Roman Gorshunov - Airship project onboarding
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Airship is an open source project that provides tools for declaratively automating cloud provisioning. It includes components for orchestration, deployment, upgrades, and resiliency. Airship uses YAML documents to provision bare metal infrastructure and deploy Kubernetes clusters and application workloads. Developers are encouraged to get involved by joining mailing lists, IRC meetings, and contributing code.
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This document discusses Docker Containers as a Service (CaaS). It begins by showing how Docker can be used to build a software layer on top of the internet hardware layer. It then discusses how Docker can be used with different cloud platforms and orchestration tools. It presents Docker CaaS as addressing the needs of both developers and IT operations by supporting the full application lifecycle across any infrastructure or operating system. Key characteristics of Docker CaaS include open APIs, pluggable architecture, and broad ecosystem support.
Docker Container As A Service - March 2016
This document discusses Docker Containers as a Service (CaaS). It begins by showing how Docker can be used to build a software layer on top of the internet hardware layer. It then discusses how Docker can be used with different cloud platforms and orchestration tools. It presents Docker CaaS as addressing the needs of both developers and IT operations by supporting the full application lifecycle across any infrastructure or operating system. Key characteristics of Docker CaaS include open APIs, pluggable architecture, and broad ecosystem support.
Kubernetes for java developers - Tutorial at Oracle Code One 2018
You’re a Java developer? Already familiar with Docker? Want to know more about Kubernetes and its ecosystem for developers? During this session, you’ll get familiar with core Kubernetes concepts (pods, deployments, services, volumes, and so on) before seeing the most-popular and most-productive Kubernetes tools in action, with a special focus on Java development. By the end of the session, you’ll have a better understanding of how you can leverage Kubernetes to speed up your Java deployments on-premises or to any cloud.
CI/CD Across Multiple Environments
Speakers: Vic Iglesias, Benjamin Good, Karl Isenberg
Venue: Google Cloud Next '19
Video: https://www.youtube.com/watch?v=rt287-94Pq4
Continuous Integration and Delivery allows companies to quickly iterate on and deploy their ideas to customers. In doing so, they should strive to have environments that closely match production. Using Kubernetes as the target platform across cloud providers and on-premises environments can help to mitigate some difficulties when ensuring environment parity but many other concerns can arise.
In this talk we will dive into the tools and methodologies available to ensure your code and deployment artifacts can smoothly transition among the various people, environments, and platforms that make up your CI/CD process.
Managing Container Clusters in OpenStack Native Way
This is a presentation from the OpenStack Austin Summit. It talks about managing containers in an OpenStack native way where containers are treated as first class citizens.
FIWARE Global Summit - Leveraging Kubernetes for FIWARE Components Automations
Presentation by Atul Pandey
Technical Specialist, NEC Technologies India
FIWARE Global Summit
27-28 November 2018
Malaga, Spain
Get you Java application ready for Kubernetes !
In this demos loaded talk we’ll explore the best practices to create a Docker image for a Java app (it’s 2019 and new comers such as Jib, CNCF buildpacks are interesting alternatives to Docker builds !) - and how to integrate best with the Kubernetes ecosystem : after explaining main Kubernetes objects and notions, we’ll discuss Helm charts and productivity tools such as Skaffold, Draft and Telepresence.
Introduction to Kubernetes and GKE
This presentation will introduce you to Container, Docker, Kubernetes, and Google Kubernetes Engine (GKE) with a live demo. This also explains Kubernetes basic concepts such as Pod, Deployment, Service, Ingress, and Rolling Update.
See the recorded session on Facebook live here (min 46.49):
https://www.facebook.com/gdgcloudkl/videos/1013942759041907
There's also recorded session on Youtube here (min 46.49):
https://www.youtube.com/watch?v=ht0ynVjkDcI
GDG Cloud KL July Webinar on July 12, 2020
Cloud-native .NET Microservices mit Kubernetes
Mario-Leander Reimer presented on building cloud-native .NET microservices with Kubernetes. He discussed key principles of cloud native applications including designing for distribution, performance, automation, resiliency and elasticity. He also covered containerization with Docker, composing services with Kubernetes and common concepts like deployments, services and probes. Reimer provided examples of Dockerfiles, Kubernetes definitions and using tools like Steeltoe and docker-compose to develop cloud native applications.
