The document discusses distributed systems and provides examples of distributed system use cases. It begins by defining distributed systems and noting some of their key characteristics and challenges. It then provides examples of distributed system use cases like processing sensor data, online stores, volunteer computing, and data storage. The document concludes by mentioning some distributed system technologies that have enabled these use cases and providing a timeline of distributed systems history and developments.
This is the keynote talk i delivered at GeekCamp.SG 2014
The main purpose of the talk is to create an awareness, if not existent, in the community when it comes to choosing and wanting to building a distributed system.
This presentation is not meant to be a survey of distributed computing through the ages but hopefully it serves as a good starting point in which the journeyman can start from.
I want to thank Jonas, CTO of Typesafe, as his work in Akka strongly influenced my own and i hope it would help you in the way his work helped me.
Distributed computing deals with hardware and software systems containing more than one processing element or storage element, concurrent processes, or multiple programs, running under a loosely or tightly controlled regime. In distributed computing a program is split up into parts that run simultaneously on multiple computers communicating over a network. Distributed computing is a form of parallel computing, but parallel computing is most commonly used to describe program parts running simultaneously on multiple processors in the same computer. Both types of processing require dividing a program into parts that can run simultaneously, but distributed programs often must deal with heterogeneous environments, network links of varying latencies, and unpredictable failures in the network or the computers.
Normally simple tasks like running a program or storing and retrieving data become much more complicated when we start to do them on collections of computers, rather than single machines. Distributed systems has become a key architectural concern, and affects everything a program would normally do. Using a series of examples all set in a coffee shop, we’ll explore distributed storage, computation, timing, communication, consensus, and even some distributed programming paradigms.
This is the keynote talk i delivered at GeekCamp.SG 2014
The main purpose of the talk is to create an awareness, if not existent, in the community when it comes to choosing and wanting to building a distributed system.
This presentation is not meant to be a survey of distributed computing through the ages but hopefully it serves as a good starting point in which the journeyman can start from.
I want to thank Jonas, CTO of Typesafe, as his work in Akka strongly influenced my own and i hope it would help you in the way his work helped me.
Distributed computing deals with hardware and software systems containing more than one processing element or storage element, concurrent processes, or multiple programs, running under a loosely or tightly controlled regime. In distributed computing a program is split up into parts that run simultaneously on multiple computers communicating over a network. Distributed computing is a form of parallel computing, but parallel computing is most commonly used to describe program parts running simultaneously on multiple processors in the same computer. Both types of processing require dividing a program into parts that can run simultaneously, but distributed programs often must deal with heterogeneous environments, network links of varying latencies, and unpredictable failures in the network or the computers.
Normally simple tasks like running a program or storing and retrieving data become much more complicated when we start to do them on collections of computers, rather than single machines. Distributed systems has become a key architectural concern, and affects everything a program would normally do. Using a series of examples all set in a coffee shop, we’ll explore distributed storage, computation, timing, communication, consensus, and even some distributed programming paradigms.
Data, Big Data and real time analytics for Connected DevicesSrinath Perera
Internet of things paints a vivid picture of a possible reality that is both fascinating and imposing. However, few talk about the sensing and decision making infrastructure--the brain--that must be present with those devices. Underline decision framework needs to collect data, analyze them, compare and contrast with all data, and draw conclusions and arrive at decisions before humans at the other end notice the lag.
In talk will start with IoT reference architecture and will discuss Complex Event Processing (CEP) coupled with Lambda architecture as a underline decision framework for underline IoT scenario while drawing examples from several real-world scenarios. You will learn about design choices in building an IoT architecture, CEP, Hive, and Lambda architecture.
Topics to be covered:
The relationship between IoT and data, big data, and real-time analytics
Design choices in building an IoT architecture, CEP, Hive, and Lambda architecture
An Architecture for Privacy-Sensitive Ubiquitous Computing at Mobisys 2004Jason Hong
Some older research I did looking at one way of building privacy-sensitive apps for ubiquitous computing environments. The core idea is to focus on locality, where all of the data is sensed and processed locally as much as possible.
Privacy is the most often-cited criticism of ubiquitous computing, and may be the greatest barrier to its long-term success. However, developers currently have little support in designing software architectures and in creating interactions that are effective in helping end-users manage their privacy. To address this problem, we present Confab, a toolkit for facilitating the development of privacy-sensitive ubiquitous computing applications. The requirements for Confab were gathered through an analysis of privacy needs for both end-users and application developers. Confab provides basic support for building ubiquitous computing applications, providing a framework as well as several customizable privacy mechanisms. Confab also comes with extensions for managing location privacy. Combined, these features allow application developers and end-users to support a spectrum of trust levels and privacy needs.
