This document discusses Google Kubernetes Engine (GKE) and Kubernetes. It explains that GKE allows users to deploy and manage containerized applications using Kubernetes clusters hosted on Google Cloud infrastructure. The document also mentions Google Cloud Speech API and using BigQuery with Ruby.
Handling Kubernetes clusters at scale can be challenging. This talk will revolve around my feedback and personal opinion regarding several configuration/deployment tools I have used and are currently using such Terraform, ArgoCD, Kustomize and Helm.
Feel free to send me a tweet if you have any questions :)
This document provides an introduction and overview of Kubernetes. It begins with introducing the presenter and their background. It then covers the history and origins of Kubernetes at Google, provides a definition and explanation of Kubernetes and some of its key features like container orchestration, horizontal scaling, rolling deployments and rollbacks, self-healing, and service discovery. It demonstrates Kubernetes concepts like pods, deployments, services and how they relate. It also briefly mentions some advanced Kubernetes topics before opening for questions.
Kubernetes GitOps featuring GitHub, Kustomize and ArgoCDSunnyvale
A brief dissertation about using GitOps paradigm to operate an application on multiple Kubernetes environments thanks to GitHub, ArgoCD and Kustomize. A talk about this matters has been taken at the event #CloudConf2020
This slide set is for the webinar we hosted about becoming a Git power user. It's a slide set for webinar series talking about different topics related to Git power usage. Covered topics are:
- Git Aliases
- Rewriting history
- Fast context switching
- Different merging strategies
Git ops: Git based application deployment patterns for KubernetesShahidh K Muhammed
Shahidh talks about various patterns revolving around GitOps (Git + Devops) for applications deployment onto Kubernetes and introduces Gitkube (https://github.com/hasura/gitkube) as a tool to do GitOps.
This document discusses Google Kubernetes Engine (GKE) and Kubernetes. It explains that GKE allows users to deploy and manage containerized applications using Kubernetes clusters hosted on Google Cloud infrastructure. The document also mentions Google Cloud Speech API and using BigQuery with Ruby.
Handling Kubernetes clusters at scale can be challenging. This talk will revolve around my feedback and personal opinion regarding several configuration/deployment tools I have used and are currently using such Terraform, ArgoCD, Kustomize and Helm.
Feel free to send me a tweet if you have any questions :)
This document provides an introduction and overview of Kubernetes. It begins with introducing the presenter and their background. It then covers the history and origins of Kubernetes at Google, provides a definition and explanation of Kubernetes and some of its key features like container orchestration, horizontal scaling, rolling deployments and rollbacks, self-healing, and service discovery. It demonstrates Kubernetes concepts like pods, deployments, services and how they relate. It also briefly mentions some advanced Kubernetes topics before opening for questions.
Kubernetes GitOps featuring GitHub, Kustomize and ArgoCDSunnyvale
A brief dissertation about using GitOps paradigm to operate an application on multiple Kubernetes environments thanks to GitHub, ArgoCD and Kustomize. A talk about this matters has been taken at the event #CloudConf2020
This slide set is for the webinar we hosted about becoming a Git power user. It's a slide set for webinar series talking about different topics related to Git power usage. Covered topics are:
- Git Aliases
- Rewriting history
- Fast context switching
- Different merging strategies
Git ops: Git based application deployment patterns for KubernetesShahidh K Muhammed
Shahidh talks about various patterns revolving around GitOps (Git + Devops) for applications deployment onto Kubernetes and introduces Gitkube (https://github.com/hasura/gitkube) as a tool to do GitOps.
The Kubeflow control plane includes kfctl and the Kubeflow operator which are used to deploy, manage and monitor Kubeflow applications on Kubernetes clusters. Kfctl is a CLI tool that uses KfDef configuration files to build and apply Kubeflow manifests from a repository. The Kubeflow operator watches for KfDef custom resources and installs Kubeflow by creating the defined applications.
