A report generated by the Chaos Toolkit after a Chaos Engineering experiment against OpenFaaS on Kubernetes.
View the run of the experiment at https://asciinema.org/a/dv4cNOMC5k1oWDhWDe97d5eij
Programming with ZooKeeper - A basic tutorialJeff Smith
This document provides a tutorial on using ZooKeeper to implement basic distributed synchronization primitives like barriers and producer-consumer queues. It includes code examples for Barrier and Queue classes that extend a base SyncPrimitive class. The Barrier class allows processes to synchronize barrier entry and exit. The Queue class implements a distributed queue where producers can add elements and consumers can remove the oldest element. Both use ZooKeeper to coordinate access through ephemeral nodes and watches.
- The document discusses how to use Solaris projects to control system resources and limit resource usage for different processes. It provides background on the project configuration file and attributes that can be set to limit CPU time, memory usage, file sizes, and other resources for processes associated with a project.
- As an example, it shows how to create a "test" project that limits processes to files smaller than 16MB. Running processes under this project hits the file size limit as expected.
- Projects provide a way to separate workloads and ensure that important applications receive sufficient system resources by limiting competing processes in other projects.
OSMC 2012 | Neues in Nagios 4.0 by Andreas EricssonNETWAYS
The document provides an overview of improvements and new features in Nagios Core 4. Key points include:
- Bottlenecks in Nagios Core 3 were analyzed and improvements were made to configuration parsing, event queue insertion, macro resolution, and check processing. These improved performance and scalability.
- A new query handler and NERD (Nagios Event Radio Dispatcher) were added to provide real-time data to external addons.
- Support for service parents and new check result variables were added. Deprecation notices were provided for removed or changed features.
Tech Talks_25.04.15_Session 3_Tibor Sulyan_Distributed coordination with zook...EPAM_Systems_Bulgaria
This document provides an overview of distributed coordination using ZooKeeper and Curator. It discusses ZooKeeper features like its data model, API, guarantees such as consistency and availability. Common recipes for coordination like shared data, service discovery, locking, and leader election are presented. The document also covers performance considerations and advanced ZooKeeper topics like using observers and hierarchical quorums. Finally, Curator is introduced as a higher-level client API for ZooKeeper that implements many recipes and handles complexity.
StatsCraft 2015: Monitoring using riemann - Moshe ZadaStatsCraft
This document discusses using Riemann for event processing and alerting. It provides examples of using Riemann to implement basic alerts, visualize events by streaming to ELK, and aggregate events. It also covers techniques like adding throttling to alerts, ignoring spikes, and putting the system in maintenance mode to ignore alerts temporarily. Code examples are provided for building alert streams and processing events in Riemann.
Testing multi outputformat based mapreduceAshok Agarwal
The document describes using a MultiOutputFormat in MapReduce to generate separate output files for each stock price based on input that contains stock price data. It includes code for a mapper that extracts the stock name and price from each input record and a reducer that writes these values to individual files for each stock name. Unit tests are also described to test the reducer by mocking the MultipleOutputs class and validating that the output files contain the expected stock price values.
This plugin lets you track your phone and maintain inventory information of your Windows mobile equipments using PandoraFMS Enterprise. For more information you can visit the following website: http://pandorafms.com/index.php?sec=Library&sec2=repository&lng=en&action=view_PUI&id_PUI=313
The document discusses unit testing and test-driven development. It introduces the QUnit JavaScript testing framework, describing how to write tests and assertions using its API. Key aspects covered include setting up QUnit, the test and assert functions, asynchronous testing, grouping tests into modules, and integrating automated testing with Node and Grunt.
Nowadays, CPU microarchitecture is concealed from developers by compilers, VMs, etc.
Do Java developers need to know microarchitecture details of modern processors?
Or, does it like to learn quantum mechanics for cooking?
Are Java developers safe from leaking low-level microarchitecture details into high level application performance behaviour?
We will try to answer these questions by analyzing several Java examples.
This document describes the STAMP project which aims to increase test automation in DevOps. It focuses on test amplification using techniques like mutation analysis and automatic test improvement. The project involves 4 research institutions, 5 companies and an open source consortium with 516 person months. It will develop tools like Descartes and DSpot which use extreme mutation and suggest improvements to existing test cases.
This presentation introduces to the main features of JTR 5. It has been prepared and used for a Lightning Talk at FOSDEM '09 in Brussels.
