#Kubernetes in Bare Metal

[virtualizationhowto]

k0s vs k3s - Battle of the Tiny Kubernetes distros

k0s vs k3s - Battle of the Tiny Kubernetes distros #100daysofhomelab #homelab @vexpert #vmwarecommunities #KubernetesDistributions, #k0svsk3s, #RunningKubernetes, #LightweightKubernetes, #KubernetesInEdgeComputing, #KubernetesInBareMetal

Kubernetes has redefined the management of containerized applications. The rich ecosystem of Kubernetes distributions testifies to its widespread adoption and versatility. Today, we compare k0s vs k3s, two unique Kubernetes distributions designed to seamlessly run Kubernetes across varied infrastructures, from cloud instances to bare metal and edge computing settings. Those with home labs will…

[yourservicesinfo]

Docker Migration Services: A Seamless Shift to Containerization

In today’s fast-paced tech world, businesses are continuously looking for ways to boost performance, scalability, and flexibility. One powerful way to achieve this is through Docker migration. Docker helps you containerize applications, making them easier to deploy, manage, and scale. But moving existing apps to Docker can be challenging without the right expertise.

Let’s explore what Docker migration services are, why they matter, and how they can help transform your infrastructure.

What Is Docker Migration?

Docker migration is the process of moving existing applications from traditional environments (like virtual machines or bare-metal servers) to Docker containers. This involves re-architecting applications to work within containers, ensuring compatibility, and streamlining deployments.

Why Migrate to Docker?

Here’s why businesses are choosing Docker migration services:

1. Improved Efficiency

Docker containers are lightweight and use system resources more efficiently than virtual machines.

2. Faster Deployment

Containers can be spun up in seconds, helping your team move faster from development to production.

3. Portability

Docker containers run the same way across different environments – dev, test, and production – minimizing issues.

4. Better Scalability

Easily scale up or down based on demand using container orchestration tools like Kubernetes or Docker Swarm.

5. Cost-Effective

Reduced infrastructure and maintenance costs make Docker a smart choice for businesses of all sizes.

What Do Docker Migration Services Include?

Professional Docker migration services guide you through every step of the migration journey. Here's what’s typically included:

- Assessment & Planning

Analyzing your current environment to identify what can be containerized and how.

- Application Refactoring

Modifying apps to work efficiently within containers without breaking functionality.

- Containerization

Creating Docker images and defining services using Dockerfiles and docker-compose.

- Testing & Validation

Ensuring that the containerized apps function as expected across environments.

- CI/CD Integration

Setting up pipelines to automate testing, building, and deploying containers.

- Training & Support

Helping your team get up to speed with Docker concepts and tools.

Challenges You Might Face

While Docker migration has many benefits, it also comes with some challenges:

Compatibility issues with legacy applications

Security misconfigurations

Learning curve for teams new to containers

Need for monitoring and orchestration setup

This is why having experienced Docker professionals onboard is critical.

Who Needs Docker Migration Services?

Docker migration is ideal for:

Businesses with legacy applications seeking modernization

Startups looking for scalable and portable solutions

DevOps teams aiming to streamline deployments

Enterprises moving towards a microservices architecture

Final Thoughts

Docker migration isn’t just a trend—it’s a smart move for businesses that want agility, reliability, and speed in their development and deployment processes. With expert Docker migration services, you can transition smoothly, minimize downtime, and unlock the full potential of containerization.

[hawkstack]

🚀 Enterprise Kubernetes Storage with Red Hat OpenShift Data Foundation

As enterprises continue to adopt Kubernetes for container orchestration, the demand for scalable, resilient, and enterprise-grade storage solutions has never been higher. While Kubernetes excels in managing stateless applications, managing stateful workloads—such as databases, messaging queues, and AI/ML pipelines—poses unique challenges. This is where Red Hat OpenShift Data Foundation (ODF) steps in as a game-changer.

📦 What is Red Hat OpenShift Data Foundation?

Red Hat OpenShift Data Foundation (formerly OpenShift Container Storage) is a software-defined storage solution designed specifically for OpenShift environments. Built on Ceph and NooBaa, ODF provides a unified storage layer that seamlessly supports block, file, and object storage within your Kubernetes infrastructure.

ODF delivers highly available, scalable, and secure storage for cloud-native workloads, empowering DevOps teams to run stateful applications confidently across hybrid and multi-cloud environments.

