This document discusses cloud computing and the Meandre framework. It provides an overview of cloud concepts like public/private clouds and IaaS, PaaS, SaaS models. It describes NCSA's use of virtual machines and Eucalyptus cloud. Meandre is presented as a component-based framework that can orchestrate data-intensive applications across cloud resources through its dataflow model and scripting language. It aims to facilitate scaling applications to leverage elastic cloud infrastructure and integrate computation and data.
Netflix on Cloud - combined slides for Dev and OpsAdrian Cockcroft
This document contains slides from a presentation given by Adrian Cockcroft on Netflix's use of cloud computing on Amazon Web Services (AWS). The summary includes:
1) Netflix moved most of its infrastructure to AWS to leverage AWS's scale and features rather than building its own datacenters, as capacity growth was unpredictable and datacenters were inflexible.
2) Netflix uses many AWS services including EC2, S3, EBS, EMR and more. It deployed a large movie encoding farm on EC2, stores content on S3, uses EMR/Hadoop for log analysis, and a CDN for content delivery.
3) Netflix has learned that cloud tools don't always scale for large
The document discusses how Acquia Search improves upon Drupal's core search capabilities. It provides an overview of Acquia Search, highlighting features like faceted navigation, content recommendations, sorting results by relevance, and searching across multiple sites. Performance tests show Acquia Search returns results 3 to 16 times faster than Drupal core search. The document also outlines how Acquia Search works and is packaged and priced and compares managing Solr yourself versus using Acquia Search.
This document provides an overview of a tutorial on Apache CloudStack. It outlines 3 sessions on introducing CloudStack, its architecture, and hands-on with DevCloud. Session 1 defines cloud computing and introduces CloudStack as an open-source orchestration platform for delivering infrastructure as a service clouds. It describes CloudStack's history and how to contribute to the project.
Service-Oriented Design and Implement with Rails3Wen-Tien Chang
The service implements a RESTful Users web service with Rails 3. It customizes Rails to remove unnecessary components and optimize for a lightweight REST service. The service follows best practices for API design including using JSON format, placing JSON conversion in the controller, and returning appropriate HTTP status codes. Requests are designed to be stateless and atomic. Errors are returned in a standardized JSON format.
Node.js (Node) , the brain child of Ryan Dahl, was released in 2009 when he worked for Joyent, Inc. Node is one of the most hyped technologies to arrive on the web development scene, though it is also one of the most misunderstood.
So what is Node? Is it a programming language like Python, Java, or C++? Is it an application framework like Django, Rails, or Symphony? Is it maybe some type of middleware that can be plugged into existing web stacks like Memcached or RabbitMQ? Actually, it is none of the above. Node is simply a set of JavaScript language bindings to Google's powerful V8 engine. This begs the question: "what is a language binding and what is V8?"
This presentation introduces Node from an architectural perspective by discussing its implementation followed by a practical demonstration of how to build an application using it through a real-world example. Michael Filbin of Aspenware explains how Ryan liberated JavaScript from the browser and brought the power of event-driven, non-blocked programming to every developer by using the world's most popular programming language.
CloudStack is an open source cloud computing platform that allows users to manage their infrastructure as an automated system. It provides self-service access to computing resources like servers, storage, and networking via a web interface. CloudStack supports multiple hypervisors and public/private cloud deployment strategies. The core components include hosts, primary storage, clusters, pods, networks, secondary storage, and zones which are managed by CloudStack servers.
This talk lays out the elements of an extension including the content model, JS API, Web Scripts, Content Policies, Action Executors, Web Scripts and more. This will draw on years of experience delivering extensions to various projects.
There is a code sample in github: https://github.com/rmknightstar/devcon2018
You can see the presentation as given at the Alfresco Developer Conference here : https://youtu.be/CKRswhh-jHE?list=PLyJdWuUHM3igOUt49uiFqs-6DCQAgJ1vs&t=0
OpenStack is an open source cloud computing platform that consists of several components including Keystone for identity, Glance for images, Nova for compute, Cinder for block storage, and Quantum for networking. The document provides an overview of each component, describing their main functions and how they interact through messaging queues like RabbitMQ. It also describes the original "Nova networking" approach and how Quantum improved on this by decoupling logical and physical networking and providing plugins to support technologies like Open vSwitch.
1. The document provides an overview of Windows Azure offerings including Compute, Storage, SQL Azure, Virtual Network, AppFabric, and Marketplace.
2. It discusses the "7 Deadly Sins of Cloud Development" including under utilization of cloud resources, platform monogamy, poorly defined release cadence, always connected assumptions, synchronous application design, lack of load/failover testing, and lack of cloud reading.
