This document provides information about an upcoming Heat Orchestration Template (HOT) learning session at the OpenStack Summit in Austin, TX on April 27th 2016. It introduces the two presenters, Kanagaraj Manickam and Huang Tianhua, and provides an agenda and overview of the content to be covered, including Heat, HOT schematics, validation and preview, and Heat features like auto-scaling and software deployment.
OpenStack 운영을 통해 얻은 교훈을 공유합니다.
목차
1. TOAST 클라우드 지금의 모습
2. OpenStack 선택의 이유
3. 구성의 어려움과 극복 사례
4. 활용 사례
5. 풀어야 할 문제들
대상
- TOAST 클라우드를 사용하고 싶은 분
- WMI를 처음 들어보시는 분
This document provides an overview of Kubernetes including:
1) Kubernetes is an open-source platform for automating deployment, scaling, and operations of containerized applications. It provides container-centric infrastructure and allows for quickly deploying and scaling applications.
2) The main components of Kubernetes include Pods (groups of containers), Services (abstract access to pods), ReplicationControllers (maintain pod replicas), and a master node running key components like etcd, API server, scheduler, and controller manager.
3) The document demonstrates getting started with Kubernetes by enabling the master on one node and a worker on another node, then deploying and exposing a sample nginx application across the cluster.
source : http://www.opennaru.com/opensource/kubernetes/
Kubernetes는 컨테이너화된 애플리케이션(Containerized Application)의 배포, 확장 그리고 관리를 할 수 있는 오픈 소스 컨테이너 오케스트레이션 시스템입니다.
쿠버네티스는 구글 엔지니어들이 개발하고 설계한 플랫폼으로서 사내에서 이용하던 컨테이너 클러스터 관리 도구인 “Borg”의 아이디어를 바탕으로 만들어진 오픈소스 소프트웨어입니다.
구글은 쿠버네티스의 원천이 되는 Borg를 수년 동안 개발하고 운영하면서 축적된 경험을 바탕으로 쿠버네티스를 오픈소스 프로젝트로 만들어 었습니다.
The document introduces the neutron packet logging framework. It discusses how the framework logs packets that are allowed or dropped by security policies to provide visibility for operators. It demonstrates the logging API and how to configure logging. Future plans include supporting additional resources like firewall groups and integrating with monitoring services.
Kubernetes Application Deployment with Helm - A beginner Guide!Krishna-Kumar
Google DevFest2019 Presentation at Infosys Campus Bangalore. Application deployment in Kubernetes with Helm is demo'ed in Google Kubernetes Engine (GKE). This is an introductory session on Helm. Several references are given in it to further explore helm3 as it is in Beta state now.
Kubernetes와 Kubernetes on OpenStack 환경의 비교와 그 구축방법에 대해서 알아봅니다.
1. 클라우드 동향
2. Kubernetes vs Kubernetes on OpenStack
3. Kubernetes on OpenStack 구축 방벙
4. Kubernetes on OpenStack 운영 방법
오픈스택이 가진 기술에 대하여 설명합니다.
1. 오픈소스기반 OpenStack 클라우드 시스템
2. OpenStack 기술 개요 및 동향
3. OpenStack 의 Community 개발 체계
4. OpenStack HA를 위한 방안
5. OpenStack SDN 개발 동향
6. Neutron OVS-DPDK 가속화와 구현방안
Kubernetes is an open source container orchestration system that automates the deployment, maintenance, and scaling of containerized applications. It groups related containers into logical units called pods and handles scheduling pods onto nodes in a compute cluster while ensuring their desired state is maintained. Kubernetes uses concepts like labels and pods to organize containers that make up an application for easy management and discovery.
Overview of Distributed Virtual Router (DVR) in Openstack/Neutronvivekkonnect
The document discusses distributed virtual routers (DVR) in OpenStack Neutron. It describes the high-level architecture of DVR, which distributes routing functions from network nodes to compute nodes to improve performance and scalability compared to legacy centralized routing. Key aspects covered include east-west and north-south routing mechanisms, configuration, agent operation modes, database extensions, scheduling, and support for services. Plans are outlined for enhancing DVR in upcoming OpenStack releases.