More information can be found at
http://jtrunner.sourceforge.net
The Scatter-Gather pattern allows an application to send a message to multiple receivers asynchronously and gather the responses. This document provides an overview of the pattern and MuleSoft's implementation of it using examples. It discusses how MuleSoft's Scatter-Gather component can send messages concurrently to receivers, aggregate responses using custom strategies, and handle exceptions from receivers. Case studies demonstrate how the pattern improves performance over sequential calling and allows gathering responses within a timeout period.
Oracle trace data collection errors: the story about oceans, islands, and riversCary Millsap
When you execute a business task on a computer system, you create an experience. The duration of this experience is called response time. The richest and easiest information about response time to obtain in the whole Oracle technology stack is available from the Oracle Database tier: Oracle's extended SQL trace data. But in almost 100% of first tries with using trace data, people make a data collection mistake that complicates their analysis. This is the story of that mistake.
TDD is a design technique where tests are written before code to determine requirements. The Red-Green-Refactor process is followed: 1) Write a test that fails (red), 2) Write minimum code to pass the test (green), 3) Refactor code. TDD ensures quality, keeps code simple and testable, and allows for rapid change. Writing tests first helps design by clarifying requirements and preventing bugs.
This document outlines the motivation and design of Java 8 streams. Streams were introduced to simplify working with collections by allowing operations to be pipelined in a declarative style. The key aspects are:
- Streams represent a sequence of values and operations are deferred
- Common patterns of filtering, mapping, sorting collections can be expressed concisely in a stream pipeline
- Sources include collections, arrays, generators. Intermediate operations transform the stream, while a terminal operation produces the result.
Mobile Fest 2018. Yonatan Levin. WTF with Android Background RestrictionsMobileFest2018
With recent Android releases the wild west of endless running threads is over.
No more smoking barrels of "last 5% battery".
You are required to act and seriously consider how you became the excellent citizen of Android device and play side by side with a marshall law of background tasks. Or die trying.
It's a talk we will explore how memory affect a background process, what solutions we have today and how new WorkManager can save our lives and galaxy :)
Most important "trick" of performance instrumentationCary Millsap
This is the material from my 10-minute TED-style talk 2014-09-29 at OakTable World held in conjunction with Oracle OpenWorld 2014 in San Francisco. It explains the importance of assigning a unique id to the Oracle Database code path associated with each performance experience that users can have with your system
Demystifying dependency Injection: Dagger and ToothpickDanny Preussler
Dependency Injection, that's the thing that Dagger2 does, right? For most of us, it's black magic. Let's look deeper how DI works. And then we better understand the frameworks that are out there. This session introduce Toothpick that was created this year and is already used by millions of users as driven a lot by engineers at Groupon.
What was their need? What's wrong with Dagger?
Toothpick was designed to bring goodies from Guice to modern mobile world. It focuses on scopes and unit testing without loosing performance as Roboguice did.
Reactive Fault Tolerant Programming with Hystrix and RxJavaMatt Stine
This document discusses reactive fault tolerant programming using Hystrix and RxJava. It begins with an overview of microservices architecture and how latency can occur between services. It then introduces futures and CompletableFutures as ways to allow asynchronous and parallel processing. RxJava is presented as a reactive programming library that can be used to compose asynchronous and event-based programs. Hystrix is discussed as a library for implementing the circuit breaker pattern to handle failures gracefully in distributed systems. The document provides examples of using RxJava and Hystrix to build fault tolerant reactive microservices.
This document provides code samples and documentation for the JActor library. It includes examples of basic actor concepts like hello world, actor-to-actor communication, parallel programming, dependency injection, and exception handling. Each example is broken down into the actor and request classes needed to demonstrate the concept.