A Hitchhiker’s Guide to the Cloud Native Stack. #CDS17
This document provides an overview of cloud native applications and the cloud native stack. It discusses key concepts like microservices, containerization, composition using Docker and Docker Compose, and orchestration using Kubernetes. It provides examples of building a simple microservices application with these technologies and deploying it on Kubernetes. Overall it serves as a guide to developing and deploying cloud native applications.
A hitchhiker‘s guide to the cloud native stack
Container Days 2017, Hamburg: Vortrag von Mario-Leander Reimer (@LeanderReimer, Cheftechnologe bei QAware).
Abstract: Cloud-Größen wie Google, Twitter und Netflix haben die Kernbausteine ihrer Infrastruktur quelloffen verfügbar gemacht. Das Resultat aus vielen Jahren Cloud-Erfahrung ist nun frei zugänglich, und jeder kann seine eigenen Cloud-nativen Anwendungen entwickeln – Anwendungen, die in der Cloud zuverlässig laufen und fast beliebig skalieren. Die einzelnen Bausteine wachsen zu einem großen Ganzen zusammen, dem Cloud Native Stack.
In dieser Session stellen wir die wichtigsten Konzepte und Schlüsseltechnologien vor und bringen dann eine Spring-Cloud-basierte Beispielanwendung schrittweise auf Kubernetes und DC/OS zum Laufen. Dabei diskutieren wir verschiedene praktikable Architekturalternativen.
DCEU 18: Docker Containers in a Serverless World
Jules Testard - Software Engineer, Docker Inc
Since the advent of AWS Lambda in 2014, the Function as a Service (FaaS) programming paradigm has gained a lot of traction in the cloud community. Since then, interest has increased for developers and entreprises to build their own open source solutions on top of Kubernetes. A number of competing frameworks in this space have been developed. In this talk, we will look at three specific frameworks (OpenFaas, Nuclio and FN) and for each framework we will: Show how to create, deploy, and invoke a function using that framework Show how Docker images and containers are used by each framework under the hood Investigate how the frameworks leverage KNative to build, ship and run applications on Kubernetes
Deploy Application on Kubernetes
- What is Kubernetes
- Why we need Kubernetes
- Demo how to deploy application on Kubernetes
Jirayut Nimsaeng
Founder & CEO
Opsta (Thailand) Co., Ltd.
Facebook Record: https://www.facebook.com/ThaiProgrammerSociety/videos/1908659749331066
Coder Live with Thai Programmer Association
June 6, 2022
Orchestrating Linux Containers while tolerating failures
lthough containers are bringing a refreshing flexibility when deploying services in production, the management of those containers in such an environment still requires special care in order to keep the application up and running. In this regard, orchestration platforms like Docker, Kubernetes and Nomad have been trying to alleviate this responsibility, facilitating the task of deploying and maintaining the entire application stack in its desired state. This ensures that a service will be always running, tolerating machine failures, network erratic behavior or software updates and downtime. The purpose of this talk is to explain the mechanisms and architecture of the Docker Engine orchestration platform (using a framework called swarmkit) to tolerate failures of services and machines, from cluster state replication and leader-election to container re-scheduling logic when a host goes down.
Similar to Roman Gorshunov - Airship project onboarding (20)
OpenStack and Kubernetes - A match made for Telco Heaven
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Kubernetes for java developers - Tutorial at Oracle Code One 2018
Kubernetes for java developers - Tutorial at Oracle Code One 2018
Managing Container Clusters in OpenStack Native Way
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FIWARE Global Summit - Leveraging Kubernetes for FIWARE Components Automations
FIWARE Global Summit - Leveraging Kubernetes for FIWARE Components Automations
A Hitchhiker’s Guide to the Cloud Native Stack. #CDS17
A Hitchhiker’s Guide to the Cloud Native Stack. #CDS17
Orchestrating Linux Containers while tolerating failures
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Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
22CYT12-Unit-V-E Waste and its Management.ppt
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
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Rainfall intensity duration frequency curve statistical analysis and modeling...