Authors are Jason Hong and James Landay
Slides for a presentation given at a Fortum's internal big data event. The presentation drafts out a journey from big data through sensor data in general into automated use of cutting edge sensors, drones and data technology in inspecting infrastructure.
Lecture 3 in the COMP 4010 course on Augmented and Virtual Reality taught at the University of South Australia. This lecture was taught by Bruce Thomas on August 13th 2019
Collecting big data in cinemas to improve recommendation systems - a model wi...ICDEcCnferenece
Kristian Dokic, Domagoj Sulc and Dubravka Mandusic. Collecting big data in cinemas to improve recommendation systems - a model with three types of motion sensors. (ICDEc 2021)
Citron : Context Information Acquisition Framework on Personal DevicesTetsuo Yamabe
Tetsuo Yamabe, Ayako Takagi, and Tatsuo Nakajima. 2005. Citron: A Context Information Acquisition Framework for Personal Devices. In Proceedings of the 11th IEEE international Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’05, full paper)
Real-time, Sensor-based Monitoring of Shipping Containersbenaam
This presentation describes a sensor-based solution for real-time monitoring of high-value assets in-transit so shippers can react quickly to unplanned events such as delays, cargo damage, and even thefts.
Selected as one of the best presentations at the 2012 Vail Computer Elements Workshop. For 42 years, this 4-day workshop has served leading architects of the computer industry. The agenda is 100% invited technical talks and the audience is mostly previous speakers.
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
Data, Big Data and real time analytics for Connected DevicesSrinath Perera
Internet of things paints a vivid picture of a possible reality that is both fascinating and imposing. However, few talk about the sensing and decision making infrastructure--the brain--that must be present with those devices. Underline decision framework needs to collect data, analyze them, compare and contrast with all data, and draw conclusions and arrive at decisions before humans at the other end notice the lag.
In talk will start with IoT reference architecture and will discuss Complex Event Processing (CEP) coupled with Lambda architecture as a underline decision framework for underline IoT scenario while drawing examples from several real-world scenarios. You will learn about design choices in building an IoT architecture, CEP, Hive, and Lambda architecture.
Topics to be covered:
The relationship between IoT and data, big data, and real-time analytics
Design choices in building an IoT architecture, CEP, Hive, and Lambda architecture
An Architecture for Privacy-Sensitive Ubiquitous Computing at Mobisys 2004Jason Hong
Some older research I did looking at one way of building privacy-sensitive apps for ubiquitous computing environments. The core idea is to focus on locality, where all of the data is sensed and processed locally as much as possible.
Privacy is the most often-cited criticism of ubiquitous computing, and may be the greatest barrier to its long-term success. However, developers currently have little support in designing software architectures and in creating interactions that are effective in helping end-users manage their privacy. To address this problem, we present Confab, a toolkit for facilitating the development of privacy-sensitive ubiquitous computing applications. The requirements for Confab were gathered through an analysis of privacy needs for both end-users and application developers. Confab provides basic support for building ubiquitous computing applications, providing a framework as well as several customizable privacy mechanisms. Confab also comes with extensions for managing location privacy. Combined, these features allow application developers and end-users to support a spectrum of trust levels and privacy needs.
Authors are Jason Hong and James Landay
Slides for a presentation given at a Fortum's internal big data event. The presentation drafts out a journey from big data through sensor data in general into automated use of cutting edge sensors, drones and data technology in inspecting infrastructure.
Lecture 3 in the COMP 4010 course on Augmented and Virtual Reality taught at the University of South Australia. This lecture was taught by Bruce Thomas on August 13th 2019
Collecting big data in cinemas to improve recommendation systems - a model wi...ICDEcCnferenece
Kristian Dokic, Domagoj Sulc and Dubravka Mandusic. Collecting big data in cinemas to improve recommendation systems - a model with three types of motion sensors. (ICDEc 2021)
Citron : Context Information Acquisition Framework on Personal DevicesTetsuo Yamabe
Tetsuo Yamabe, Ayako Takagi, and Tatsuo Nakajima. 2005. Citron: A Context Information Acquisition Framework for Personal Devices. In Proceedings of the 11th IEEE international Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’05, full paper)
Real-time, Sensor-based Monitoring of Shipping Containersbenaam
This presentation describes a sensor-based solution for real-time monitoring of high-value assets in-transit so shippers can react quickly to unplanned events such as delays, cargo damage, and even thefts.