OSMC 2012 | Monitoring at CERN by Christophe HaenNETWAYS
Das CERN, die Europäische Organisation für Kernforschung, ist das weltweit größte Forschungszentrum für Teilchenphysik. Es werden dort Experimente in der Hochenergiephysik mit Hilfe des Teilchenbeschleunigers durchgeführt sowie anderer bereitgestellter Infrastrukturen. Die Untersuchungen rund um den Large Hadron Collider (LHC), erfordern umfangreiche IT-Infrastrukturen, um die Daten, die durch die Kollisionen generiert werden, zu verarbeiten. Sogar die Überwachung des LHC selbst hängt von einer komplexen Infrastruktur ab. Die CERN-IT stellt den Mitarbeitern und den Usern viele unterschiedliche Services bereit und ist vor allem aber der Hauptakteur des LHC GRID. Das GRID ist das weltweit verteilte Rechen- und Speicher-Netzwerk, das die nötige Kapazität zur Verfügung stellt um die Menge an Daten, die anhand des Teilchenbeschleunigers gesammelt wird, analysieren zu können. Es besteht aus 200.000 Cores verteilt auf 34 Ländern. All diese großen Rechenzentren erfordern ein sorgfältiges Monitoring, aber jedes für sich hat Besonderheiten, was dazu führt, dass unterschiedliche Monitoring Strategien und Tools angewandt werden müssen. Die unzähligen Herangehensweisen an diese Herausforderung werden in diesem Vortrag aufgezeigt sowie ein Ausblick auf geplante künftige Entwicklungen.
This document discusses common scenarios for using Varnish Configuration Language (VCL) scripts to configure the caching behavior of the Varnish caching server. It covers topics like normalizing requests, caching static assets, whitelisting and blacklisting URLs, handling cookies, using Edge Side Includes, controlling the time to live for cached responses, debugging techniques, and purging cached content. Example VCL code is provided for many of these common use cases.
Why Kotlin - Apalon Kotlin Sprint Part 1Kirill Rozov
Kotlin is a modern programming language that was created by JetBrains as a replacement for Java, with some key advantages:
- It simplifies development tasks like creating data classes and working with collections. Kotlin also reduces the amount of code needed for common operations.
- Kotlin works seamlessly with Java code and is fully interoperable. It is also compatible with existing Java tools and platforms.
- The language has seen growing adoption since its 1.0 release in 2016 and is now officially supported by Google for Android development. Many large companies and open source projects use Kotlin due to its improvements over Java.
The document discusses Perl testing tools like Test::Harness that allow for automated testing of Perl modules. It provides an example of using Test::Harness to test a module called Acme::Test and outputs the results in the TAP format. The document also lists many other Perl testing and development modules that can be used for tasks like profiling, benchmarking, and code coverage analysis.
The document discusses modern concurrency primitives like threads, thread pools, coroutines, and schedulers. It covers why asynchronous programming with async/await is preferred over traditional threading. It also discusses challenges like sharing data across threads and blocking on I/O calls. Some solutions covered include using thread pools with dedicated I/O threads, work stealing, and introducing interruption points in long-running tasks.
An evaluation of FaaS platforms as a foundation for serverless big data proce...Mohamed Samir
- My presentation in faculty of computers and artficial intelligence, cairo university on Evaluation of Faas Platforms as a foundation for serverless applications
- This presentation is base on a research paper by the same name [ Evaluation of FaaS Platforms as a Foundation for Serverless Big Data Processing ]
- Link to the research paper => [https://www.researchgate.net/publication/337581127_An_Evaluation_of_FaaS_Platforms_as_a_Foundation_for_Serverless_Big_Data_Processing]
- I encourage developers specially Technical team leads and Architects spare time to read the research paper or at least the presentation, it has many insights on how FaaS platforms like Amazon Web Services Lambda (AWS),
Google Cloud Function (GCF),
IBM Cloud Function (ICF) and
Microsoft Azure Functions (MAF) work under the hood to serve your application , such insights will aid you in take critical design decisions in building your apps
Practical Chaos Engineering will show how to start running chaos experiments in your infrastructure and will try to guide your through the principles of chaos.
當線上運作環境發生問題時,如何在最短時間找出問題核心?我們使用 counter 這個工具來解決。Counter 是在程式裡的一行程式碼,用來記錄感興趣的事件。本演講包含以下內容:Golang counter 程式碼範例,後台系統架構,線上 dashboard,以及如何使用 counter 來偵錯,甚至可以一路追至某一行程式碼。另外,本演講會提及幾個有趣的應用:counter 如何協助定期伺服器更新,如何使用 counter 協助 autoscaling,以及未來的應用。
When production alert triggers, how to identify the root cause within the shortest amount of time? We solve the problem by counter, a line of code inserted by developer to count interesting events. In this talk, we'll cover the following topics: 1) how counter looks like in Golang production code, 2) our counter pipeline, 3) service dashboard with counters, 4) how to use counter to find production issues all the way to certain line of code. We'll also cover a few interesting counter use-cases, including: 1) How counter helps our weekly server upgrade, 2) Use counter for autoscaling, and 3) case-studies to demonstrate what counter can do when outages happen.