CloudZone's Meetup at Google offices, 20.08.2018
Covering Google Cloud Platform Kubernetes Engine in Depth, including networking, compute, storage, monitoring & logging
This document discusses development tooling and provides an overview of the tools used at tado° for various stages of development including collaboration, development, build, test, deployment, production, and logging/monitoring. It recommends tools like Google Apps, Github, Jenkins, Gradle, AWS, Packer.io, Logstash, and CloudWatch and provides examples of how they are used at tado° for tasks like source control, continuous integration, deployment, and analytics. It also includes information about the presenter and an invitation to learn more about job opportunities at tado°.
Introduction of cloud native CI/CD on kubernetesKyohei Mizumoto
This document discusses setting up continuous integration and continuous delivery (CI/CD) pipelines on Kubernetes using Concourse CI and Argo CD. It provides an overview of each tool, instructions for getting started with Concourse using Helm and configuring sample pipelines in YAML, and instructions for installing and configuring applications in Argo CD through application manifests.
Configuration Management for the Cloud Native world with GitOps and Helm - To...PROIDEA
Traditional DevOps wisdom is to utilize automation tools for configuring all the systems, so all pieces of configuration should be described with code stored in version control. Then the people responsible for these systems can use standard Dev practices to collaborate on this code (reviews, tests, quality gates, etc.).
With the advent of cloud native systems, we now utilize containers and orchestrators to define how our applications should be operated.
Kubernetes provides a declarative API, so you can describe the desired state of the system. And then it is the role of the control plane to operate the cluster (make the actual state match the desired state).
But we still need config mgmt for API objects to the point when they are applied to the cluster.
Helm helps organizing these configs into charts, template them, and manage releases. And GitOps lets you use a git repo as a single source of truth for the desired state of the whole system. Then all changes to this state are delivered as git commits instead of using kubectl apply or helm upgrade.
In this talk I will introduce the GitOps model for operating cloud native environments and give a short demo.
How to build an Android Project with Kotlin, Anko, Rx and Dagger 2.
Please access my Github repository for full project: https://github.com/lalbuquerque/DARK
Presented at: Config Management Camp, Ghent, 2020-02
Kubernetes provides a declarative API, so you can describe the desired state of the system. And then it is the role of the control plane to operate the cluster (make the actual state match the desired state).
But we still need config mgmt for API objects to the point when they are applied to the cluster.
Helm helps to organize these configs into charts, template them, and manage releases. And GitOps lets you use a git repo as a single source of truth for the desired state of the whole system. Then all changes to this state are delivered as git commits instead of using kubectl apply or helm upgrade.
In this talk I will introduce the GitOps model for operating cloud native environments and give a short demo.
In this talk I show two CNCF projects:
- Flux CD: https://fluxcd.io/
- Helm: https://helm.sh/
Tips and tricks to maximize performance and minimize serverless costs with Firebase and Google Cloud Functions. Live examples and analysis to show that GCF is the cheapest function provider, compared to Azure Functions and AWS Lambda.
When you build a serverless app, you either tie yourself to a cloud provider, or you end up building your own serverless stack. Knative provides a better choice. Knative extends Kubernetes to provide a set of middleware components (build, serving, events) for modern, source-centric, and container-based apps that can run anywhere. In this talk, we’ll see how we can use Knative primitives to build a serverless app that utilizes the Machine Learning magic of the cloud.
As part of our DevOps Day 2019 Series, we gathered AWS and DevOps experts to educate the group about the power and challenges of working with Kubernetes.
Contents:
• Containers: A short story
• Quick Kubernetes
• Challenges faced
• How Kubernetes helps
By: Rahul Sharma | DevOps Engineer @ SourceFuse
Google Cloud Platform provides services well-suited for a Splatoon stats tracking application including App Engine's cron jobs, BigQuery for affordable large data storage and analysis, Stackdriver for error reporting and debugging, and Git integration. App Engine's Ruby runtime is suitable for the application and BigQuery can handle data inserts from API responses to track kill ratios over time with Stackdriver aiding in issue identification.
Kubernetes & Google Container Engine @ mablJoseph Lust
Mabl uses Google Container Engine (GKE) and Kubernetes to run automated tests at scale, validating 100 million web pages per month. Some key points:
- Mabl builds a Docker image containing test code and runs it in containers on GKE for isolation, consistency, and scalability.