🔧 Key Features of OpenShift Data Foundation

1. Unified Storage for Kubernetes

ODF supports:

Block Storage for databases and persistent workloads

File Storage for legacy applications and shared volumes

Object Storage for cloud-native applications, backup, and AI/ML data lakes

2. Multi-Cloud & Hybrid Cloud Ready

Deploy ODF on bare metal, private clouds, public clouds, or hybrid environments. With integrated NooBaa technology, it allows seamless object storage across AWS S3, Azure Blob, and on-premises storage.

3. Integrated with OpenShift

ODF is tightly integrated with Red Hat OpenShift, allowing:

Native support for Persistent Volume Claims (PVCs)

Automated provisioning and scaling

Built-in monitoring through OpenShift Console and Prometheus/Grafana

4. Data Resilience & High Availability

Through Ceph under the hood, ODF offers:

Data replication across nodes

Self-healing storage clusters

Built-in erasure coding for space-efficient redundancy

5. Security & Compliance

ODF supports:

Encryption at rest and in transit

Role-Based Access Control (RBAC)

Integration with enterprise security policies and key management services (KMS)

🧩 Common Use Cases

Database as a Service (DBaaS) on Kubernetes

CI/CD Pipelines with persistent cache

AI/ML Workloads requiring massive unstructured data

Kafka, Elasticsearch, and other stateful operators

Backup & Disaster Recovery for OpenShift clusters

🛠️ Architecture Overview

At a high level, ODF deploys the following components:

ODF Operator: Automates lifecycle and management

CephCluster: Manages block and file storage

NooBaa Operator: Manages object storage abstraction

Multicloud Object Gateway (MCG): Bridges cloud and on-prem storage

The ODF stack ensures zero downtime for workloads and automated healing in the event of hardware failure or node loss.

🚀 Getting Started

To deploy OpenShift Data Foundation:

Install OpenShift on your preferred infrastructure.

Enable the ODF Operator from OperatorHub.

Configure storage cluster using local devices, AWS EBS, or any supported backend.

Create storage classes for your apps to consume via PVCs.

Pro Tip: Use OpenShift’s integrated dashboard to visualize storage usage, health, and performance metrics out of the box.

🧠 Final Thoughts

Red Hat OpenShift Data Foundation is more than just a storage solution—it's a Kubernetes-native data platform that gives you flexibility, resilience, and performance at scale. Whether you're building mission-critical microservices or deploying petabyte-scale AI workloads, ODF is designed to handle your stateful needs in an enterprise-ready way.

Embrace the future of cloud-native storage with Red Hat OpenShift Data Foundation.For more details www.hawkstack.com 

[straliatechnologies123]

Exploring Oracle Cloud Infrastructure: Driving Business Innovation and Efficiency

In the rapidly evolving digital landscape, businesses are increasingly leveraging cloud technologies to streamline operations and drive innovation. Among the leading cloud computing providers, Oracle Cloud Infrastructure (OCI) stands out by offering a robust suite of services designed to meet the unique demands of enterprises.

What Is Oracle Cloud Infrastructure?

Oracle Cloud Infrastructure is a comprehensive set of cloud services that deliver computing power, storage, and networking capabilities. It supports both traditional and modern workloads, offering flexible and secure solutions for businesses. With its high-performance infrastructure, OCI enables organizations to run mission-critical applications efficiently.

Key Features of Oracle Cloud Infrastructure

Oracle Cloud Infrastructure is built for enterprise-grade applications, offering superior performance, reliability, and scalability. Its multi-cloud capabilities allow businesses to integrate with other cloud platforms while maintaining centralized management. OCI also provides advanced data management solutions with Oracle Autonomous Database, delivering self-driving, self-securing, and self-repairing database capabilities. Enhanced security is a key feature, with built-in encryption, identity management, and compliance controls. Additionally, Oracle’s AI and machine learning tools empower businesses to derive actionable insights from their data.

Popular Oracle Cloud Services

Oracle Compute offers scalable virtual machines and bare metal servers, providing flexibility for various workloads. Oracle Cloud Storage delivers secure, reliable storage options for structured and unstructured data. Oracle Kubernetes Engine facilitates the deployment and management of containerized applications. Oracle Autonomous Database ensures high performance, security, and automatic management without human intervention. Oracle Analytics provides advanced data visualization and predictive insights to support data-driven decision-making.