Red Hat OpenShift 4 allows for automated and customized deployments. The Full Stack Automation method fully automates installation and updates of both the OpenShift platform and Red Hat Enterprise Linux CoreOS host operating system. The Pre-existing Infrastructure method allows OpenShift to be deployed on user-managed infrastructure, where the customer provisions resources like load balancers and DNS. Both methods use the openshift-install tool to generate ignition configs and monitor the cluster deployment.
Kanagaraj Manickam presents Namos, an OpenStack manager that aims to solve problems in the OpenStack ecosystem. Namos provides RESTful APIs and a graphical user interface to manage OpenStack deployments, services, devices, and configurations. It discovers deployment architectures, checks compliance, and provides a single pane of glass for configuration. Namos facilitates adding, updating, deleting, and managing devices and their quotas. This allows operators more control and developers a way to build features based on managed devices.
This document discusses network function virtualization (NFV) and how OpenStack can be used as an NFV orchestrator. It outlines that telecom operators need to reduce costs and increase agility. NFV aims to implement network functions through software running on standard server hardware instead of proprietary appliances. The ETSI NFVO-MANO framework specifies an NFV orchestrator. The document discusses how OpenStack, with components like Tacker and Heat, provides an orchestration platform as the virtualized infrastructure manager (VIM) to deploy and manage virtual network functions on cloud infrastructure. It also lists some open source NFV orchestrator projects and commercial offerings.
OpenStack Summit 2015 Tokyo Heat-Translator and TOSCA vbrownbagme_slideshare_2
Charts were used during the technical talk on the latest of Heat-Translator and TOSCA-Parser and how they can be used to deploy TOSCA workloads in OpenStack. The location of talk was OpenStack Summit 2015 Tokyo.
OpenStack is an open-source cloud computing platform that provides common services for both public and private clouds. It was launched in 2010 by Rackspace and NASA as a joint project. OpenStack provides APIs and tools to provision resources like compute, storage, and networking. It allows building clouds on top of heterogeneous data center hardware. Customers choose OpenStack because it is open source, low cost, customizable, has community support, and is compatible with AWS APIs. Major companies like BMW, Disney, and Walmart use OpenStack in production environments.
The document discusses auto scaling of cloud applications using OpenStack Heat and Monasca. It begins with an overview of auto scaling and its benefits. It then provides details on how Heat can orchestrate the auto scaling of an application based on alarms from Monasca. Metrics from the application's virtual machines in a scale group are monitored. When thresholds are crossed, Monasca notifies Heat which scales the application by adding or removing VMs through Heat templates according to scaling policies. A demo and Q&A session are also included on the agenda.
Presentation of Ceilometer (OpenStack Telemetry) new features in OpenStack Havana and a look at the features coming in IceHouse. Joint presentation done with Julien Danjou at the OpenStack In Action 4 (Dec 5th 2013)
Application and Network Orchestration using Heat & ToscaNati Shalom
The buzzwords Neutron, Heat, and TOSCA are spoken about quite often when it comes to the OpenStack - and many of us are still trying to make sense of the terminology and its place in the OpenStack world.
Where OpenStack Neutron provides APIs for creating network elements, OpenStack Heat provides an orchestration engine for automating the setup and configuration of OpenStack infrastructure, while TOSCA is a standard for templating and defining application topology and policies (that form the basis for Heat). In this context, it really makes sense to put these all together to achieve application and network automation for OpenStack on steroids.
In this session we will learn how we can use the robust combination of Heat and TOSCA to configure and control resources on Nova and Neutron in order to automate the network configuration as part of the application deployment.
The session will include a demo and code examples that show how you can configure virtual networks, attach public IPs, set up security groups, set up load balancing and automatically scale up/down and more. You will leave this session understanding where Neutron meets Heat and TOSCA.