3. The document includes demos of various Windows Azure features to illustrate how to avoid the sins.
Latest version of the Netflix Cloud Architecture story was given at Gluecon May 23rd 2012. Gluecon rocks, and lots of Van Halen references were added for the occasion. There tradeoff between developer driven high functionality AWS based PaaS, and operations driven low cost portable PaaS is discussed. The three sections cover the developer view, the operator view and the builder view.
The document introduces Apache CloudStack, an open source cloud computing platform that allows users to build Infrastructure as a Service (IaaS) clouds. It discusses how CloudStack can be used to create VMs, disks, networks and other services with self-service access and usage metering. The document also compares how CloudStack and Amazon EC2 are architected, highlighting CloudStack's ability to support both traditional and cloud-era workloads across multiple availability zones with an object storage backend.
Growing in the Wild. The story by CUBRID Database Developers.CUBRID
The presentation the CUBRID team presented at Russian Internet Technologies Conference in 2012. The presentation covers such questions as *WHY* CUBRID was developed, *WHY* the developers did not fork existing solutions, *WHY* it was necessary to develop a new RDBMS from scratch, and *HOW* CUBRID Database was evolved over the years.
The document summarizes CloudStack architecture plans for the future. It discusses moving to management server clusters per availability zone rather than per region. It also discusses using an object storage system for templates and snapshots rather than a separate NFS server. Finally, it discusses a possible future model where CloudStack manages existing virtualization clusters rather than deploying and managing its own system VMs.
The document discusses the REST (REpresentational State Transfer) architectural style and how it differs from traditional SOA (Service Oriented Architecture). It outlines the key principles of REST including addressing resources with URIs, using a uniform interface (HTTP methods), stateless communication, and hypermedia as the driver of application state. The document explains how the web exemplifies REST through the use of URLs, HTTP, and standard formats to access and transfer representations of resources.
Windows Phone 7 and Windows Azure – A Match Made in the CloudMichael Collier
Windows Phone 7 and Windows Azure are a good match because they both provide easy and familiar development environments, connectivity through the cloud, and scalability. They are compatible in these areas. The document discusses how Windows Phone 7 and Windows Azure can be used together through features like data storage in Windows Azure tables and blobs, push notifications, and identity management with Access Control Services. It provides examples of how to integrate the platforms for storing, retrieving, and displaying data stored in the cloud.
NoSQL and SQL - Why Choose? Enjoy the best of both worlds with MySQLAndrew Morgan
Theres a lot of excitement around NoSQL Data Stores with the promise of simple access patterns, flexible schemas, scalability and High Availability. The downside comes in the form of losing ACID transactions, consistency, flexible queries and data integrity checks. What if you could have the best of both worlds? This session shows how MySQL Cluster provides simultaneous SQL and native NoSQL access to your data whether a simple key-value API (Memcached), REST, JavaScript, Java or C++. You will hear how the MySQL Cluster architecture delivers in-memory real-time performance, 99.999% availability, on-line maintenance and linear, horizontal scalability through transparent auto-sharding.
2012 CloudStack Design Camp in Taiwan--- CloudStack Overview-1tcloudcomputing-tw
CloudStack is an open source cloud orchestration platform that allows users to provision infrastructure as a service (IaaS) clouds. It supports multiple hypervisors and cloud deployment strategies. Key features include self-service VM provisioning, monitoring of consumed resources, volume and snapshot management, and network services like load balancing and firewall rules. CloudStack uses a multi-tenant architecture with logical abstractions like zones, pods, clusters, and hosts to manage the underlying physical infrastructure.
Clould Computing and its application in LibrariesAmit Shaw
Cloud computing offers several potential benefits for libraries, including lower costs, increased storage capacity, improved mobility and access, and more flexible workflows. Key aspects of cloud computing include deployment models like private, public and hybrid clouds. Issues include security, data ownership, and lack of control. Recent trends include the use of cloud-based library services and products, as well as research into cloud computing architectures and management. Overall, cloud computing can help libraries modernize services in a cost-effective manner.
Deployment of private cloud infrastructure copyprabhat kumar
The document discusses deploying a private cloud infrastructure using open source software like OpenStack and MostlyLinux. It would create a cost-effective private cloud architecture as an alternative to proprietary solutions. The summaries would provide high-level overviews of key sections in 3 sentences or less.