Benefits of Using Oracle Cloud Infrastructure

Businesses leveraging Oracle Cloud Infrastructure experience numerous advantages. OCI’s high-performance architecture ensures low-latency and efficient operations for demanding applications. With its cost-effective pricing model, companies can optimize expenses by paying only for the resources they use. OCI’s built-in disaster recovery and failover solutions provide enhanced reliability and business continuity. Additionally, Oracle’s focus on security and compliance offers businesses peace of mind, ensuring their data remains protected.

Conclusion

Oracle Cloud Infrastructure offers a powerful and flexible environment for organizations seeking to accelerate their digital transformation. From autonomous database management to scalable compute resources, OCI provides the tools and infrastructure needed to drive innovation and achieve business goals.

Ready to explore how Oracle Cloud Infrastructure can transform your business operations? Partner with leading cloud computing providers and choose the best cloud computing provider to unlock the full potential of the cloud today.

[infernovm]

What is KubeVirt? How does it migrate VMware workloads to Kubernetes?

There are many different types of abstractions used in modern IT deployments. While it’s still possible to run applications on bare metal, that approach doesn’t fully optimize hardware utilization. That’s where virtualization comes in. With virtualization, one physical piece of hardware can be abstracted or ‘virtualized’ to enable more workloads to run. Modern virtualization isn’t just about…

[jcmarchi]

The role of machine learning in enhancing cloud-native container security - AI News

New Post has been published on https://thedigitalinsider.com/the-role-of-machine-learning-in-enhancing-cloud-native-container-security-ai-news/

The role of machine learning in enhancing cloud-native container security - AI News

The advent of more powerful processors in the early 2000’s shipping with support in hardware for virtualisation started the computing revolution that led, in time, to what we now call the cloud. With single hardware instances able to run dozens, if not hundreds of virtual machines concurrently, businesses could offer their users multiple services and applications that would otherwise have been financially impractical, if not impossible.

But virtual machines (VMs) have several downsides. Often, an entire virtualised operating system is overkill for many applications, and although very much more malleable, scalable, and agile than a fleet of bare-metal servers, VMs still require significantly more memory and processing power, and are less agile than the next evolution of this type of technology – containers. In addition to being more easily scaled (up or down, according to demand), containerised applications consist of only the necessary parts of an application and its supporting dependencies. Therefore apps based on micro-services tend to be lighter and more easily configurable.

Virtual machines exhibit the same security issues that affect their bare-metal counterparts, and to some extent, container security issues reflect those of their component parts: a mySQL bug in a specific version of the upstream application will affect containerised versions too. With regards to VMs, bare metal installs, and containers, cybersecurity concerns and activities are very similar. But container deployments and their tooling bring specific security challenges to those charged with running apps and services, whether manually piecing together applications with choice containers, or running in production with orchestration at scale.

Container-specific security risks

Misconfiguration: Complex applications are made up of multiple containers, and misconfiguration – often only a single line in a .yaml file, can grant unnecessary privileges and increase the attack surface. For example, although it’s not trivial for an attacker to gain root access to the host machine from a container, it’s still a too-common practice to run Docker as root, with no user namespace remapping, for example.

Vulnerable container images: In 2022, Sysdig found over 1,600 images identified as malicious in Docker Hub, in addition to many containers stored in the repo with hard-coded cloud credentials, ssh keys, and NPM tokens. The process of pulling images from public registries is opaque, and the convenience of container deployment (plus pressure on developers to produce results, fast) can mean that apps can easily be constructed with inherently insecure, or even malicious components.

Orchestration layers: For larger projects, orchestration tools such as Kubernetes can increase the attack surface, usually due to misconfiguration and high levels of complexity. A 2022 survey from D2iQ found that only 42% of applications running on Kubernetes made it into production – down in part to the difficulty of administering large clusters and a steep learning curve.

According to Ari Weil at Akamai, “Kubernetes is mature, but most companies and developers don’t realise how complex […] it can be until they’re actually at scale.”

Container security with machine learning

The specific challenges of container security can be addressed using machine learning algorithms trained on observing the components of an application when it’s ‘running clean.’ By creating a baseline of normal behaviour, machine learning can identify anomalies that could indicate potential threats from unusual traffic, unauthorised changes to configuration, odd user access patterns, and unexpected system calls.

