstract: Recent projects have stressed the "need for speed" while handling large amounts of data, with near zero downtime. An analysis of multiple environments has identified optimizations and architectures that improve both performance and reliability. The session covers data gathering and analysis, discussing everything from the network (multiple NICs, nearby catalogs, high speed Ethernet), to the latest features of extreme scale. Performance analysis helps pinpoint where time is spent (bottlenecks) and we discuss optimization techniques (MQ tuning, IIB performance best practices) as well as helpful IBM support pacs. Log Analysis pinpoints system stress points (e.g. CPU starvation) and steps on the path to near zero downtime.
Client-Side Performance Monitoring (MobileTea, Rome)
The document discusses effective strategies for monitoring client-side web performance. It recommends collecting both real user monitoring metrics from actual users as well as synthetic metrics from automated tests. It describes tools like Navigation Timing API, paint metrics, custom metrics, and open-source libraries that can capture metrics. It also discusses storing and visualizing metrics with tools like Graphite and Grafana and how to reduce noise and account for environment differences when analyzing performance data. The overall goal is to utilize performance metrics to inform decisions that improve the user experience.
Application Performance Management
This document discusses application performance management (APM) tools at Blackboard, including:
- The Blackboard performance team monitors servers, databases, and frontends using tools like New Relic, load generators, and profilers.
- APM tools provide visibility into performance issues through centralized monitoring, and help identify abnormal behaviors, anti-patterns, and diagnose root causes.
- Keys to success include choosing the right APM tool, automating deployments, constructing effective alert policies, and properly instrumenting applications.
- The document demonstrates New Relic and provides best practices around gradual deployment, right-sizing resources, and using APM data for troubleshooting.
ATAGTR2017 Unified APM: The new age performance monitoring for production sys...
ATAGTR2017 Unified APM: The new age performance monitoring for production sys...Agile Testing Alliance
The presentation on Unified APM: The new age performance monitoring for production systems was done during #ATAGTR2017, one of the largest global testing conference. All copyright belongs to the author.
Author and presenter : Kaushik Raghavan Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
Think you have big data? What about high availability
requirements? At DataDog we process billions of data points every day including metrics and events, as we help the world
monitor the their applications and infrastructure. Being the world’s monitoring system is a big responsibility, and thanks to
Redis we are up to the task. Join us as we discuss how the DataDog team monitors and scales Redis to power our SaaS based monitoring offering. We will discuss our usage and deployment patterns, as well as dive into monitoring best practices for production Redis workloads
New relic
New Relic is an application performance monitoring tool that allows users to monitor key metrics of their applications in production. It works by installing language-specific agents that collect data on code execution times, errors, and system resource usage. The agents send this data to New Relic's cloud platform once per minute, where it is organized and calculations are performed to show metrics on application performance, server resource usage, browser performance, and availability on dashboards and mobile apps. The New Relic dashboard provides visualizations of metrics like response times, Apdex scores, throughput, errors, and more to help users analyze the health and usage of their applications in real-time.
SAP Gateway scalability testing
This document summarizes testing done on SAP NetWeaver Gateway for throughput, scalability, and high availability. Testing showed that gateway throughput increased linearly as CPU utilization increased. Gateway processing time also scaled as the number of fetched objects increased. Additional testing demonstrated that scaling out gateway instances improved throughput and that failover worked properly when an instance was taken offline. Load was also properly distributed between instances assigned to different logon groups.
Similar to Large Scale Test Automation (20)
SharePoint 2013 Performance and Capacity Management
SharePoint 2013 Performance and Capacity Management
[Season - 3 Free OpManager Training] Monitoring Server Performance
[Season - 3 Free OpManager Training] Monitoring Server Performance
Microsoft Infrastructure Monitoring using OpManager
Microsoft Infrastructure Monitoring using OpManager
Edge 2014: A Modern Approach to Performance Monitoring
Edge 2014: A Modern Approach to Performance Monitoring
Performance testing in scope of migration to cloud by Serghei Radov
Performance testing in scope of migration to cloud by Serghei Radov
Metrics, metrics everywhere (but where the heck do you start?)