Introduction to Azure fundamentals of cloud.pptxNadir Arain
This document provides an overview of cloud computing and its key concepts. It discusses the main types of cloud services including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It also covers the major cloud providers Azure and OpenStack and provides examples of common cloud use cases like web and mobile applications, big data analytics, and online storage.
Cloud computing relies on core technologies like distributed computing, virtualization, service orientation, and web 2.0. It provides on-demand access to configurable computing resources like networks, servers, storage, applications and services over the internet. Applications that benefit from cloud include web applications for their varying workload demands and resource-intensive applications for their temporary large resource needs. Popular cloud platforms discussed are Amazon Web Services, Google App Engine, Microsoft Azure, Hadoop, and Force.com which provide infrastructure, platform and application services through web interfaces.
Cloud computing allows users to access applications and store and access data over the Internet instead of locally on personal devices. It offers on-demand services that are available anywhere, anytime through centralized data centers. This technology improves efficiency by centralizing storage, memory, processing and bandwidth. The major cloud service models are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Cloud services can be deployed in public, private or hybrid cloud models depending on security and control needs.
The document provides an overview of cloud computing including its popularity, definitions, benefits, key technology drivers like virtualization and SOA, top cloud providers like Amazon and Google, different cloud services and types, challenges, and real-world case studies demonstrating benefits like cost savings and faster deployment times.
General discussions
Why cloud?
The terminology: relating virtualization and cloud
Types of Virtualization and Cloud deployment model
Decisive factors in migration
Hands-on cloud deployment
Cloud for banks
This document provides an overview of cloud computing concepts including:
- A brief history of cloud computing from early centralized computing to modern cloud models.
- Key characteristics of cloud computing including on-demand access, no upfront commitments, and pay-per-use pricing.
- Examples of different cloud service models including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
- Virtualization is discussed as a fundamental concept enabling cloud computing by allowing centralized resources to serve multiple users.
The document discusses cloud computing, including definitions, common attributes, service layers, implementation types, trends, and applications. It defines cloud computing as IT capabilities provided over the internet, including massively scalable computing resources. Key aspects include pooled computing resources, elastic scaling, flexible pricing, and resources delivered as a service. The document outlines common cloud service layers of SaaS, PaaS, and IaaS and provides examples. It also discusses private, public, and hybrid cloud implementation types and emerging trends in cloud adoption.
This document discusses cloud computing and its potential applications and benefits for libraries. It begins by defining cloud computing as the delivery of computing resources such as storage, software, and processing over a network. It then outlines different cloud service models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Examples of each service model are provided. The document also discusses characteristics of cloud computing such as rapid elasticity, resource pooling, and pay-per-use billing. Potential uses for libraries are suggested, such as using cloud services for storage or hosting applications like WordPress or Omeka. Security concerns with cloud services are also briefly addressed.
The document provides an overview of the evolution of cloud computing from its roots in mainframe computing, distributed systems, grid computing, and cluster computing. It discusses how hardware virtualization, Internet technologies, distributed computing concepts, and systems management techniques enabled the development of cloud computing. The document then describes several early technologies and models such as time-shared mainframes, distributed systems, grid computing, and cluster computing that influenced the development of cloud computing.
Cloud computing involves delivering computing resources over a network, typically the Internet. It dates back to the 1950s but demand increased due to the need to make things easier and save on costs. By 2020, the cloud computing market is forecast to exceed $241 billion. There are three main deployment models - public cloud (external cloud), private cloud (internal cloud), and hybrid cloud (combination of both). The three main service models are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). While cloud computing provides benefits like flexibility, cost savings, and scalability, there are also potential risks regarding security, ownership of data, and dependence on major providers.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services available over the internet. It has three main types of service models - Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). IaaS provides basic storage, networking and computing resources, PaaS provides development tools and environments for building applications, and SaaS provides users access to applications over the internet. The document discusses these service models and their examples in more detail.
Cloud computing is the delivery of computing resources such as servers, storage, databases, networking, software, analytics and more over the Internet ("the cloud"). It enables companies to scale up as needed and pay only for resources used. Key aspects include virtualization, utility computing, and service-oriented architecture. Details are abstracted from consumers, who no longer need expertise in or control over the underlying technology infrastructure. Cloud computing provides dynamically scalable and often virtualized resources delivered over the Internet.
Virtualization allows multiple virtual servers to run on the same physical hardware by encapsulating the server software. In cloud computing, virtualization is extended by allowing users to rent hardware resources from cloud providers on demand, without having to purchase and manage their own physical servers. This enables rapid deployment of applications and scaling of resources. Key benefits include avoiding upfront capital costs, paying only for resources used, and gaining location and device independence through virtual servers that can be accessed from anywhere.