The document discusses unit testing and test-driven development. It introduces the QUnit JavaScript testing framework, describing how to write tests and assertions using its API. Key aspects covered include setting up QUnit, the test and assert functions, asynchronous testing, grouping tests into modules, and integrating automated testing with Node and Grunt.
Nowadays, CPU microarchitecture is concealed from developers by compilers, VMs, etc.
Do Java developers need to know microarchitecture details of modern processors?
Or, does it like to learn quantum mechanics for cooking?
Are Java developers safe from leaking low-level microarchitecture details into high level application performance behaviour?
We will try to answer these questions by analyzing several Java examples.
This document describes the STAMP project which aims to increase test automation in DevOps. It focuses on test amplification using techniques like mutation analysis and automatic test improvement. The project involves 4 research institutions, 5 companies and an open source consortium with 516 person months. It will develop tools like Descartes and DSpot which use extreme mutation and suggest improvements to existing test cases.
This presentation introduces to the main features of JTR 5. It has been prepared and used for a Lightning Talk at FOSDEM '09 in Brussels.
More information can be found at
http://jtrunner.sourceforge.net
The Scatter-Gather pattern allows an application to send a message to multiple receivers asynchronously and gather the responses. This document provides an overview of the pattern and MuleSoft's implementation of it using examples. It discusses how MuleSoft's Scatter-Gather component can send messages concurrently to receivers, aggregate responses using custom strategies, and handle exceptions from receivers. Case studies demonstrate how the pattern improves performance over sequential calling and allows gathering responses within a timeout period.
Oracle trace data collection errors: the story about oceans, islands, and riversCary Millsap
When you execute a business task on a computer system, you create an experience. The duration of this experience is called response time. The richest and easiest information about response time to obtain in the whole Oracle technology stack is available from the Oracle Database tier: Oracle's extended SQL trace data. But in almost 100% of first tries with using trace data, people make a data collection mistake that complicates their analysis. This is the story of that mistake.
TDD is a design technique where tests are written before code to determine requirements. The Red-Green-Refactor process is followed: 1) Write a test that fails (red), 2) Write minimum code to pass the test (green), 3) Refactor code. TDD ensures quality, keeps code simple and testable, and allows for rapid change. Writing tests first helps design by clarifying requirements and preventing bugs.
This document outlines the motivation and design of Java 8 streams. Streams were introduced to simplify working with collections by allowing operations to be pipelined in a declarative style. The key aspects are:
- Streams represent a sequence of values and operations are deferred
- Common patterns of filtering, mapping, sorting collections can be expressed concisely in a stream pipeline
- Sources include collections, arrays, generators. Intermediate operations transform the stream, while a terminal operation produces the result.
Mobile Fest 2018. Yonatan Levin. WTF with Android Background RestrictionsMobileFest2018
With recent Android releases the wild west of endless running threads is over.
No more smoking barrels of "last 5% battery".
You are required to act and seriously consider how you became the excellent citizen of Android device and play side by side with a marshall law of background tasks. Or die trying.
It's a talk we will explore how memory affect a background process, what solutions we have today and how new WorkManager can save our lives and galaxy :)
Most important "trick" of performance instrumentationCary Millsap
This is the material from my 10-minute TED-style talk 2014-09-29 at OakTable World held in conjunction with Oracle OpenWorld 2014 in San Francisco. It explains the importance of assigning a unique id to the Oracle Database code path associated with each performance experience that users can have with your system
Demystifying dependency Injection: Dagger and ToothpickDanny Preussler
Dependency Injection, that's the thing that Dagger2 does, right? For most of us, it's black magic. Let's look deeper how DI works. And then we better understand the frameworks that are out there. This session introduce Toothpick that was created this year and is already used by millions of users as driven a lot by engineers at Groupon.
What was their need? What's wrong with Dagger?
Toothpick was designed to bring goodies from Guice to modern mobile world. It focuses on scopes and unit testing without loosing performance as Roboguice did.