Metrics, metrics everywhere (but where the heck do you start?)
Metrics, Metrics Everywhere (but where the heck do you start?)
Metrics, Metrics Everywhere (but where the heck do you start?)
Metrics, Metrics Everywhere (but where the heck do you start?)
Metrics, Metrics Everywhere (but where the heck do you start?)
Season 4 [Free OpManager training] Part2- Monitoring Server Performance
Season 4 [Free OpManager training] Part2- Monitoring Server Performance
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...
Architecting and Tuning IIB/eXtreme Scale for Maximum Performance and Reliabi...
Client-Side Performance Monitoring (MobileTea, Rome)
Client-Side Performance Monitoring (MobileTea, Rome)
ATAGTR2017 Unified APM: The new age performance monitoring for production sys...
ATAGTR2017 Unified APM: The new age performance monitoring for production sys...
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDog
Recently uploaded
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Dmitrii Kamaev, PhD
Senior Product Owner - QIAGEN
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Tomaz Bratanic
Graph ML and GenAI Expert - Neo4j
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Principle of conventional tomography-Bibash Shahi ppt..pptx
before the computed tomography, it had been widely used.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Recently uploaded (20)
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Large Scale Test Automation
- 1. Secrets of Successful Test Automation Narek Arushanov - Ashot Karapetyan Barcamp 2018 Synergy International Systems
- 2. Agenda Backend Performance UI Performance Scaling Acceptance Test
- 3. Requirements 1. Periodicity 2. Resource Allocation. 3. Summary Reports 4. Centralized Monitoring.
- 4. Scheduled Execution Every night 100 rps 10-20 min
- 5. Limits 1 GB RAM No Pers. Storage 10Mb network 1 core
- 6. Tools
- 7. Percentiles
- 8. Standard Reports Service endpoint Scale rps 95p in millis Total requests count # of Errors ml LoadAllLanguages 1 100 19 59547 0 LoadLanguagesById 1 100 29 59548 0 LoadMessagesByKeys 1 100 92 59547 0 LoadMessagesByLanguage Id 1 100 13 59548 0
- 9. Fail
- 10. OK
- 12. Measuring UI Performance 1. Page load time with different network speed 2. Response traffic size 3. Requests count 4. Automated 5. Centralized monitoring
- 13. UI performance tools 1. sitespeed.io 2. Navigation Timing API 3. BrowserMob Proxy + Selenium
- 16. Page Load Time
- 17. Request Count
- 19. Scaling Acceptance Tests 1. Parallel Test Execution 2. Multi-browser 3. Browser Versions
- 20. Ways to test in browsers 1. Cloud web testing platform BrowserStack Sauce Labs Testing Bot
- 21. Ways to test in browsers 1. Cloud web testing platform 2. Selenium Grid
- 22. Problems 1. Hub is crushing. 2. Different browser versions. 3. Temp folders 4. Window focus 5. etc …
- 23. Ways to test in browsers 1. Cloud web testing platform 2. Selenium Grid 3. Selenoid
- 24. How to work solenoid
- 25. How to work solenoid
- 26. Selenium Grid UI
- 27. Selenoid UI
- 28. Advantages 1. Stability 2. Tests run in isolated container 3. Run tests in different browser versions 4. Tmpfs support 5. Hot configuration reload
- 29. Resources control 1. Limit total number of sessions per host 2. Queues 3. Limit CPUs per container 4. Limit RAM per container
- 30. Cluster Solution
Editor's Notes
- Software engi. in test
- Response time 1s
- Response time 1s
- Locust Scalability, because you can run Locust distributed with many agents. Based on gevent simulate thousand of users in single process
- orinak 10ic 5 1s 2 3s. 3 4s nkarel estex
- poxel chart@
- Verjum asel en inch karox enq chapel
- Sakayn mer tester@ run enq talis mer development envirementum, ev chenq uzum kaxvacutyun unenanq ayl servisneric.
- Selenoid @ nuyn selenium serveri implementacian e , go lezvov grac. vor@ aveli arag e ev aveli tetev. ira hamar ka shat harmar dockerov realizacia