- Kubernetes concepts like pods, nodes, and replication controllers are used to manage and schedule containers. Jobs ensure tests run to completion and resources are allocated properly.
- Auto-scaling allows the GKE cluster to dynamically add or remove nodes based on load, while pub/sub decouples scheduling from the cluster.
- Challenges included cleaning up old pods, defining resource limits, and adjusting auto-
Git allows creating snapshots of project files called commits. Users can create branches to develop features independently and then merge them together. Common commands include commit to save snapshots, checkout to switch branches, merge to combine work, and push/pull to share commits between remote repositories. Advanced techniques involve rebasing to clean up commit histories and resolving merge conflicts when branches diverge.
**Watch the full webinar at https://codefresh.io/events/terraform-gitops-codefresh/
Today we write "Infrastructure as Code" and even "Pipelines as Code", so let's start treating our "code as code" and practice CI/CD with GitOps! In this talk, we'll show you how we build and deploy applications with Terraform using GitOps and Codefresh. Cloud Posse is a Terraform power user that has developed over 130 Terraform modules which are free and open source. We'll share how we handle automation with security while making the process easy for engineers.
Through the looking glass an intro to scalable, distributed counting in data...Geoff Cooney
Lightning talk I gave at GCP Boston meetup for a quick hands on intro to google dataflow. Example based on the public pubsub topic described here: https://github.com/googlecodelabs/cloud-dataflow-nyc-taxi-tycoon
Deploying containerized applications with KubeappsJanakiram MSV
Kubeapps is a Kubernetes dashboard that supercharges your Kubernetes cluster with simple browse and click deployment of apps in any format. Building on Bitnami’s contributions to leading open source projects, Kubeapps provides a complete application delivery environment that empowers users to launch, review and share applications.
The Kubeflow control plane includes kfctl and the Kubeflow operator which are used to deploy, manage and monitor Kubeflow applications on Kubernetes clusters. Kfctl is a CLI tool that uses KfDef configuration files to build and apply Kubeflow manifests from a repository. The Kubeflow operator watches for KfDef custom resources and installs Kubeflow by creating the defined applications.
CloudZone's Meetup at Google offices, 20.08.2018
Covering Google Cloud Platform Kubernetes Engine in Depth, including networking, compute, storage, monitoring & logging
This document discusses development tooling and provides an overview of the tools used at tado° for various stages of development including collaboration, development, build, test, deployment, production, and logging/monitoring. It recommends tools like Google Apps, Github, Jenkins, Gradle, AWS, Packer.io, Logstash, and CloudWatch and provides examples of how they are used at tado° for tasks like source control, continuous integration, deployment, and analytics. It also includes information about the presenter and an invitation to learn more about job opportunities at tado°.
Introduction of cloud native CI/CD on kubernetesKyohei Mizumoto
This document discusses setting up continuous integration and continuous delivery (CI/CD) pipelines on Kubernetes using Concourse CI and Argo CD. It provides an overview of each tool, instructions for getting started with Concourse using Helm and configuring sample pipelines in YAML, and instructions for installing and configuring applications in Argo CD through application manifests.
Configuration Management for the Cloud Native world with GitOps and Helm - To...PROIDEA
Traditional DevOps wisdom is to utilize automation tools for configuring all the systems, so all pieces of configuration should be described with code stored in version control. Then the people responsible for these systems can use standard Dev practices to collaborate on this code (reviews, tests, quality gates, etc.).
With the advent of cloud native systems, we now utilize containers and orchestrators to define how our applications should be operated.
Kubernetes provides a declarative API, so you can describe the desired state of the system. And then it is the role of the control plane to operate the cluster (make the actual state match the desired state).
But we still need config mgmt for API objects to the point when they are applied to the cluster.
Helm helps organizing these configs into charts, template them, and manage releases. And GitOps lets you use a git repo as a single source of truth for the desired state of the whole system. Then all changes to this state are delivered as git commits instead of using kubectl apply or helm upgrade.
In this talk I will introduce the GitOps model for operating cloud native environments and give a short demo.
How to build an Android Project with Kotlin, Anko, Rx and Dagger 2.