Lino Telera gave a presentation on serverless computing. He began with introductions and background. The presentation covered serverless concepts like Function as a Service, demonstrated building a simple microservice using AWS Lambda that interacts with S3, and discussed integrating functions with services like S3 using Boto. It also showed how functions can be called from devices using skills and discussed running serverless on-premise using OpenFaaS or Pivotal Container Service. The presentation concluded with a Q&A and thanks to sponsors.
This document discusses cloud computing. It begins with an introduction and overview of essential cloud characteristics, service models, deployment models, architecture, and underlying components. It then discusses key research challenges in cloud computing. The document provides definitions of cloud computing and outlines the advantages of the cloud model compared to traditional internal IT or managed service models. It also diagrams the different cloud service models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).
From Galapagos to Twitter: Darwin, Natural Selection, and Web 2.0Xavier Llorà
One hundred and fifty years have passed since the publication of Darwin's world-changing manuscript "The Origins of Species by Means of Natural Selection". Darwin's ideas have proven their power to reach beyond the biology realm, and their ability to define a conceptual framework which allows us to model and understand complex systems. In the mid 1950s and 60s the efforts of a scattered group of engineers proved the benefits of adopting an evolutionary paradigm to solve complex real-world problems. In the 70s, the emerging presence of computers brought us a new collection of artificial evolution paradigms, among which genetic algorithms rapidly gained widespread adoption. Currently, the Internet has propitiated an exponential growth of information and computational resources that are clearly disrupting our perception and forcing us to reevaluate the boundaries between technology and social interaction. Darwin's ideas can, once again, help us understand such disruptive change. In this talk, I will review the origin of artificial evolution ideas and techniques. I will also show how these techniques are, nowadays, helping to solve a wide range of applications, from life science problems to twitter puzzles, and how high performance computing can make Darwin ideas a routinary tool to help us model and understand complex systems.
Large Scale Data Mining using Genetics-Based Machine LearningXavier Llorà
We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them.
This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for GBML methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms. We will also review a topic interlaced with all of them: how can we model the scalability of the components of our GBML systems to better engineer them to get the best performance out of them for large datasets. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study us...Xavier Llorà
Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases (selectorecombinative genetic algorithms and estimation of distribution algorithms) are presented, analyzed, and discussed. This study shows that equivalent data-intensive computing evolutionary computation algorithms can be easily developed, providing robust and scalable algorithms for the multicore-computing era. Experimental results show how such algorithms scale with the number of available cores without further modification.
Scalabiltity in GBML, Accuracy-based Michigan Fuzzy LCS, and new TrendsXavier Llorà
The document summarizes a presentation given by Jorge Casillas on research related to scaling up genetic learning algorithms and fuzzy classifier systems. Specifically, it discusses:
1. An approach using evolutionary instance selection and stratification to extract rule sets from large datasets that balance prediction accuracy and interpretability.
2. Fuzzy-XCS, an accuracy-based genetic fuzzy system the author is developing that uses competitive fuzzy inference and represents rules as disjunctive normal forms to address challenges in credit assignment.
3. Open problems and opportunities in applying genetic learning at large scales, such as addressing chromosome size and efficient evaluation over large datasets.
Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Sca...Xavier Llorà
This document summarizes research using a Pittsburgh Learning Classifier System (LCS) called GAssist to predict protein structure by determining coordination numbers (CN). The researchers tested GAssist on a dataset of over 250,000 protein residues, comparing it to support vector machines, Naive Bayes, and C4.5 decision trees. While support vector machines achieved the best accuracy, GAssist produced more interpretable and compact rule sets at the cost of lower performance. The researchers analyzed the interpretability and scalability of GAssist for this challenging bioinformatics problem, identifying avenues for improving its accuracy while maintaining explanatory power.
Learning Classifier Systems for Class Imbalance ProblemsXavier Llorà
The document discusses learning classifier systems (LCS) for addressing class imbalance problems in datasets. It aims to enhance the applicability of LCS to knowledge discovery from real-world datasets that often exhibit class imbalance, where one class is represented by significantly fewer examples than other classes. The author proposes adapting parameters of the XCS learning classifier system, such as learning rate and genetic algorithm threshold, based on estimated class imbalance ratios within classifiers' niches in order to minimize bias towards majority classes and better handle small disjuncts representing minority classes.