Selected as one of the best presentations at the 2012 Vail Computer Elements Workshop. For 42 years, this 4-day workshop has served leading architects of the computer industry. The agenda is 100% invited technical talks and the audience is mostly previous speakers.
Book: Software Architecture and Decision-MakingSrinath Perera
Uncertainty is the leading cause of mistakes made by practicing software architects. The primary goal of architecture is to handle uncertainty arising from user cases as well as architectural techniques. The book discusses how to make architectural decisions and manage uncertainty. From the book, You will learn common problems while designing a system, a default solution for each, more complex alternatives, and 5Q & 7P (Five Questions and Seven Principles) that help you choose.
Book, https://amzn.to/3v1MfZX
Blog: http://tinyurl.com/swdmblog
Six min video - https://youtu.be/jtnuHvPWlYU
We have critically evaluated how AI will shape integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study.
We observe that AI can significantly impact integration use cases and identify 13 AI-based use case classes for integration. Points to note include:
Enabling AI in an enterprise involves collecting, cleaning up, and creating a single representation of data as well as enforcing decisions and exposing data outside, each of which leads to many integration use cases. Hence, AI indirectly creates demand for integration.
AI needs data, which in some cases lead to significant competitive advantages. The need to collect data would drive vendors to offer most AI products in the cloud through APIs.
Due to lack of expertise and data, custom AI model building will be limited to large organizations. It is hard for small and medium size organization to build and maintain custom models.
The Role of Blockchain in Future IntegrationsSrinath Perera
We have critically evaluated blockchain-based integration use cases, their feasibility, and timelines. Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, is the methodology of our study. Based on our analysis, we observe that blockchain can significantly impact integration use cases.
In our paper, we identify 30-plus blockchain-based use cases for integration and four architecture patterns. Notably, each use case we identified can be implemented using one of the architecture patterns. Furthermore, we also discuss challenges and risks posed by blockchains that would affect these architecture patterns.
Our webinar presents a critical analysis of serverless technology and our thoughts about its future. We use Emerging Technology Analysis Canvas (ETAC), a framework built to analyze emerging technologies, as the methodology of our study. Based on our analysis, we believe that serverless can significantly impact applications and software development workflows.
We’ve also made two further observations:
Limitations, such as tail latencies and cold starts, are not deal breakers for adoption. There are significant use cases that can work with existing serverless technologies despite these limitations.
We see a significant gap in required tooling and IDE support, best practices, and architecture blueprints. With proper tooling, it is possible to train existing enterprise developers to program with serverless. If proper tools are forthcoming, we believe serverless can cross the chasm in 3-5 years.
A detailed analysis can be found here: A Survey of Serverless: Status Quo and Future Directions. Join our webinar as we discuss this study, our conclusions, and evidence in detail.
1. Blockchain potential impact is real. If successful, Blockchain technologies can transform the way we live our day to day lives.
2. We believe technology is ready for limited applications in Digital Currency, Lightweight financial systems, Ledgers (of identity, ownership, status, and authority), Provenance (e.g. supply chains and other B2B scenarios) and Disintermediation, which we believe will happen in next three years.
3. However, with other use cases, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. These are hard problems and
4. It is not clear whether blockchain can sustain the current level of effort for extended period of 5+ years. There are many startups and they run the risk of running out of money before markets are ready. Failure of startups can inhibit further funding and investments.
5. Value and need of decentralization compared to centralized and semi-centralized alternatives is not clear.
A Visual Canvas for Judging New TechnologiesSrinath Perera
In the fast-changing technology world, the technology landscape shifts faster and faster. The agents of thses changes are new emerging technologies, which sometimes even create, destroy, or transform segments. In a shifting world, prevailing advantages are fleeting. Organizations that can master change and ride technology waves owns the future.
Not all emerging technologies live up to their promise. Every year, as a part of annual planning, most organizations need to decide relevance, impact, and the probability of success of emerging technologies and pick their bets. Although it is a regular decision there is no widely accepted framework for evaluating emerging technologies.
As a solution to this problem, we present “Emerging Technology Analysis Canvas” (ETAC), a framework to assess an individual emerging technology as a solution to this problem. Inspired by the Business Model Canvas, It represents different aspects of technology visually on a single page. This approach includes a set of questions that probe the technology arranged around a logical narrative. The visual representation is concise, compact, and comprehensible in a glance.