Please access my Github repository for full project: https://github.com/lalbuquerque/DARK
Presented at: Config Management Camp, Ghent, 2020-02
Kubernetes provides a declarative API, so you can describe the desired state of the system. And then it is the role of the control plane to operate the cluster (make the actual state match the desired state).
But we still need config mgmt for API objects to the point when they are applied to the cluster.
Helm helps to organize these configs into charts, template them, and manage releases. And GitOps lets you use a git repo as a single source of truth for the desired state of the whole system. Then all changes to this state are delivered as git commits instead of using kubectl apply or helm upgrade.
In this talk I will introduce the GitOps model for operating cloud native environments and give a short demo.
In this talk I show two CNCF projects:
- Flux CD: https://fluxcd.io/
- Helm: https://helm.sh/
Tips and tricks to maximize performance and minimize serverless costs with Firebase and Google Cloud Functions. Live examples and analysis to show that GCF is the cheapest function provider, compared to Azure Functions and AWS Lambda.
When you build a serverless app, you either tie yourself to a cloud provider, or you end up building your own serverless stack. Knative provides a better choice. Knative extends Kubernetes to provide a set of middleware components (build, serving, events) for modern, source-centric, and container-based apps that can run anywhere. In this talk, we’ll see how we can use Knative primitives to build a serverless app that utilizes the Machine Learning magic of the cloud.
As part of our DevOps Day 2019 Series, we gathered AWS and DevOps experts to educate the group about the power and challenges of working with Kubernetes.
Contents:
• Containers: A short story
• Quick Kubernetes
• Challenges faced
• How Kubernetes helps
By: Rahul Sharma | DevOps Engineer @ SourceFuse
Google Cloud Platform provides services well-suited for a Splatoon stats tracking application including App Engine's cron jobs, BigQuery for affordable large data storage and analysis, Stackdriver for error reporting and debugging, and Git integration. App Engine's Ruby runtime is suitable for the application and BigQuery can handle data inserts from API responses to track kill ratios over time with Stackdriver aiding in issue identification.
Kubernetes & Google Container Engine @ mablJoseph Lust
Mabl uses Google Container Engine (GKE) and Kubernetes to run automated tests at scale, validating 100 million web pages per month. Some key points:
- Mabl builds a Docker image containing test code and runs it in containers on GKE for isolation, consistency, and scalability.
- Kubernetes concepts like pods, nodes, and replication controllers are used to manage and schedule containers. Jobs ensure tests run to completion and resources are allocated properly.
- Auto-scaling allows the GKE cluster to dynamically add or remove nodes based on load, while pub/sub decouples scheduling from the cluster.
- Challenges included cleaning up old pods, defining resource limits, and adjusting auto-
Git allows creating snapshots of project files called commits. Users can create branches to develop features independently and then merge them together. Common commands include commit to save snapshots, checkout to switch branches, merge to combine work, and push/pull to share commits between remote repositories. Advanced techniques involve rebasing to clean up commit histories and resolving merge conflicts when branches diverge.
**Watch the full webinar at https://codefresh.io/events/terraform-gitops-codefresh/
Today we write "Infrastructure as Code" and even "Pipelines as Code", so let's start treating our "code as code" and practice CI/CD with GitOps! In this talk, we'll show you how we build and deploy applications with Terraform using GitOps and Codefresh. Cloud Posse is a Terraform power user that has developed over 130 Terraform modules which are free and open source. We'll share how we handle automation with security while making the process easy for engineers.
Through the looking glass an intro to scalable, distributed counting in data...Geoff Cooney
Lightning talk I gave at GCP Boston meetup for a quick hands on intro to google dataflow. Example based on the public pubsub topic described here: https://github.com/googlecodelabs/cloud-dataflow-nyc-taxi-tycoon
Deploying containerized applications with KubeappsJanakiram MSV
Kubeapps is a Kubernetes dashboard that supercharges your Kubernetes cluster with simple browse and click deployment of apps in any format. Building on Bitnami’s contributions to leading open source projects, Kubeapps provides a complete application delivery environment that empowers users to launch, review and share applications.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
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.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
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
imaging, emphasizing addressing false positives and resource efficiency.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
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.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.