This talk was delivered as part of OpenStack Paris summit - 2014 - http://openstacksummitnovember2014paris.sched.org/event/2b85b682ccaf3a5961e463b61e2403f8#.VFeuG_TF8mc
Deployment Automation on OpenStack with TOSCA and CloudifyCloudify Community
TOSCA (Topology and Orchestration Specification for Cloud Applications) is an emerging standard for modeling complete application stacks and automating their deployment and management. It’s been discussed in the context of OpenStack for quite some time, mostly around Heat. In this session we’ll discuss what TOSCA is all about, why it makes sense in the context of OpenStack, and how we can take it farther up the stack to handle complete applications, both during and after deployment, on top of OpenStack.
The document outlines the lengthy new drug application (NDA) process with the FDA, which involves animal testing, clinical trials with increasing numbers of participants over three phases, and committee approval. If approved, the drug can be sold for 20 years before the patent expires, after which generic versions can be produced by other companies to show equivalence under a different name. The overall process from development to approval takes around 10 years and $800 million on average for a new drug.
A Digipak is a type of DVD and CD packaging made of paperboard or card stock with a plastic tray inside to hold the disc. It provides a nicer presentation than jewel cases and is more environmentally friendly, but is more expensive to produce. Key elements of a Digipak include pictures, design related to the artist's genre, track lists, credits, and bonus content. Digipaks provide audiences with a collector's item and bonus materials not otherwise available, supporting the music industry. Radiohead's "Hail to the Thief" special edition used a unique folded design fitting their indie style, though some felt it could have included more bonuses. Popular artists that have used Digipaks include Johnny Cash, Brit
This document describes a study that used a maximum entropy distribution model (Maxent) to predict suitable habitat for the Afghan pika (Ochotona rufescens) in Iran based on climatic parameters. The study identified annual mean temperature, temperature annual range, and precipitation of the coldest quarter as the most important climatic factors limiting the pika's distribution. The Maxent model performed well, with an average area under the receiver operating characteristic curve of 0.846. The resulting suitability map is consistent with other studies and can help explain the pika's distribution in Iran.
Hauling costs can get expensive and out of hand with added costs and fees. There are ways to overcome these extra costs and fees. We want you to learn more about RS and how we can help you nix these fees that are adding 8-30% to your expenses each year.
Heat is an OpenStack template-based orchestration service that allows users to describe infrastructure and applications in text files called Heat Orchestration Templates (HOT) and automate the deployment of multi-component, multi-tier applications across OpenStack and other platforms. Heat provides the ability to define infrastructure resources like servers, networks, routers, and security groups and specify relationships between resources. It comprises several Python applications that work together to provision and manage OpenStack resources through a REST API according to the templates.
LinuxCon 2013 Steven Dake on Using Heat for autoscaling OpenShift on OpenstackOpenShift Origin
OpenStack Heat allows modeling relationships between OpenStack resources and managing infrastructure resources throughout application lifecycles. The presentation discusses Heat architecture, autoscaling workflows using Heat and Ceilometer, and demonstrates an OpenShift autoscaling workflow on OpenStack using Heat templates, DIB elements, and CloudWatch alarms. Future work may expand autoscaling to other resources and integrate it more fully across OpenStack projects.
Apache Spark Performance Troubleshooting at Scale, Challenges, Tools, and Met...Databricks
This talk is about methods and tools for troubleshooting Spark workloads at scale and is aimed at developers, administrators and performance practitioners. You will find examples illustrating the importance of using the right tools and right methodologies for measuring and understanding performance, in particular highlighting the importance of using data and root cause analysis to understand and improve the performance of Spark applications. The talk has a strong focus on practical examples and on tools for collecting data relevant for performance analysis. This includes tools for collecting Spark metrics and tools for collecting OS metrics. Among others, the talk will cover sparkMeasure, a tool developed by the author to collect Spark task metric and SQL metrics data, tools for analysing I/O and network workloads, tools for analysing CPU usage and memory bandwidth, tools for profiling CPU usage and for Flame Graph visualization.