ML-based container security platforms can scan image repositories and compare each against databases of known vulnerabilities and issues. Scans can be automatically triggered and scheduled, helping prevent the addition of harmful elements during development and in production. Auto-generated audit reports can be tracked against standard benchmarks, or an organisation can set its own security standards – useful in environments where highly-sensitive data is processed.

The connectivity between specialist container security functions and orchestration software means that suspected containers can be isolated or closed immediately, insecure permissions revoked, and user access suspended. With API connections to local firewalls and VPN endpoints, entire environments or subnets can be isolated, or traffic stopped at network borders.

Final word

Machine learning can reduce the risk of data breach in containerised environments by working on several levels. Anomaly detection, asset scanning, and flagging potential misconfiguration are all possible, plus any degree of automated alerting or amelioration are relatively simple to enact.

The transformative possibilities of container-based apps can be approached without the security issues that have stopped some from exploring, developing, and running microservice-based applications. The advantages of cloud-native technologies can be won without compromising existing security standards, even in high-risk sectors.

(Image source)

[ubuntu-server]

Canonical announces 12 year Kubernetes LTS 

Canonical’s Kubernetes LTS (Long Term Support) will support FedRAMP compliance and receive at least 12 years of committed security maintenance and enterprise support on bare metal, public clouds, OpenStack, Canonical MicroCloud and VMware. February 11,  2025 Today, Canonical announced a 12 year security maintenance and support commitment starting with  Kubernetes 1.32. The new release is easy to…

[shivamthakrejr]

AI Data Center Builder Nscale Secures $155M Investment

Nscale Ltd., a startup based in London that creates data centers designed for artificial intelligence tasks, has raised $155 million to expand its infrastructure.

The Series A funding round was announced today. Sandton Capital Partners led the investment, with contributions from Kestrel 0x1, Blue Sky Capital Managers, and Florence Capital. The funding announcement comes just a few weeks after one of Nscale’s AI clusters was listed in the Top500 as one of the world’s most powerful supercomputers.

The Svartisen Cluster took the 156th spot with a maximum performance of 12.38 petaflops and 66,528 cores. Nscale built the system using servers that each have six chips from Advanced Micro Devices Inc.: two central processing units and four MI250X machine learning accelerators. The MI250X has two graphics cards made with a six-nanometer process, plus 128 gigabytes of memory to store data for AI models.

The servers are connected through an Ethernet network that Nscale created using chips from Broadcom Inc. The network uses a technology called RoCE, which allows data to move directly between two machines without going through their CPUs, making the process faster. RoCE also automatically handles tasks like finding overloaded network links and sending data to other connections to avoid delays.

On the software side, Nscale’s hardware runs on a custom-built platform that manages the entire infrastructure. It combines Kubernetes with Slurm, a well-known open-source tool for managing data center systems. Both Kubernetes and Slurm automatically decide which tasks should run on which server in a cluster. However, they are different in a few ways. Kubernetes has a self-healing feature that lets it fix certain problems on its own. Slurm, on the other hand, uses a network technology called MPI, which moves data between different parts of an AI task very efficiently.

Nscale built the Svartisen Cluster in Glomfjord, a small village in Norway, which is located inside the Arctic Circle. The data center (shown in the picture) gets its power from a nearby hydroelectric dam and is directly connected to the internet through a fiber-optic cable. The cable has double redundancy, meaning it can keep working even if several key parts fail. 

The company makes its infrastructure available to customers in multiple ways. It offers AI training clusters and an inference service that automatically adjusts hardware resources depending on the workload. There are also bare-metal infrastructure options, which let users customize the software that runs their systems in more detail.

Customers can either download AI models from Nscale's algorithm library or upload their own. The company says it provides a ready-made compiler toolkit that helps convert user workloads into a format that runs smoothly on its servers. For users wanting to create their own custom AI solutions, Nscale provides flexible, high-performance infrastructure that acts as a builder ai platform, helping them optimize and deploy personalized models at scale.