XCS: Current capabilities and future challengesXavier Llorà
The document discusses the XCS classifier system, which uses a combination of gradient-based techniques and evolutionary algorithms to learn predictive models from complex problems. It summarizes XCS's current capabilities in classification, function approximation, and reinforcement learning tasks. However, it notes there are still challenges to improve XCS's representations and operators, niching abilities, handling of dynamic problems, solution compactness, and development of hierarchical classifier systems.
Computed Prediction: So far, so good. What now?Xavier Llorà
This document discusses computed prediction in learning classifier systems (LCS). It addresses representing the payoff function Q(s,a) that maps state-action pairs to expected future payoffs. Specifically:
1) In computed prediction, each classifier has parameters w and the classifier prediction is computed as a parametrized function p(x,w) like a linear approximation.
2) Classifier weights are updated using the Widrow-Hoff rule online as the payoff function is learned.
3) Using a powerful approximator like tile coding to compute predictions allows the problem to potentially be solved by a single classifier, but evolution of different approximators per problem subspace may still
This document provides information about the NCSA/IlliGAL Gathering on Evolutionary Learning (NIGEL 2006) conference. It discusses how the conference originated from a previous 2003 gathering. It thanks the organizers and participants and provides details about the agenda, which includes presentations on topics like classifier systems and discussions around applications and techniques of evolutionary learning.
Linkage Learning for Pittsburgh LCS: Making Problems TractableXavier Llorà
Presentation by Xavier Llorà, Kumara Sastry, & David E. Goldberg showing how linkage learning is possible on Pittsburgh style learning classifier systems
Meandre: Semantic-Driven Data-Intensive Flows in the CloudsXavier Llorà
- Meandre is a semantic-driven data-intensive workflow infrastructure for distributed computing. It allows users to assemble modular components into complex workflows (flows) in a visual programming tool or using a scripting language called ZigZag.
- Workflows are composed of components, which can be executable or control components. Executable components perform computational tasks when data is available, while control components pause workflows for user interactions. Components are described semantically using ontologies to separate functionality from implementation.
- Data availability drives workflow execution in Meandre. When required inputs are available, components will fire and produce outputs to make data available for downstream components. This dataflow approach aims to make workflows transparent, intuitive, and reusable across
ZigZag is a new language for describing data-intensive workflows. It aims to make the Meandre infrastructure easier to use by allowing users to assemble complex data flows. The language has a new syntax and compiles workflows that can then be run on Meandre to process large datasets.
Do not Match, Inherit: Fitness Surrogates for Genetics-Based Machine Learning...Xavier Llorà
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems---and genetics-based machine learning in general---can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the x-ary extended compact classifier system (xeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks---a necessary condition to accurately estimate the fitness of the evolved rules.
Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infr...Xavier Llorà
Cancer diagnosis is essentially a human task. Almost universally, the process requires the extraction of tissue (biopsy) and examination of its microstructure by a human. To improve diagnoses based on limited and inconsistent morphologic knowledge, a new approach has recently been proposed that uses molecular spectroscopic imaging to utilize microscopic chemical composition for diagnoses. In contrast to visible imaging, the approach results in very large data sets as each pixel contains the entire molecular vibrational spectroscopy data from all chemical species. Here, we propose data handling and analysis strategies to allow computer-based diagnosis of human prostate cancer by applying a novel genetics-based machine learning technique ({\tt NAX}). We apply this technique to demonstrate both fast learning and accurate classification that, additionally, scales well with parallelization. Preliminary results demonstrate that this approach can improve current clinical practice in diagnosing prostate cancer.
This presentation covers a brief overview of the current stage of the DISCUS project. General overview and introduction to some of the currently available tools
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
Data Protection in a Connected World: Sovereignty and Cyber Securityanupriti
Delve into the critical intersection of data sovereignty and cyber security in this presentation. Explore unconventional cyber threat vectors and strategies to safeguard data integrity and sovereignty in an increasingly interconnected world. Gain insights into emerging threats and proactive defense measures essential for modern digital ecosystems.
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
How to Avoid Learning the Linux-Kernel Memory ModelScyllaDB
The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve?
This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
AI_dev Europe 2024 - From OpenAI to Opensource AIRaphaël Semeteys
Navigating Between Commercial Ownership and Collaborative Openness
This presentation explores the evolution of generative AI, highlighting the trajectories of various models such as GPT-4, and examining the dynamics between commercial interests and the ethics of open collaboration. We offer an in-depth analysis of the levels of openness of different language models, assessing various components and aspects, and exploring how the (de)centralization of computing power and technology could shape the future of AI research and development. Additionally, we explore concrete examples like LLaMA and its descendants, as well as other open and collaborative projects, which illustrate the diversity and creativity in the field, while navigating the complex waters of intellectual property and licensing.