Reactive Fault Tolerant Programming with Hystrix and RxJavaMatt Stine
This document discusses reactive fault tolerant programming using Hystrix and RxJava. It begins with an overview of microservices architecture and how latency can occur between services. It then introduces futures and CompletableFutures as ways to allow asynchronous and parallel processing. RxJava is presented as a reactive programming library that can be used to compose asynchronous and event-based programs. Hystrix is discussed as a library for implementing the circuit breaker pattern to handle failures gracefully in distributed systems. The document provides examples of using RxJava and Hystrix to build fault tolerant reactive microservices.
This document provides code samples and documentation for the JActor library. It includes examples of basic actor concepts like hello world, actor-to-actor communication, parallel programming, dependency injection, and exception handling. Each example is broken down into the actor and request classes needed to demonstrate the concept.
OSMC 2012 | Monitoring at CERN by Christophe HaenNETWAYS
Das CERN, die Europäische Organisation für Kernforschung, ist das weltweit größte Forschungszentrum für Teilchenphysik. Es werden dort Experimente in der Hochenergiephysik mit Hilfe des Teilchenbeschleunigers durchgeführt sowie anderer bereitgestellter Infrastrukturen. Die Untersuchungen rund um den Large Hadron Collider (LHC), erfordern umfangreiche IT-Infrastrukturen, um die Daten, die durch die Kollisionen generiert werden, zu verarbeiten. Sogar die Überwachung des LHC selbst hängt von einer komplexen Infrastruktur ab. Die CERN-IT stellt den Mitarbeitern und den Usern viele unterschiedliche Services bereit und ist vor allem aber der Hauptakteur des LHC GRID. Das GRID ist das weltweit verteilte Rechen- und Speicher-Netzwerk, das die nötige Kapazität zur Verfügung stellt um die Menge an Daten, die anhand des Teilchenbeschleunigers gesammelt wird, analysieren zu können. Es besteht aus 200.000 Cores verteilt auf 34 Ländern. All diese großen Rechenzentren erfordern ein sorgfältiges Monitoring, aber jedes für sich hat Besonderheiten, was dazu führt, dass unterschiedliche Monitoring Strategien und Tools angewandt werden müssen. Die unzähligen Herangehensweisen an diese Herausforderung werden in diesem Vortrag aufgezeigt sowie ein Ausblick auf geplante künftige Entwicklungen.
This document discusses common scenarios for using Varnish Configuration Language (VCL) scripts to configure the caching behavior of the Varnish caching server. It covers topics like normalizing requests, caching static assets, whitelisting and blacklisting URLs, handling cookies, using Edge Side Includes, controlling the time to live for cached responses, debugging techniques, and purging cached content. Example VCL code is provided for many of these common use cases.
Why Kotlin - Apalon Kotlin Sprint Part 1Kirill Rozov
Kotlin is a modern programming language that was created by JetBrains as a replacement for Java, with some key advantages:
- It simplifies development tasks like creating data classes and working with collections. Kotlin also reduces the amount of code needed for common operations.
- Kotlin works seamlessly with Java code and is fully interoperable. It is also compatible with existing Java tools and platforms.
- The language has seen growing adoption since its 1.0 release in 2016 and is now officially supported by Google for Android development. Many large companies and open source projects use Kotlin due to its improvements over Java.
The document discusses Perl testing tools like Test::Harness that allow for automated testing of Perl modules. It provides an example of using Test::Harness to test a module called Acme::Test and outputs the results in the TAP format. The document also lists many other Perl testing and development modules that can be used for tasks like profiling, benchmarking, and code coverage analysis.
The document discusses modern concurrency primitives like threads, thread pools, coroutines, and schedulers. It covers why asynchronous programming with async/await is preferred over traditional threading. It also discusses challenges like sharing data across threads and blocking on I/O calls. Some solutions covered include using thread pools with dedicated I/O threads, work stealing, and introducing interruption points in long-running tasks.