The talk discusses how analytics can attack privacy and what we can do about it. It discusses the legal responses (e.g. GDPR) as well technical responses ( differential privacy and homomorphic encryption).
The video is in https://www.facebook.com/eduscopelive/videos/314847475765297/ from 1.18.
Blockchain is often cited as one of the most impactful technology along with AI. It has attracted many startups, venture investments, and academic research. If successful, Blockchain technologies can transform the way, we live our day to day lives.
However, blockchain faces significant challenges such as performance, irrevocability, need for regulation and lack of census mechanisms. They are hard problems, and likely it will take at least 5-10 years to find answers to those problems.
Given the risk involved as well as the significant potential returns, we recommend a cautiously optimistic approach for blockchain with the focus on concrete use cases.
Today's Technology and Emerging Technology LandscapeSrinath Perera
We have seen the rise and fall of many technologies, some disappearing without a trace while others redefining the world. Collectively they have shaped our world beyond recognition. In this talk, Srinath will start with past technologies exploring their behavior. Then he will explore current middleware landscape, its composition, and relationships between different segments. He will discuss significant developments and discuss their future. Further, he will discuss emerging technologies, forces that shape them, and the promise of each technology, and finally, speculate about their evolution. You will walk away with knowledge on the evolution of middleware, the status quo, and discussion about how, at WSO2, we think those technologies will evolve.
Some died, some get by, but some have woven themselves to today's middleware so much that we do not notice them. The point I want to make is that not all emerging technologies are fads. Some are, and some are too early, like AI. But some are lasting.
The Rise of Streaming SQL and Evolution of Streaming ApplicationsSrinath Perera
First-generation stream processors, such as Apache Storm, wanted us to write code. It was a great start. However, when building real-world apps, which are used for a long time and evolve, writing code gets us into trouble.
If we want to query a database or query data stored in storage with Hadoop, we use SQL. Why can't we query data streaming using SQL? We can. Almost all open source stream processors, including Storm, Flink, and Kafka, have switched to SQL.
In this webinar, Srinath will talk about the evolution of stream processing, streaming SQL, the status quo, and what this means to stream applications. He will also dissect the experience of building streaming applications by exploring common patterns and pitfalls.
Analytics and AI: The Good, the Bad and the UglySrinath Perera
Analytics let us question the data, which in effect questions the world around us. This let us understand, monitor, and shape the world. AI let us discover connections, predict the possible futures and automate tasks.
These twin technologies can change the world around us. On one hand, make us efficient, connected, and fulfilled. At the same time, the change of status quo can replace jobs, affect lives and build biases into our systems that can marginalize millions.
In this talk, we will discuss core ideas behind analytics and AI, their possible impact, both good and bad outcomes, and challenges.
The dawn of digital businesses is upon us, with reimagined business models that make the best use of digital technologies such as automation, analytics, integration and cloud. Digital businesses are efficient, continuously optimizing, proactive, flexible and are able to fully understand their customers. Analytics is a key technology that helps in doing so. It acts as the eyes and ears of the system and provides a holistic view on the past and present so that decision-makers can predict what will happen in the future. This webinar will explore
Why becoming a digital business is not a choice
The role of analytics in digital transformation with examples
How best to leverage state of the art analytics technology
SoC Keynote:The State of the Art in Integration TechnologySrinath Perera
This talk discusses Outline of the state of the art of Enterprise Software and how we get there, as I see it. Also second part describes Ballerina, a new programming language WSO2 has built for Enterprise Computing.
It is presented as a Keynote at 11th Symposium and Summer School On Service-Oriented Computing.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Generating a custom Ruby SDK for your web service or Rails API using Smithy
Keynote for CSE conference 2011: Distributed Systems: What? Why? And bit of How?
1.
2. I cannot cover
Distributed Systems
in 30 minutes!
But, I can tell why
you might want to
learn Distributed
Systems in 30
minutes!
http://www.flickr.com/photos/uwehermann/82753155/sizes/m/in/photostream/ and
http://www.flickr.com/photos/peterpearson/5921765552, licensed under CC
3. What is a Distributed System?
"A distributed system is
one on which I cannot get
any work done because
some machine I have
never heard of has
crashed.“
--Leslie Lamport
4. What is a Distributed System?
“A system in which hardware or “A distributed system is a
software components located
collection of independent
at networked computers
communicate and coordinate
computers that appear to the
their actions only by message users of the system as a single
passing.” - [Coulouris] coherent system.” - [Tanenbaum]
5. Characteristics and Challenges
• No Global Clock • Fault
Tolerance
• Communication
• Scale
only by message • Transparenc
Passing
• No Global State
• Independent
Failures
Photo by John Trainoron Flickr
http://www.flickr.com/photos/trainor/2902023575/, Licensed u
6. Fallacies of Distributed Systems
• The network is reliable. • There is one
• Latency is zero. administrator.