Best Practice for Deploying Application with HeatEthan Lynn
Long Quan Sha and Ethan Lynn from IBM and Tian Hua Huang from Huawei presented on best practices for Heat resource modules and deployment patterns. They discussed Heat introduction, software deployment options using cloud-init and software deployments, building custom images, and signal transport methods. They also covered creating resource modules based on business concepts to make templates easier to understand and compose common deployment patterns. Finally, they demonstrated resource modules and a load balancing autoscaling group template.
The document discusses using Senlin, an OpenStack clustering service, to provide autoscaling capabilities for multicloud platforms. Senlin allows for managing clusters of nodes across different cloud providers and includes features like load balancing, auto-healing, and scaling policies. It describes how Senlin was implemented at a company to provide a centralized autoscaling solution across OpenStack and VMware cloud environments. Some drawbacks of Senlin are also outlined, along with potential future work like multi-region clusters and global load balancing.
Apache Eagle at Hadoop Summit 2016 San JoseHao Chen
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop that was created by eBay and later open sourced as an Apache Incubator project. It provides security for Hadoop systems by instantly identifying access to sensitive data, recognizing attacks/malicious activity, and blocking access in real time through complex policy definitions and stream processing. Eagle was designed to handle the huge volume of metrics and logs generated by large-scale Hadoop deployments through its distributed architecture and linear scalability.
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop that was created by eBay and later open sourced as an Apache Incubator project. It provides security for Hadoop systems by instantly identifying access to sensitive data, recognizing attacks/malicious activity, and blocking access in real time through complex policy definitions and stream processing. Eagle was designed to handle the huge volume of metrics and logs generated by large-scale Hadoop deployments through its distributed architecture and use of technologies like Apache Storm and Kafka.
Centralized log-management-with-elastic-stackRich Lee
Centralized log management is implemented using the Elastic Stack including Filebeat, Logstash, Elasticsearch, and Kibana. Filebeat ships logs to Logstash which transforms and indexes the data into Elasticsearch. Logs can then be queried and visualized in Kibana. For large volumes of logs, Kafka may be used as a buffer between the shipper and indexer. Backups are performed using Elasticsearch snapshots to a shared file system or cloud storage. Logs are indexed into time-based indices and a cron job deletes old indices to control storage usage.
It’s no news that containers represent a portable unit of deployment, and OpenStack has proven an ideal environment for running container workloads. However, where it usually becomes more complex is that many times an application is often built out of multiple containers, as well as hybrid environments - diverse clouds, bare metal and even non-virtualized infrastructure. What’s more, setting up a cluster of container images can be fairly cumbersome because you need to make one container aware of another and expose intimate details that are required for them to communicate which is not trivial especially if they’re not on the same host.
These scenarios have instigated the demand for some kind of orchestrator. The list of container orchestrators is growing fairly fast. This session will compare the different orchestration projects out there - from Heat to Kubernetes to Mesos & Cloudify - and help you choose the right tool for the job.
Powering a Graph Data System with Scylla + JanusGraphScyllaDB
Key Value and Column Stores are not the only two data models Scylla is capable of. In this presentation learn the What, Why and How of building and deploying a graph data system in the cloud, backed by the power of Scylla.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Heat is OpenStack's orchestration service for launching multiple composite cloud applications called "stacks" using templates. It provides an API and templates to define resources and allow repeatable, automated deployments. The Heat architecture uses a central engine and plugins to provision resources across OpenStack services. New features improve reliability, scalability, and allow concurrent stack updates through a convergence engine.
Achieve big data analytic platform with lambda architecture on cloudScott Miao
This document discusses achieving a big data analytic platform using the Lambda architecture on cloud infrastructure. It begins by explaining why moving to the cloud provides benefits like elastic scaling, reduced operational overhead, and increased focus on innovation. Common cloud services at Trend Micro like an analytic engine and cloud storage are then described. The document introduces the Lambda architecture and proposes a serving layer as a service. Key lessons learned from building big data solutions on AWS include the pros of unlimited scalability and easy disaster recovery compared to on-premises infrastructure.