An evaluation of FaaS platforms as a foundation for serverless big data proce...Mohamed Samir
- My presentation in faculty of computers and artficial intelligence, cairo university on Evaluation of Faas Platforms as a foundation for serverless applications
- This presentation is base on a research paper by the same name [ Evaluation of FaaS Platforms as a Foundation for Serverless Big Data Processing ]
- Link to the research paper => [https://www.researchgate.net/publication/337581127_An_Evaluation_of_FaaS_Platforms_as_a_Foundation_for_Serverless_Big_Data_Processing]
- I encourage developers specially Technical team leads and Architects spare time to read the research paper or at least the presentation, it has many insights on how FaaS platforms like Amazon Web Services Lambda (AWS),
Google Cloud Function (GCF),
IBM Cloud Function (ICF) and
Microsoft Azure Functions (MAF) work under the hood to serve your application , such insights will aid you in take critical design decisions in building your apps
Practical Chaos Engineering will show how to start running chaos experiments in your infrastructure and will try to guide your through the principles of chaos.
當線上運作環境發生問題時,如何在最短時間找出問題核心?我們使用 counter 這個工具來解決。Counter 是在程式裡的一行程式碼,用來記錄感興趣的事件。本演講包含以下內容:Golang counter 程式碼範例,後台系統架構,線上 dashboard,以及如何使用 counter 來偵錯,甚至可以一路追至某一行程式碼。另外,本演講會提及幾個有趣的應用:counter 如何協助定期伺服器更新,如何使用 counter 協助 autoscaling,以及未來的應用。
When production alert triggers, how to identify the root cause within the shortest amount of time? We solve the problem by counter, a line of code inserted by developer to count interesting events. In this talk, we'll cover the following topics: 1) how counter looks like in Golang production code, 2) our counter pipeline, 3) service dashboard with counters, 4) how to use counter to find production issues all the way to certain line of code. We'll also cover a few interesting counter use-cases, including: 1) How counter helps our weekly server upgrade, 2) Use counter for autoscaling, and 3) case-studies to demonstrate what counter can do when outages happen.
Redefining Plattform "Openness" with OpenFaaSSimon Pelczer
This talk was given during Talks 4 Nerds (T4N) on the 12.11.2018. The topic of the talk was OpenFaaS and also an use case showing the usage of OpenFaaS to redefining external API's.
Real-time monitoring and delay management of a transport information systemLorenzo Sfarra
Algorithms and models like Fully Time Expanded, software and hardware for a real-time monitoring and delay management solution of a transport information system
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.
Manage Microservices Chaos and Complexity with ObservabilityNGINX, Inc.
Learn about the three principal classes of observability data, the importance of infrastructure and app alignment, and ways to start analyzing deep data.
A chaos experiment a day, keeping the outage awayYan Cui
Presented at ServerlessDays Warsaw
Recording: https://youtu.be/21HprKZQczs
You might have heard about chaos engineering in the context of Netflix and Amazon, and how they kill EC2 servers in production at random to verify that their systems can stay up in the face of infrastructure failures. But did you know that the same ideas can be applied to serverless applications? Yes, despite not having access to the underlying servers, we can still apply principles of chaos engineering to uncover failure modes in our system (and there are plenty!) so we can build defence against them and make our serverless applications more robust and more resilient!
Docker and serverless Randstad Jan 2019: OpenFaaS Serverless: when functions ...Edward Wilde
This talk introduces OpenFaaS, an open source platform for serverless functions. We begin by installing, running the platform and invoking a machine learning function within 60 second (ish). Next we step up and look at the high level platform architecture and key pillars of OpenFaaS: developer first, operator friendly and community focused. We close by looking at a multiuser platform built on top OpenFaas.
The objective of this talk is to introduce you to OpenFaaS the serverless platform and OpenFaaS cloud the multi-user environment for practicing git ops with functions.
Dmytro Lavrinenko "Running Functions at the Edge"Fwdays
The document discusses running functions at the edge to perform data processing, analytics, and machine learning predictions closer to end users. Some benefits are cost savings, efficiency, and faster data processing. Common functions at the edge include data loading, processing, analytics, visualization, and machine learning. Key components for running functions at the edge are event gateways, function executors, storage management, and interfaces to trigger functions via events from sources like Kafka, HTTP, and MQTT. Functions need to be deployed and managed in edge environments through function execution engines and management systems.
How to bring chaos engineering to serverlessYan Cui
You might have heard about chaos engineering in the context of Netflix and Amazon, and how they kill EC2 servers in production at random to verify that their systems can stay up in the face of infrastructure failures. But did you know that the same ideas can be applied to serverless applications? Yes, despite not having access to the underlying servers, we can still apply principles of chaos engineering to uncover failure modes in our system (and there are plenty!) so we can build a defence against them and make our serverless applications more robust and more resilient!