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Mechanical Engineering on AAI Summer Training Report-003.pdf
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Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Unit-III-ELECTROCHEMICAL STORAGE DEVICES.ppt
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IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
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Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Roman Gorshunov - Airship project onboarding
- 1. Project Onboarding Poland OpenInfra Days 2019
- 2. “Airship is a collection of loosely coupled, but interoperable, open source tools that declaratively automate cloud provisioning.” Airship Overview • Orchestration: Batteries included • Singular Deployment Method • Predictable Upgrades • Repeatable Multi-site Deployments • Resiliency Motivations
- 3. Airship Projects Pegleg Document Aggregation Shipyard Front door to Airship; orchestrates activities Drydock Declarative bare-metal provisioning Promenade Declarative HA Kubernetes installer Deckhand Document management and layering Armada Declarative management of complex Helm deployments Divingbell Declarative bare-metal configuration management Treasuremap Reference YAML manifests and CICD 5
- 4. Airship Control Plane Node(s) Pegleg PromenadeDrydock Shipyard Armada Deckhand PostgreSQL Barbican Kubernetes Node (1..N) Git Repo (e.g. Treasuremap) Step 1: Prepare for Deployment Pegleg fetches YAML documents from Git Pegleg feeds them to Shipyard, which forwards them to Deckhand for storage Deckhand saves secrets to Barbican, other data to PostgreSQL Airship components validate the YAML documents 1 2 3 4 1 2 3 4 Next, the Shipyard `deploy_site` (or `update_site`) API is invoked Kubernetes Node (1..N) Nodes (1..N) Off-site On-site
- 5. Airship Control Plane Node(s) Pegleg MaaS PromenadeDrydock Shipyard Armada Deckhand Shipyard invokes Drydock to perform bare metal provisioning Drydock drives MaaS, which provisions the node(s) Drydock retrieves a boot action Kubernetes join script from Promenade The provisioned node joins the Kubernetes cluster upon reboot 1 Step 2: Infrastructure Provisioning Kubernetes Node (1..N) Kubernetes Node (1..N) Nodes (1..N) Kubernetes Nodes 2 4 3 1 2 3 4 Next, the workload software is deployed/updated Off-site On-site
- 6. Airship Control Plane Nodes Pegleg PromenadeDrydock Shipyard Armada Deckhand Tiller Shipyard invokes Armada to perform software install/update Armada feeds rendered helm charts to Tiller Tiller installs/updates charts via the Kubernetes API Step 3: Workload Deployment 1 2 3 1 2 3 Off-site On-site Kubernetes Node (1..N) Kubernetes Node (1..N) Kubernetes Cluster Nodes (1..N) Kubernetes API Server Kubelet Kubelet
- 7. •Leverage Kubernetes Cluster API •Containerized Network Function (CNF) support •Leverage cloud-native workflow management •Helm v3 and Armada Operator •Cloud Harbor •Service Layers •Edge use cases •Multus integration •Multi-tenancy •MetalKube / Ironic integration •KubeADM integration Strategic Focus – Post 1.0 Technical Roadmap
- 8. Get Involved • Airship Wiki • airship-discuss@lists.openstack.org • Freenode IRC: #airshipit • IRC team meetings are Tuesdays @ 18:00 CEST 1h • Design meeting 1 are Tuesdays @ 15:00 CEST 1.5h • Design meeting 2 are Thursdays @ 17:00 CEST 1.5h • OpenStack Foundation Touchpoint Call bi-weekly Get Ramped Up • Airship architectural overview video • Treasuremap site authoring guide • Treasuremap sample site documents • Airship developer getting started guide
- 9. Questions? Join the Community! Mailing Lists: lists.airshipit.org FreeNode IRC: #airshipit Website: www.airshipit.org Wiki: wiki.openstack.org/airship Git Repo: opendev.org/airhip Join our weekly IRC and Design meetings