Right now, Nscale is building data centers that together use 300 megawatts of power. That’s 10 times more electricity than the company’s Glomfjord facility uses. Using the Series A funding round announced today, Nscale will grow its pipeline by 1,000 megawatts. “The biggest challenge to scaling the market is the huge amount of continuous electricity needed to power these large GPU superclusters,” said Nscale CEO Joshua Payne. Read this link also : https://sifted.eu/articles/tech-events-2025

“Nscale has a 1.3GW pipeline of sites in our portfolio, which lets us design everything from scratch – the data center, the supercluster, and the cloud environment – all the way through for our customers.” The company will build new data centers in North America and Europe. The company plans to build 120 megawatts of data center capacity next year. The new infrastructure will support Nscale’s upcoming public cloud service, which will focus on training and inference tasks, and is expected to launch in the first quarter of 2025.

[govindhtech]

What Is AWS EKS? Use EKS To Simplify Kubernetes On AWS

What Is AWS EKS?

AWS EKS, a managed service, eliminates the need to install, administer, and maintain your own Kubernetes control plane on Amazon Web Services (AWS). Kubernetes simplifies containerized app scaling, deployment, and management.

How it Works?

AWS Elastic Kubernetes Service (Amazon EKS) is a managed Kubernetes solution for on-premises data centers and the AWS cloud. The Kubernetes control plane nodes in the cloud that are in charge of scheduling containers, controlling application availability, storing cluster data, and other crucial functions are automatically managed in terms of scalability and availability by AWS EKS.

You can benefit from all of AWS infrastructure’s performance, scalability, dependability, and availability with Amazon EKS. You can also integrate AWS networking and security services. When deployed on-premises on AWS Outposts, virtual machines, or bare metal servers, EKS offers a reliable, fully supported Kubernetes solution with integrated tools.Image Credit To Amazon Web Services

AWS EKS advantages

Integration of AWS Services

Make use of the integrated AWS services, including EC2, VPC, IAM, EBS, and others.

Cost reductions with Kubernetes

Use automated Kubernetes application scalability and effective computing resource provisioning to cut expenses.

Security of automated Kubernetes control planes

By automatically applying security fixes to the control plane of your cluster, you can guarantee a more secure Kubernetes environment

Use cases

Implement in a variety of hybrid contexts

Run Kubernetes in your data centers and manage your Kubernetes clusters and apps in hybrid environments.

Workflows for model machine learning (ML)

Use the newest GPU-powered instances from Amazon Elastic Compute Cloud (EC2), such as Inferentia, to efficiently execute distributed training jobs. Kubeflow is used to deploy training and inferences.

Create and execute web apps

With innovative networking and security connections, develop applications that operate in a highly available configuration across many Availability Zones (AZs) and automatically scale up and down.

Amazon EKS Features

Running Kubernetes on AWS and on-premises is made simple with Amazon Elastic Kubernetes Service (AWS EKS), a managed Kubernetes solution. An open-source platform called Kubernetes makes it easier to scale, deploy, and maintain containerized apps. Existing apps that use upstream Kubernetes can be used with Amazon EKS as it is certified Kubernetes-conformant.

The Kubernetes control plane nodes that schedule containers, control application availability, store cluster data, and perform other crucial functions are automatically scaled and made available by Amazon EKS.

You may run your Kubernetes apps on AWS Fargate and Amazon Elastic Compute Cloud (Amazon EC2) using Amazon EKS. You can benefit from all of AWS infrastructure’s performance, scalability, dependability, and availability with Amazon EKS. It also integrates with AWS networking and security services, including AWS Virtual Private Cloud (VPC) support for pod networking, AWS Identity and Access Management (IAM) integration with role-based access control (RBAC), and application load balancers (ALBs) for load distribution.

Managed Kubernetes Clusters

Managed Control Plane

Across several AWS Availability Zones (AZs), AWS EKS offers a highly available and scalable Kubernetes control plane. The scalability and availability of Kubernetes API servers and the etcd persistence layer are automatically managed by Amazon EKS. To provide high availability, Amazon EKS distributes the Kubernetes control plane throughout three AZs. It also automatically identifies and swaps out sick control plane nodes.

Service Integrations

You may directly manage AWS services from within your Kubernetes environment with AWS Controllers for Kubernetes (ACK). Building scalable and highly available Kubernetes apps using AWS services is made easy with ACK.