OSMC 2016 - Alerting with Time Series by Fabian ReinartzNETWAYS
Fabian Reinarz ist Software Ingenieur bei CoreOS und einer der Core Developer von Prometheus, ein Monitoringsystem und Timeseries-Datenbank. Davor war er Produktionsingenieur bei SoundCloud und arbeitete Im Bereich Informationsgewinnung an der Universität Saarland.
Traffic Based Malicious Switch and DDoS Detection in Software Defined NetworkAkshaya Arunan
In this project secured threshold value (STV) and sequential probability ratio test (SPRT) were
developed and used to detect and eliminate malicious switches and DDoS attacks in the SDN network.
How to use probabilistic inference programming for application orchestration ...Veselin Pizurica
As companies are adopting serverless architectures and moving away from monolithic and microservice-based deployments, they realise that the challenge lies not only in the rewrite of an old application, but also in the shift towards a new way of thinking. We see many serverless architecture patterns today, such as function chaining, function chaining with rollback (for transaction), ASync HTTP, fan-out and more. We also have a number of tools on the market that ease application development using serverless, of which Apache OpenWhisk (via action chaining or using function composites) and Amazon Step Functions are some of the more popular. In this talk, we will present a new alternative way of building serverless applications based on the orchestration of typed functions, using the probabilistic inference programing paradigm. Inference-based programming brings about the best of the current modelling approaches: the expressiveness and simplicity of decision trees, the high debugging capabilities of state machines, the scalability and flexibility of flow based programming and superior logic expressions to forward chaining approaches. The talk will include a live demo of how to use probabilistic inference programming for a complex IoT application.
This document is the preface to the 2005 edition of the Teletraffic Engineering Handbook published jointly by the ITU and ITC. The handbook covers the basic theory of teletraffic engineering using elementary probability theory. It includes 15 chapters covering topics like introduction, mathematical background, telecommunication loss models, data communication delay models, and measurements. The purpose is to serve as both a handbook and textbook to help engineers understand ITU recommendations on traffic engineering, evaluate tools and methods, and stay up-to-date. The handbook was initiated by the ITC and adopted by the ITU-D Study Group 2 to benefit both developing and developed countries.
Extent 2013 Obninsk Trading Systems: Testing at the Confluence of FT & NFTextentconf Tsoy
Exactpro Systems - "Trading Systems: Testing at the Confluence of FT & NFT"
Alexey Zverev, Managing Director, Exactpro Systems
Alyona Bulda, QA Project Lead, Exactpro Systems
Ivan Bobrov, HFT Analyst, Exactpro Systems
Trading Systems: Testing at the Confluence of FT and NFTIosif Itkin
EXTENT Trading Technology Trends & Quality Assurance Conference in Obninsk, 2 March, 2013
Trading Systems:
Testing at the Confluence of FT & NFT
Alexey Zverev, Managing Director
Alyona Bulda, QA Project Lead
Ivan Bobrov, HFT Analyst
Distributed Trace & Log Analysis using MLJorge Cardoso
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As businesses are transitioning to the adoption of the multi-cloud environment to promote flexibility, performance, and resilience, the hybrid cloud strategy is becoming the norm. This session explores the pivotal nature of Microsoft Azure in facilitating smooth integration across various cloud platforms. See how Azure’s tools, services, and infrastructure enable the consistent practice of management, security, and scaling on a multi-cloud configuration. Whether you are preparing for workload optimization, keeping up with compliance, or making your business continuity future-ready, find out how Azure helps enterprises to establish a comprehensive and future-oriented cloud strategy. This session is perfect for IT leaders, architects, and developers and provides tips on how to navigate the hybrid future confidently and make the most of multi-cloud investments.
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2. Completed Thu, 12 Apr 2018 09:40:41 GMT
Duration 1 minute
Experiment
The experiment was made of 1 actions, to vary conditions in your system, and 5
probes, to collect objective data from your system during the experiment.
Steady State Hypothesis
The steady state hypothesis this experiment tried was “Function is available”.