• Bandwidth is infinite. • Transport cost is zero.
• The network is secure. • The network is
• Topology doesn't change. homogeneous.
http://www.flickr.com/photos/12587661@N06/2300406685, @Michael Gwyther-Jones, L
7. Why Distributed Systems
• Need to build bigger systems
• Many usecases are inherently distributed
• To avoid failures
• Omnipresence
– if you buy food from a super market
– If you buy a book from a Bookshop Chain
– If you search in the Web
– If you use a GPS navigator
– If you turn on your My 10 list
– If you pay a bill
– If you use your mobile App
8. A System Usecase Classification
• Processing Data
(Moving vs. Stored
Data)
• Servers: Receive,
Process, and Respond
• Running User provided
Jobs
• Data Storages and
Provenance
http://www.flickr.com/photos/kelsea-groves/5535666329/
9. Usecase: Processing Data: React to Sensors
• Many sensors: Weather, Travel, Traffic, Surveillance, Stock
exchange, Smart Grid, Production line
• Monitor, understand, and react to events
• Usually handled with CEP (e.g. Esper, Stream Base, Siddhi) or
Stream Processing (S4, Twitter Stream)
http://www.flickr.com/photos/imuttoo/4257813689/ by Ian
Muttoo, http://www.flickr.com/photos/eastcapital/4554220770/, http://www.flickr.com/photos/patdavid/4619331472/ by Pat David
copyright CC
10. Usecase: Processing Data: Target Marketing
• Receive data about users continuously: e.g. web
clicks, what they brought, what they liked and do not
like, what their friends like and brought
• Build models, index information in the background
• Send him advertisements that best matches his
preferences
– have to do this quickly
– in few (say 50) milliseconds
• Cloud be the next billion dollar problem
11. Usecase: Receive, Process, and Respond:
Online Store (e.g. Amazon)
• Many Sellers selling
many items and
Many Byers
• List of all items,
with their specs
• Index items by
many dimensions
and support search
• Support checkout, track the delivery, returns, ratings, and
complains
• Supported by partitioning sellers/ items across many nodes
12. Usecase: Running User Provided Jobs :
SETI@Home
• Many people volunteer
their computing power
• Scientists submit
computing jobs to the
system
• Broker and match
resources with jobs, run
them and return results.
Handle failures. Avoid
free riding.
• Considered biggest
computer in earth (505
TFLOPS, 150k active
computers)
http://www.elfwood.com/~axthony/Staring-Aliens.2552052.html, Licensed CC
13. Usecase: Data Storages and Provenance
(Sky Server)
• Telescopes (Square Kilometer
Array) keep collecting data from
the sky (Tera bytes per day)
• Sky Server let scientists to come
and see the sky of a given
location, as seen at a given
time.
• Moving data takes long time.
1TB takes
– 100 Mbps network : 30 hrs
– 1 Gbps network : 3 hrs
– 10 Gbps network : 20 minutes
• Given a data item, need to track
how it is created, equipment
accuracy, transformations used
http://www.fotopedia.com/items/flickr-518876976 and
etc.
http://www.geograph.org.uk/photo/103069, Licensed CC
14. Mobile Sensor Crowdsourcing
• Mobile phones are now like a
weather center: has
– a barometer
– temperature sensor
– proximity sensor
– GPS
– moisture sensor
• Get volunteer phones to send
sensor data (Crowd source).
– report on weather
– crop diseases (agriculture
officials)
– epidemics (from hospitals,
doctors)
• Use that to do weather
predications, crop disease and
http://www.fotopedia.com/items/flickr-2548697541 , epidemic spread
http://www.geograph.org.uk/photo/1534209, and
http://www.yourbdnews.com/2011/10/17/samsung-files-to-halt-iphone- • Moving Sensors (Polar Grid)
4s-in-japan-australia/iphone-4s, Licensed CC
15. Great! lets see what
Distributed System
technologies have made these
use cases possible!!