AC Atlassian Coimbatore Session Slides( 22/06/2024)apoorva2579
This is the combined Sessions of ACE Atlassian Coimbatore event happened on 22nd June 2024
The session order is as follows:
1.AI and future of help desk by Rajesh Shanmugam
2. Harnessing the power of GenAI for your business by Siddharth
3. Fallacies of GenAI by Raju Kandaswamy
What Not to Document and Why_ (North Bay Python 2024)Margaret Fero
We’re hopefully all on board with writing documentation for our projects. However, especially with the rise of supply-chain attacks, there are some aspects of our projects that we really shouldn’t document, and should instead remediate as vulnerabilities. If we do document these aspects of a project, it may help someone compromise the project itself or our users. In this talk, you will learn why some aspects of documentation may help attackers more than users, how to recognize those aspects in your own projects, and what to do when you encounter such an issue.
These are slides as presented at North Bay Python 2024, with one minor modification to add the URL of a tweet screenshotted in the presentation.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/07/intels-approach-to-operationalizing-ai-in-the-manufacturing-sector-a-presentation-from-intel/
Tara Thimmanaik, AI Systems and Solutions Architect at Intel, presents the “Intel’s Approach to Operationalizing AI in the Manufacturing Sector,” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge is powering a revolution in industrial IoT, from real-time processing and analytics that drive greater efficiency and learning to predictive maintenance. Intel is focused on developing tools and assets to help domain experts operationalize AI-based solutions in their fields of expertise.
In this talk, Thimmanaik explains how Intel’s software platforms simplify labor-intensive data upload, labeling, training, model optimization and retraining tasks. She shows how domain experts can quickly build vision models for a wide range of processes—detecting defective parts on a production line, reducing downtime on the factory floor, automating inventory management and other digitization and automation projects. And she introduces Intel-provided edge computing assets that empower faster localized insights and decisions, improving labor productivity through easy-to-use AI tools that democratize AI.
Sustainability requires ingenuity and stewardship. Did you know Pigging Solutions pigging systems help you achieve your sustainable manufacturing goals AND provide rapid return on investment.
How? Our systems recover over 99% of product in transfer piping. Recovering trapped product from transfer lines that would otherwise become flush-waste, means you can increase batch yields and eliminate flush waste. From raw materials to finished product, if you can pump it, we can pig it.
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
1. Data-Intensive
Research Workshop
Soaring through clouds with Meandre
Xavier Llorà and Bernie Ács
xllora@illinois.edu
bernie@ncsa.illinois.edu
National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign
3. An Ideological Metaphor & Definition
• Cloud Metaphor
• The term cloud is used as a metaphor for
the Internet, based on how it is depicted in
computer network diagrams and is an
abstraction for the complex infrastructure
it conceals
• Cloud Computing – Definition
• The first academic use of this term appears to define it as a computing
paradigm where the boundaries of computing will be determined
by economic rationale rather than technical limits.
• Cloud computing is a paradigm of computing in which dynamically
scalable and often virtualized resources are provided as a service over
the Internet. Users need not have knowledge of, expertise in, or control
over the technology infrastructure in the "cloud" that supports them
http://en.wikipedia.org/wiki/Cloud_computing
Imaginations unbound
10. Cloud Classification Types
• Public cloud or external cloud describes cloud
computing in the traditional mainstream sense, whereby
resources are dynamically provisioned on a fine-grained,
self-service basis over the Internet, via web applications/
web services, from an off-site third-party provider who
shares resources and bills on a fine-grained
utility computing basis
• Private cloud and internal cloud is a neologism that
describe configurations that emulate (public) cloud
computing on private networks
• Hybrid cloud consists of multiple internal and/or
external cloud deployments
http://en.wikipedia.org/wiki/Cloud_Computing
Imaginations unbound
11. Cloud Computing Models
• Infrastructure as a Service (IaaS)
• the delivery of computer infrastructure (typically a
platform virtualization environment) as a service
• Rather than purchasing servers, software, data center space
or network equipment, clients instead buy those resources as
a fully outsourced service.
• The service is typically billed on a utility computing basis and
amount of resources consumed (and therefore the cost) will
typically reflect the level of activity.
• Supersedes term Hardware as a Service (HaaS)
• It is an evolution of web hosting and virtual private server
offerings.