Nagios Conference 2014 - Konstantin Benz - Monitoring Openstack The Relations...Nagios
Konstantin Benz's presentation on Monitoring Openstack The Relationship Between Nagios and Ceilometer.
The presentation was given during the Nagios World Conference North America held Oct 13th - Oct 16th, 2014 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/conference
This document discusses Infrastructure as Code (IaC) principles and practices. It describes how a software company implemented IaC to automate their infrastructure provisioning and application deployments using Kubernetes and Google Cloud Platform. They used Ansible playbooks to define and build their infrastructure and container images. Kubernetes configuration files standardized across environments allow for fast, idempotent deployments. Continuous integration tests changes at each stage from unit testing to integration testing after deployments. This implementation of IaC provides many wins including easily reproducible, consistent systems and improved deployment processes.
Enterprise guide to building a Data MeshSion Smith
Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.
Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Lov...Databricks
This talk is about sharing experience and lessons learned on setting up and running the Apache Spark service inside the database group at CERN. It covers the many aspects of this change with examples taken from use cases and projects at the CERN Hadoop, Spark, streaming and database services. The talks is aimed at developers, DBAs, service managers and members of the Spark community who are using and/or investigating “Big Data” solutions deployed alongside relational database processing systems. The talk highlights key aspects of Apache Spark that have fuelled its rapid adoption for CERN use cases and for the data processing community at large, including the fact that it provides easy to use APIs that unify, under one large umbrella, many different types of data processing workloads from ETL, to SQL reporting to ML.
Spark can also easily integrate a large variety of data sources, from file-based formats to relational databases and more. Notably, Spark can easily scale up data pipelines and workloads from laptops to large clusters of commodity hardware or on the cloud. The talk also addresses some key points about the adoption process and learning curve around Apache Spark and the related “Big Data” tools for a community of developers and DBAs at CERN with a background in relational database operations.
The document provides an overview of Kubernetes including its introduction, configuration file creation using direct editing, templates with Helm and Kustomize, usage patterns, web service practices, and deployment pipelines. Key sections include explaining Kubernetes architecture and mechanisms, setting up access to a Kubernetes cluster, generating Helm templates to render Kubernetes objects, customizing templates for different environments in Kustomize, and using ArgoCD for deployment automation.
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.
UiPath Community Day Kraków: Devs4Devs ConferenceUiPathCommunity
We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner!
We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
09:40 From bottlenecks to breakthroughs: Citizen Development in action (25')
Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
10:05 Next-level bots: API integration in UiPath Studio (30')
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
12:20 🍕 Lunch Break (1hr)
13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30')
Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
13:50 Communications Mining - focus on AI capabilities (30')
Thomasz Wierzbicki, Business Analyst @Office Samurai
14:20 Polish MVP panel: Insights on MVP award achievements and career profiling
YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS
WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well.
Some facts about WPRiders and why we are one of the best firms around:
More than 700 five-star reviews! You can check them here.
1500 WordPress projects delivered.
We respond 80% faster than other firms! Data provided by Freshdesk.
We’ve been in business since 2015.
We are located in 7 countries and have 22 team members.
With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce.
Our team members are:
- highly experienced developers (employees & contractors with 5 -10+ years of experience),
- great designers with an eye for UX/UI with 10+ years of experience
- project managers with development background who speak both tech and non-tech
- QA specialists
- Conversion Rate Optimisation - CRO experts
They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals.
At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
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.
論文紹介:A Systematic Survey of Prompt Engineering on Vision-Language Foundation ...Toru Tamaki
Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023
https://arxiv.org/abs/2307.12980
Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 :
- Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants.
- REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxSynapseIndia
Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation
Best Programming Language for Civil EngineersAwais Yaseen
The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era.