Hosted Kubernetes Console

For Kubernetes clusters, EKS offers an integrated console. Kubernetes apps running on AWS EKS may be arranged, visualized, and troubleshooted in one location by cluster operators and application developers using EKS. All EKS clusters have automatic access to the EKS console, which is hosted by AWS.

EKS Add-Ons

Common operational software for expanding the operational capability of Kubernetes is EKS add-ons. The add-on software may be installed and updated via EKS. Choose whatever add-ons, such as Kubernetes tools for observability, networking, auto-scaling, and AWS service integrations, you want to run in an Amazon EKS cluster when you first launch it.

Managed Node Groups

With just one command, you can grow, terminate, update, and build nodes for your cluster using AWS EKS. To cut expenses, these nodes can also make use of Amazon EC2 Spot Instances. Updates and terminations smoothly deplete nodes to guarantee your apps stay accessible, while managed node groups operate Amazon EC2 instances utilizing the most recent EKS-optimized or customized Amazon Machine Images (AMIs) in your AWS account.

AWS EKS Connector

Any conformant Kubernetes cluster may be connected to AWS using AWS EKS, and it can be seen in the Amazon EKS dashboard. Any conformant Kubernetes cluster can be connected, including self-managed clusters on Amazon Elastic Compute Cloud (Amazon EC2), Amazon EKS Anywhere clusters operating on-premises, and other Kubernetes clusters operating outside of AWS. You can access all linked clusters and the Kubernetes resources running on them using the Amazon EKS console, regardless of where your cluster is located.

Read more on Govindhtech.com

[qcsdslabs]

Unlocking the Power of MCC: Simplifying Multi-Cloud Container Management

In the ever-evolving world of cloud technology, managing multi-cloud environments can feel like navigating an intricate maze. Organizations need solutions that not only simplify operations but also maximize efficiency, scalability, and control. Enter MCC (Mirantis Container Cloud)—a powerful platform designed to streamline containerized application management across diverse infrastructures.

What is MCC?

MCC is a comprehensive solution for managing Kubernetes clusters and containerized workloads across multiple cloud providers and on-premises environments. It is purpose-built for flexibility, ensuring that businesses can leverage their existing infrastructure investments while embracing the future of cloud-native applications.

Whether you’re running workloads on public cloud platforms, private clouds, or even bare metal, MCC provides a unified control plane to orchestrate and manage Kubernetes clusters with ease.

Key Features of MCC

Unified Management Across Multi-Cloud Environments: MCC eliminates the silos by offering centralized control over Kubernetes clusters, no matter where they’re deployed—AWS, Azure, Google Cloud, or on-premises.

Self-Service Automation: Developers and operators can create and manage Kubernetes clusters with a few clicks or API calls, speeding up deployment and reducing operational overhead.

Built-In Security and Compliance: With security policies and compliance tools integrated into the platform, MCC ensures that your workloads meet stringent regulatory and organizational requirements.

Scalability with Ease: MCC allows organizations to scale up or down based on demand, ensuring that resources are optimally utilized.

Cluster Lifecycle Management: From provisioning to decommissioning, MCC provides end-to-end lifecycle management for Kubernetes clusters, simplifying the complex processes of updates, backups, and monitoring.

Why Choose MCC?

Simplified Operations: By abstracting away the complexities of managing individual clusters and cloud environments, MCC allows IT teams to focus on innovation rather than infrastructure.

Cost Efficiency: With centralized management and resource optimization, organizations can significantly reduce operational costs and avoid cloud vendor lock-in.

Future-Ready Architecture: As businesses evolve, MCC scales and adapts, making it a long-term solution for organizations on their cloud-native journey.

Real-World Applications

DevOps Empowerment: MCC accelerates DevOps processes by providing developers with self-service cluster provisioning while ensuring that IT maintains control over configurations and security.

Hybrid Cloud Strategies: Organizations can seamlessly manage workloads across on-premises and cloud environments, ensuring high availability and resilience.

Global Deployments: MCC supports distributed architectures, making it an excellent choice for businesses with global operations and diverse infrastructure needs.

Conclusion

MCC is not just a container management tool—it’s a strategic enabler for organizations embracing digital transformation. By providing a unified, efficient, and scalable approach to Kubernetes cluster management, MCC empowers businesses to stay ahead in an increasingly complex and competitive landscape.

If you’re looking to simplify your cloud-native journey, MCC is the bridge between your current infrastructure and your future aspirations. Start exploring its potential today, and see how it transforms your multi-cloud strategy.