• Example: Amazon EC2/S3 services
http://en.wikipedia.org/wiki/Infrastructure_as_a_service
Imaginations unbound
12. Cloud Computing Models
• Platform as a Service (PaaS)
• delivery of a computing platform and solution stack as a service
• It facilitates deployment of applications without the cost and
complexity of buying and managing the underlying hardware
and software layers, providing all of the facilities required to
support the complete life cycle of building and delivering
web applications and services entirely available from the
Internet —with no software downloads or installation for
developers, IT managers or end-users
• Open Platform as a Service (OPaaS)
• another step in the Application Service Provider, SaaS, PaaS
evolution
• Example: Microsoft TechNet VLabs
http://en.wikipedia.org/wiki/Platform_as_a_service
Imaginations unbound
13. Cloud Computing Models
• Software as a Service (SaaS)
• is a model of software deployment whereby a provider licenses
an application to customers for use as a service on demand
• vendors may host the application on their own web servers or
download the application to the consumer device, disabling it
after use or after the on-demand contract expires
• Examples:
• Google Apps (Maps, Docs, and Others)
• Adobe (Connect & Buzzword)
• Microsoft (Workspace office live)
http://en.wikipedia.org/wiki/Platform_as_a_service
Imaginations unbound
15. NCSA Uses Virtual Machine Technologies
• Virtual machine technology to support projects &
services using VMware, XenServer, & Others
• An Example Case: ICLCS & WebMO
• Institute for Chemistry Literacy Through Computational Science
(http://Iclcs.uiuc.edu/workshops & http://www.webmo.net/)
Shared Network
File System
Passive LB Node
Centralize
Active LB Node Relational
Database
Internet Users Worker Worker
Internet Users Worker
Node
Worker
Node
Internet Users Node Worker Node
Internet Users
Internet Users Node
Imaginations unbound
16. NCSA Enterprise Cloud
• Virtual Machine Infrastructure Expansion
• Dedicated Resources
• 176 Cores/18 Machines with 50TB Storage and 40Gb IB
• Dedicated Switches, Network services for VM & Cloud.
• Eucalyptus installation base
• “Amazon at home”
• EC2/S3/EBS
• Potential future support for
• dynamic load-balanced services & load-based procurement
• High degree of variability possible in configurations
• Account based virtual private enterprise
• Elastic IP, Elastic Block Storage, & Elastic Computing
• Empowers users versus Constrains users
• Cloud mechanics require a steep learning curve
Imaginations unbound
17. NCSA Enterprise Cloud User Tools
• Command Line Tools
• Amazon Web Services API compatible tools (euca-*)
• Customizations and Refinements
• ElasticFox (Version 1.6)
• FireFox plugin works well; has required modification, more to do.
List, Launch, & Manage Images
Imaginations unbound
18. NCSA Enterprise Cloud User Tools
• Command Line Tools
• Amazon Web Services API compatible tools (euca-*)
• Customizations and Refinements
• ElasticFox (Version 1.6)
• FireFox plugin works well; has required modification, more to do.
Enterprise Security Rules
Imaginations unbound
19. NCSA Enterprise Cloud User Tools
• Command Line Tools
• Amazon Web Services API compatible tools (euca-*)
• Customizations and Refinements
• ElasticFox (Version 1.6)
• FireFox plugin works well; has required modification, more to do.
SSH Key-Pair Management
Imaginations unbound
20. NCSA Enterprise Cloud User Tools
• Command Line Tools
• Amazon Web Services API compatible tools (euca-*)
• Customizations and Refinements
• ElasticFox (Version 1.6)
• FireFox plugin works well; has required modification, more to do.
Allocate, Assign, & Associate Elastic IP
Imaginations unbound
21. NCSA Enterprise Cloud User Tools
• Command Line Tools
• Amazon Web Services API compatible tools (euca-*)
• Customizations and Refinements
• ElasticFox (Version 1.6)
• FireFox plugin works well; has required modification, more to do.
Allocate, Assign, &
Associate
Elastic Block Storage
Imaginations unbound
22. NCSA Enterprise Cloud User Tools
• Command Line Tools
• Amazon Web Services API compatible tools (euca-*)
• Customizations and Refinements
• AWS Manager
• Statically deployed Web-Application
Imaginations unbound
23. NCSA Enterprise Cloud Conduits
• Private Cloud to Grid Conduit
• Dynamically Scalable Web Front-end & Middleware Layers
• Next Generation WebMO “Science Gateway”
• Batch Queue Proxy Integration, Metering, and Monitoring
• Private Cloud to Private Cloud Conduit
• Exploring Transparent Integration with Remote Sites
• UIUC Computer Science Hadoop Cluster
• Dynamic Integration with other Eucalyptus Site
• Private Cloud to Public Cloud Conduit
• Exploring Transparent Integration with Amazon EC2 Service
• Roles of Virtual Private Network Services
• Dynamic Scalability and Data Localities
Imaginations unbound
24. Part 2: Cloud Programming Paradigm
• How are Software Architecture and Design Impacted by
Virtual Machines & Cloud technologies?