Technology is revolutionizing civil engineering by integrating advanced tools and techniques. Programming allows for the automation of repetitive tasks, enhancing the accuracy of designs, simulations, and analyses. With the advent of artificial intelligence and machine learning, engineers can now predict structural behaviors under various conditions, optimize material usage, and improve project planning.
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.
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.
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfNeo4j
Presented at Gartner Data & Analytics, London Maty 2024. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.
2. About Authors
Kanagaraj Manickam
Sr. System Architect @ Huawei, Bangalore, India.
• Core-reviewer @ Heat, OpenStack.
• Active participant @ Open-O an Global SDN & NFV-Orchestrator .
• Expert in Data-center Server, Storage management and automation.
• Founder of namos – OpenStack Manager)
Huang Tianhua
Software engineer @ Huawei, Xi’an, China
Core-reviewer @ Heat, Openstack.
Having 3+ years of experience in Openstack Cloud.
2
3. Start to prepare setup
• Download https://goo.gl/ne29yO
• Install devstack with https://github.com/mkr1481/hot-
tutorials/blob/master/localrc (devstack with heat, ceilometer, aodh)
• Upload the image to glace
• (Ctrl+C from https://etherpad.openstack.org/p/austin-heat)
3
4. Agenda
• Heat
• HOT schematics
• Validation & Pre-view
• Heat features
– Auto-scaling
– Software deployment
4
5. Heat
• An orchestration service to create and manage the
lifecycle of OpenStack cloud application i.e,
anything-as-a-service (XaaS)
• Uses JSON/YAML based parameterized HOT
template to model the customizable cloud application made of
software, configurations and infrastructure.
• Represent each provisioned cloud application as
Stack
• Similar to AWS cloud-formation (CFN) service
5
9. HOT schematic : Pseudo parameters
Available by default
- OS::Stack_id : Id of Stack , the template belongs to
- OS::stack_name: Name of Stack , the template belongs to
-OS::project_id: User project Id
How to get the value of these parameters?
-Use ‘openstack stack show <stack name>’ and refer from the
parameters in the output
9
11. get_file
files: {
“file:///path/to/userdata.sh”:
<file content of userdata.sh>
}
resources:
my_instance:
type: OS::Nova::Server
properties:
user_data: {get_file: userdata.sh }
user_data: <file content of userdata.sh>
NOTE:
- OpenStack heat CLI or REST API provides the ‘files’ map used here
- file path could either in absolute file path or url format
Data Reference Intrinsic functions
11
17. Provider template
heat_template_version: 2015-04-30
resources:
my_server:
type: my_nova.yaml
properties:
key_name: my_key
resource_registry:
"OS::Nova::Server": my_nova.yaml
heat_template_version: 2015-04-30
resources:
my_server:
type: OS::Nova::Server
properties:
key_name: my_key
outputs:
test_out:
value: {get_attr: [ my_server, resource.server, first_address]}
OS::stack_id:
value: {get_resource: my_server}
heat_template_version: 2015-04-30
parameters:
key_name:
type: string
description: Name of a KeyPair
resources:
server:
type: OS::Nova::Server
properties:
key_name: {get_param: key_name}
flavor: m1.small
image: ubuntu-trusty-x86_64
my_nova.yaml•Bifurcate complex template into easy-manageable-small template
• New Resourc e type
• Direct reference
• using environment
• Access the provider template’s resource attributes
• Make provider template based resources as transparent
17
18. Validation & Preview
Validation
heat template-validate –template-file <template file>
openstack orchestration template validate -t <template file>
- Produces list of input parameters for this template.