For more information visit: https://www.hawkstack.com/

[lowendbox]

Vultr Welcomes AMD Instinct MI300X Accelerators to Enhance Its Cloud Platform

The partnership between Vultr's flexible cloud infrastructure and AMD's cutting-edge silicon technology paves the way for groundbreaking GPU-accelerated workloads, extending from data centers to edge computing. “Innovation thrives in an open ecosystem,” stated J.J. Kardwell, CEO of Vultr. “The future of enterprise AI workloads lies in open environments that promote flexibility, scalability, and security. AMD accelerators provide our customers with unmatched cost-to-performance efficiency. The combination of high memory with low power consumption enhances sustainability initiatives and empowers our customers to drive innovation and growth through AI effectively.” With the AMD ROCm open-source software and Vultr's cloud platform, businesses can utilize a premier environment tailored for AI development and deployment. The open architecture of AMD combined with Vultr’s infrastructure grants companies access to a plethora of open-source, pre-trained models and frameworks, facilitating a seamless code integration experience and creating an optimized setting for speedy AI project advancements. “We take great pride in our strong partnership with Vultr, as their cloud platform is specifically designed to handle high-performance AI training and inferencing tasks while enhancing overall efficiency,” stated Negin Oliver, corporate vice president of business development for the Data Center GPU Business Unit at AMD. “By implementing AMD Instinct MI300X accelerators and ROCm open software for these latest deployments, Vultr customers will experience a truly optimized system capable of managing a diverse array of AI-intensive workloads.” Tailored for next-generation workloads, the AMD architecture on Vultr's infrastructure enables genuine cloud-native orchestration of all AI resources. The integration of AMD Instinct accelerators and ROCm software management tools with the Vultr Kubernetes Engine for Cloud GPU allows the creation of GPU-accelerated Kubernetes clusters capable of powering the most resource-demanding workloads globally. Such platform capabilities empower developers and innovators with the tools necessary to create advanced AI and machine learning solutions to address complex business challenges. Additional advantages of this collaboration include: Vultr is dedicated to simplifying high-performance cloud computing so that it is user-friendly, cost-effective, and readily accessible for businesses and developers worldwide. Having served over 1.5 million customers across 185 nations, Vultr offers flexible, scalable global solutions including Cloud Compute, Cloud GPU, Bare Metal, and Cloud Storage. Established by David Aninowsky and fully bootstrapped, Vultr has emerged as the largest privately-held cloud computing enterprise globally without ever securing equity financing.   LowEndBox is a go-to resource for those seeking budget-friendly hosting solutions. This editorial focuses on syndicated news articles, delivering timely information and insights about web hosting, technology, and internet services that cater specifically to the LowEndBox community. With a wide range of topics covered, it serves as a comprehensive source of up-to-date content, helping users stay informed about the rapidly changing landscape of affordable hosting solutions. Read the full article

[hawkstack]

🚀 Why You Should Choose "Enterprise Kubernetes Storage with Red Hat OpenShift Data Foundation (DO370)" for Your Next Career Move

In today’s cloud-native world, Kubernetes is the gold standard for container orchestration. But when it comes to managing persistent storage for stateful applications, things get complex — fast. This is where Red Hat OpenShift Data Foundation (ODF) comes in, providing a unified and enterprise-ready solution to handle storage seamlessly in Kubernetes environments.

If you’re looking to sharpen your Kubernetes expertise and step into the future of cloud-native storage, the DO370 course – Enterprise Kubernetes Storage with Red Hat OpenShift Data Foundation is your gateway.

🎯 Why Take the DO370 Course?

Here’s what makes DO370 not just another certification, but a career-defining move:

1. Master Stateful Workloads in OpenShift

Stateless applications are easy to deploy, but real-world applications often need persistent storage — think databases, logging systems, and message queues. DO370 teaches you how to:

Deploy and manage OpenShift Data Foundation.

Use block, file, and object storage in a cloud-native way.

Handle backup, disaster recovery, and replication with confidence.

2. Hands-On Experience with Real-World Use Cases

This is a lab-heavy course. You won’t just learn theory — you'll work with scenarios like deploying storage for Jenkins, MongoDB, PostgreSQL, and more. You'll also learn how to scale and monitor ODF clusters for production-ready deployments.