16. Distributed Systems Timeline/History
Period Topics
1965-late 70s Parallel Programming, Self Stabilization, Fault Tolerance, ER Model/
Transactions, Time Clock
1980s Consensus and impossibility, SQL, Distributed Snapshots,
Replications, Group Communication
Early 90s Linearizability, Parallel DB, transactional Memory, RAID, MPI
Late 90s Volunteer Computing, P2P file sharing, Complex event processing
Early 2000 Oceanostore, Web Services, Symantec Web, REST, DHT, Pub/Sub,
Grid, Autonomic Computing, Google File System, Virtualization, SOA,
Map reduce
2005-2010 Cloud, NoSQL, Mobile Apps, Data Provenance
17. Theoretical Computer Science
• Concerns with
– Coordination algorithms:
Leader Election, multi-cast,
distributed locks, barriers,
snapshot algorithms
– Impossibility results, upper
and lower bounds
– Distributed versions of some
centralized algorithms (e.g.
shortest path)
– Lot of work done on 70s,
and layed the ground work
for Distributed Systems
http://www.flickr.com/photos/lodz_na_nowo/5690492370/
http://xkcd.com/384/
http://www.flickr.com/photos/quinnanya/4990131194/sizes/z/in/photostream/
, Licensed CC
23. Building Scalable Systems
• Single Machine
• Shared Memory
Model
• Clustering (State
Replication
through group
communication)
• Shard Nothing
• Loose Consistency
with Shared
nothing http://www.fotopedia.com/items/louromig-8P4w6xtSgbY, Licensed CC
24. Publish Subscribe and EDA
• Many publishers send events
• Subscribers register events, and a
publish/subscribe network match and redirect
events
• Have scalable implementations
• Basis for event driven architectures
25. Cloud Computing
• Ability to buy computations
power, storage, or execution
services as an Utility, on demand.
• Best way to explain it is by
comparing it to Electricity
• Idea is a big pool of servers and
share.
• Economics of scale through Optimize
large scale operations.
• Resource Pooling.
• No need for capacity planning, start
small and grow as needed.
• Outsource and enabling
specialization.
photo by LoopZilla on Flickr,
http://www.flickr.com/photos/loopzilla/2328231843/sizes/m/in/photostre
27. If You Plan to Learn about Distributed
Systems
• One of the fields to learn by
doing
• You have to be a good
programmer
– a patient one (Debugging)
– Lazy one (but intelligent)
• Start by writing some Web
Services, request response stuff
• Stop reinventing the wheel, start
using tools (middleware)
• Learn Zookeeper
• Take a class – read, write code,
debug, ..
http://www.flickr.com/photos/mariachily/5250487136,
Licensed CC
28. Distributed System Community
• Based around ACM, IEEE, and USENIX
• Well known journals
– IBM System journal, ACM Operating Systems Review,
ACM Transactions on Computer Systems, IEEE
Distributed Systems Online, IEEE Transactions on
Parallel and Distributed Systems
• Conferences
– Theory: ICDCS, SPDC
– SOA/Cloud : ICWS
– E-Science, Parallel Programming : HPDC, SC, E-
Science, Ccgrid
– Systems : USENIX, Middleware, ACM Symposium on
Operating Systems Principles, FAST, LISA, OSDI
– DB : Sigmoid record, VLDB
• Awards
– Turing Award
– Edsger W. Dijkstra Prize in Distributed Computing
http://www.flickr.com/photos/dullhunk/4187914071, http://www.foto
pedia.com/items/flickr-1544709148, Licensed CC
29. Few Must Read Papers
• System Structure for Software Fault Tolerance (1975)
• Reaching Agreement in the Presence of Faults (1980)
• Time, Clocks, and the Ordering of Events in a Distributed System (1978)
• Reaching agreement in the presence of faults(1980) and The Byzantine
generals problem” (1982),
• End-to-End Arguments in System Design (1984)
• A Note on Distributed Computing (1994)
• Scale in Distributed Systems, (1994)
• The Google File System (2003)
• Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications,
(2001)
• The Google file system (2003)
• Xen and the Art of virtualization (2003)
• MapReduce: Simplified Data Processing on Large Clusters (2004)
30. Some Open Challenges
• Every thing Data: Analytics, AI,
Data Mining (Distributed
versions of many algorithms)
• Complex Event Processing
(CEP)
• How to Scale?
• Middleware for the Cloud
• Scalable Storage
• Provenance
• Workflows
• Guard against DDoS and other http://www.flickr.com/photos/brianscott/5474210001,
Distributed Security Issues Licensed CC