Pre-view
heat stack-preview –template-file <template file> <stack_name>
openstack stack create -t <template file> --dry-run <stack_name>
- Produces the pre-view of all resources in the template
18
19. Heat features: by means of HOT
• Using Resource Type
– auto-scaling
– software configuration & deployment
– resource grouping
– remote stack
– none resource
– random string
– wait condition
• Using Environment
– hook/breakpoint
19
20. Auto-scaling
• Scale up/down compute/storage/network
capabilities of cloud application based on the
pre-defined threshold level, which is
monitored continuously
• For defining threshold and monitoring, user
could choose either Ceilometer or Monasca
20
21. Auto-scaling: based on Monasca
OS::Heat::AutoScalingGroup
Properties
resource (scaling element)
desired_capacity (initial count)
max_size
min_szie
Outputs
current_size
outputs/output_list
• Scale Group
• Scaling policy
• Alarm Notification
• Alarm definition
• Metadata-Dimension
OS::Heat::AutoScalingGroupOS::Heat::ScalingPolicy
Properties
auto_scaling_group_id
cooldown (timing-window for scaling)
adjustement_type
Scaling_adjustment (+/- 1, n, x%)
Outputs
alarm_url ( CFN signal type)
signal_url (HOT signal type)
OS::Heat::ScalingPolicy
OS::Monasca::Notification
Properties
type (webhook, email)
address
signal / alarm url
OS::Monasca::Notification
signal / alarm url
OS::Monasca::AlarmDefinition
Properties
name
expression
match_by=scale_group
alarm_actions
OS::Monasca::AlarmDefinition
OS::Heat::AutoScalingGroup
resource:
type: OS::Nova::Server
properties:
metadata: {"scale_group": <stack-id>}
OS::Monasca::AlarmDefinition
expression: avg(vm.cpu.utilization_perc{scale_group=<stack-id>}) > 90
Monasca alarm definition wiki
Presented an demo @ OpenStack Summit, Tokyo, JP
21
23. Auto-scaling :work-flow
Heat
Monasca/
Ceilometer
VMVMVM
heat_template_version: 2015-10-15
resources:
group:
type: OS::Heat::AutoScalingGroup
scaleup_policy:
type: OS::Heat::ScalingPolicy
notification:
type: OS::Monasca::Notification
cpu_alarm_high:
type: OS::Monasca::AlarmDefinition
1
3
4
5
6
2 7
1. Call Heat with a cloud application HOT template
2. Heat creates cloud application stack with scale group and
scale policy
3. Heat creates alarm definitions and scaling notifications in
Ceilometer/Monasca
4. Ceilometer/Monasca starts to monitor the scale group
elements
[[CLOUD APPLICATION AUTO-SCALING SETUP IS DONE]]
5. When Scale group reaches the alarm threshold,
Ceilometer/Monasca detects and generates alarm
6. Ceilometer/Monasca signals cloud application stack in heat
7. Heat either scale up or scale down based on the signal
(here, increment scale group elements by 1)
5-7 runs for ever ! (auto-scale)
5
23
24. • Custom image building
• User-data boot scripts and cloud-init
• Software deployment resources
24
Software Config & Deployment
Helps to keep the files such as scripts, config, provider template out of the template
and these files could be referenced in multiple places in template
Maintain the template become easy
Only Part of resources->properties and outputs->value
These fn are only Part of resources->properties and outputs->value
(Explain about resource type (next slide) and come back here)
Get_attr similar to get_param, it can have indexing as well
Get_attr returns all in case key is not given
Deletion_policy: 'Delete', 'Retain', 'Snapshot‘
Update_policy: depends on the resource type
The template file extension must be .yaml or .template, or it will not be treated as a custom template resource.
In Ceilometer based auto-scaling, the notification and the alarm definition are combined into one heat resource type OS::Ceilometer::Alarm
OS::Ceilometer::CombinationAlarm: Helps to create Alarm from existing alarm with and/or (type: combination)
OS::Ceilometer::GnocchiResourcesAlarm: Helps to create Alarm for a specific resources (type: gnocchi_resources_threshold)
OS::Ceilometer::GnocchiAggregationByMetricsAlarm: (type: gnocchi_aggregation_by_metrics_threshold)
OS::Ceilometer::GnocchiAggregationByResourcesAlarm: (type: gnocchi_aggregation_by_resources_threshold)