• Natural Match for Multi-tier applications
• To best leverage cloud technology applications need to be more
modular and less monolithic
• Service orientated architecture can benefit from JeOS (Just
Enough Operating System) platforms and
• Can be easily configured to dynamically scale
• Meandre: Overview & Introduction
• Agile Infrastructure for Data Intensive Applications
• Semantic Orientated Component Based Architecture
• Data Driven Execution Paradigm
• SEASR Application Examples
Imaginations unbound
25. MONK Project – GSLIS
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
26. Feature Lens Blow up
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
27. Date Entities to Simile Timeline
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
28. Analyzing CSPAN Archives
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
29. NEMA – Son of Blinkie - GSLIS
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
30. NESTER – GSLIS
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
34. Evolution Highway – IGB
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
35. Fedora Commons Repository
Components & Flows
Interactive Web
Application
Web Service
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
36. Twitter For Research
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
38. Data-intensive Computing for the Cloud
• Meandre
• Integrates within Existing Applications
• May be a Free Standing Service
• Capitalize on elasticity
• Provide complex data computing as a service
• Collocating computation and data
• Natively access data in the cloud
• Hadoop Distributed File System (HDFS)
• Document stores
• KeyValue stores
• Relational stores
39. Meandre: The Dataflow Component
• Data dictates component execution semantics
Inputs Outputs
Component
P
Descriptor in RDF" The component "
of its behavior
implementation
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
40. Meandre: Flow (Complex Tasks)
• A flow is a collection of connected components
Read
P Merge
P
Get Show
P
P
Do
P
Dataflow execution
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
41. Meandre Connectors
Flows are made up of “One or More” components
with “None to Many” connectors that are described Flows may contain connectors that
to the Mendre Server for management are cyclical over one or more
components
Flows must contain at minimum one
component with NO Inputs to cause
an Execute call to be made.
*Outputs are Always Optional.
Flow components may have
multiple connectors assigned
to any input data port
Flows can have any number of components with
“None to Many” Inputs data port s
“None to Many” Output data ports
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
42. Meandre: ZigZag Script Language
• Automatic Parallelization
• Adding the operator [+4] would result in a directed grap
# Describes the data-intensive flow # Describes the data-intensive flow
# #
@pu = push() @pu = push()
@pt = pass( string:pu.string ) [+4] @pt = pass( string:pu.string ) [+4!]
print( object:pt.string ) print( object:pt.string )
The SEASR project and its Meandre infrastructure!
are sponsored by The Andrew W. Mellon Foundation
43. Scaling Genetic Algorithms with Meandre
Intel 2.8Ghz QuadCore, 4Gb RAM. Average of 20 runs.
Imaginations unbound
44. And Beyond with Hadoop
60 Dual Quad Core Xeons with 8GB RAM. GB Ethernet
Resources exhaustion
Imaginations unbound
45. Are Components Black-Box Wrappers?
• Programming Components is multilingual
• Natively support: Java, Scala, Python, and Clojure
• Easily Wrap: R, C, and C++
• Components can also interact with the OS
• Leverage OS tools
• Orchestrate other programs
• The question:
• Can Meandre help orchestrate and facilitate interaction and
cooperation between cloud and grid assets?
47. Cloud Conduits to the Grid
• Cloud mechanics have a steep learning curve
• Can Meandre help simplify the process?
• Orchestrating clouds with Meandre
• Amazon/Eucalyptus model
• Components can be created to:
• List images
• List instances
• Launch instances
• Allocate Elastic IP and Elastic Block Storage
• Transfer Data or Programs to running instances
• Trigger process computation
• Monitor processes and/or executing persistent services
• Terminate instances
49. Conclusions
• Next generation data-intensive applications will:
• Use cloud computing technologies and conduits
• Require adaptation of programming paradigms
• Leverage a flexible architecture and a modular
• Promote processing and resources at scale.
• Meandre
• Data-intensive execution engine
• Component-based programming architecture
• Distributed data flow designs to allow processing to be co-
located with data sources and enable transparent scalability
• Orchestrate cloud deployments
• Leverage cloud conduits
Imaginations unbound