Before Run
The steady state was verified
Probe Tolerance Verified
function-must-exist 200 True
function-must-respond 200 True
there-should-be-only-one-running-function-
pod
1 True
there-should-be-no-active-alerts {‘type’: ‘jsonpath’,
‘path’:
“$.data.result[?metric.alertname==‘APIHighInvocationRate’]
‘count’: 0}
True
After Run
The steady state was verified
Probe Tolerance Verified
function-must-exist 200 True
function-must-respond 200 True
there-should-be-only-one-running-function-
pod
1 True
there-should-be-no-active-alerts {‘type’: ‘jsonpath’,
‘path’:
“$.data.result[?metric.alertname==‘APIHighInvocationRate’]
‘count’: 0}
True
2
3. Method
The experiment method defines the sequence of activities that help gathering
evidence towards, or against, the hypothesis.
The following activities were conducted as part of the experimental’s method:
Type Name
action simulate-user-traffic-under-heavy-load
probe we-should-see-active-alerts
probe we-should-now-see-many-function-pods
probe query-number-of-functions
probe fetch-openfaas-gateway-logs
probe query-total-function-invocation
Result
The experiment was conducted on Thu, 12 Apr 2018 09:39:14 GMT and lasted
roughly 1 minute.
Action - simulate-user-traffic-under-heavy-load
Status succeeded
Background True
Started Thu, 12 Apr 2018 09:39:16 GMT
Ended Thu, 12 Apr 2018 09:40:06 GMT
Duration 50 secondes
The action provider that was executed:
Type process
Path vegeta
Timeout N/A
Arguments attack -targets=scenario.txt -workers=8 -rate=30
-timeout=3s -duration=50s -output=result.bin
Probe - we-should-see-active-alerts
Status succeeded
Background False
3
4. Started Thu, 12 Apr 2018 09:39:46 GMT
Ended Thu, 12 Apr 2018 09:39:46 GMT
Duration 0 secondes
Paused Before 30s
The probe provider that was executed:
Type python
Module chaosprometheus.probes
Function query
Arguments {‘query’: ‘ALERTS’}
Probe - we-should-now-see-many-function-pods
Status succeeded
Background False
Started Thu, 12 Apr 2018 09:40:06 GMT
Ended Thu, 12 Apr 2018 09:40:07 GMT
Duration 1 seconde
Paused Before 20s
The probe provider that was executed:
Type python
Module chaosk8s.pod.probes
Function count_pods
Arguments {‘ns’: ‘openfaas-fn’, ‘label_selector’: ‘faas_function=astre’,
‘phase’: ‘Running’}
Probe - query-number-of-functions
Status succeeded
Background False
Started Thu, 12 Apr 2018 09:40:37 GMT
Ended Thu, 12 Apr 2018 09:40:38 GMT
Duration 1 seconde
Paused Before 30s
The probe provider that was executed:
4
5. Type python
Module chaosprometheus.probes
Function query_interval
Arguments {‘query’:
“gateway_service_count{function_name=‘astre’}”,
‘start’: ‘1 minute ago’, ‘end’: ‘now’, ‘step’: 1}
Probe - fetch-openfaas-gateway-logs
Status succeeded
Background False
Started Thu, 12 Apr 2018 09:40:38 GMT
Ended Thu, 12 Apr 2018 09:40:39 GMT
Duration 1 seconde
The probe provider that was executed:
Type python
5
6. Module chaosk8s.pod.probes
Function read_pod_logs
Arguments {‘label_selector’: ‘app=gateway’, ‘last’: ‘85s’, ‘ns’: ‘openfaas’}
Probe - query-total-function-invocation
Status succeeded
Background False
Started Thu, 12 Apr 2018 09:40:39 GMT
Ended Thu, 12 Apr 2018 09:40:40 GMT
Duration 1 seconde
The probe provider that was executed:
Type python
Module chaosprometheus.probes
Function query_interval
Arguments {‘query’: “gate-
way_function_invocation_total{function_name=‘astre’}”,
‘start’: ‘2 minutes ago’, ‘end’: ‘now’, ‘step’: 1}
6
7. Appendix
Action - simulate-user-traffic-under-heavy-load
The action returned the following result:
{'status': 0, 'stderr': '', 'stdout': ''}
Probe - we-should-see-active-alerts
The probe returned the following result:
{'data': {'result': [{'metric': {'__name__': 'ALERTS',
'alertname': 'APIHighInvocationRate',
'alertstate': 'firing',
'function_name': 'astre',
'service': 'gateway',
'severity': 'major',
'value': '20.2'},
'value': [1523525986.758, '1']}],
7