3. Leverage the Power of Ceph and NooBaa

Red Hat OpenShift Data Foundation is built on Ceph and NooBaa. Understanding these technologies means you’re not only skilled in OpenShift storage but also in some of the most sought-after open-source storage technologies in the market.

💡 Career Growth and Opportunities

🔧 DevOps & SRE Engineers

This course bridges the gap between developers and infrastructure teams. As storage becomes software-defined and container-native, DevOps professionals need this skill set to stay ahead.

🧱 Kubernetes & Platform Engineers

Managing platform-level storage at scale is a high-value skill. DO370 gives you the confidence to run stateful applications in production-grade Kubernetes.

☁️ Cloud Architects

If you're designing hybrid or multi-cloud strategies, you’ll learn how ODF integrates across platforms — from bare metal to AWS, Azure, and beyond.

💼 Career Advancement

Red Hat certifications are globally recognized. Completing DO370:

Enhances your Red Hat Certified Architect (RHCA) portfolio.

Adds a high-impact specialization to your résumé.

Boosts your value in organizations adopting OpenShift at scale.

🚀 Future-Proof Your Skills

Organizations are moving fast to adopt cloud-native infrastructure. And with OpenShift being the enterprise Kubernetes leader, having deep knowledge in managing enterprise storage in OpenShift is a game-changer.

As applications evolve, storage will always be a critical component — and skilled professionals will always be in demand.

📘 Final Thoughts

If you're serious about growing your Kubernetes career — especially in enterprise environments — DO370 is a must-have course. It's not just about passing an exam. It's about:

✅ Becoming a cloud-native storage expert ✅ Understanding production-grade OpenShift environments ✅ Standing out in a competitive DevOps/Kubernetes job market

👉 Ready to dive in? Explore DO370 and take your skills — and your career — to the next level.

For more details www.hawkstack.com

[qcs01]

Managing OpenShift Clusters: Best Practices and Tools

Introduction

Brief overview of OpenShift and its significance in the Kubernetes ecosystem.

Importance of effective cluster management for stability, performance, and security.

1. Setting Up Your OpenShift Cluster

Cluster Provisioning

Steps for setting up an OpenShift cluster on different platforms (bare metal, cloud providers like AWS, Azure, GCP).

Using OpenShift Installer for automated setups.

Configuration Management

Initial configuration settings.

Best practices for cluster configuration.

2. Monitoring and Logging

Monitoring Tools

Using Prometheus and Grafana for monitoring cluster health and performance.

Overview of OpenShift Monitoring Stack.

Logging Solutions

Setting up EFK (Elasticsearch, Fluentd, Kibana) stack.

Best practices for log management and analysis.

3. Scaling and Performance Optimization

Auto-scaling

Horizontal Pod Autoscaler (HPA).

Cluster Autoscaler.

Resource Management

Managing resource quotas and limits.

Best practices for resource allocation and utilization.

Performance Tuning

Tips for optimizing cluster and application performance.

Common performance issues and how to address them.

4. Security Management

Implementing Security Policies

Role-Based Access Control (RBAC).

Network policies for isolating workloads.

Managing Secrets and Configurations

Securely managing sensitive information using OpenShift secrets.

Best practices for configuration management.

Compliance and Auditing

Tools for compliance monitoring.

Setting up audit logs.

5. Backup and Disaster Recovery

Backup Strategies

Tools for backing up OpenShift clusters (e.g., Velero).

Scheduling regular backups and verifying backup integrity.

Disaster Recovery Plans

Creating a disaster recovery plan.

Testing and validating recovery procedures.

6. Day-to-Day Cluster Operations

Routine Maintenance Tasks

Regular updates and patch management.

Node management and health checks.

Troubleshooting Common Issues

Identifying and resolving common cluster issues.

Using OpenShift diagnostics tools.

7. Advanced Management Techniques

Custom Resource Definitions (CRDs)

Creating and managing CRDs for extending cluster functionality.

Operator Framework

Using Kubernetes Operators to automate complex application deployment and management.

Cluster Federation

Managing multiple OpenShift clusters using Red Hat Advanced Cluster Management (ACM).

Conclusion

Recap of key points.

Encouragement to follow best practices and continuously update skills.

Additional resources for further learning (official documentation, community forums, training programs).

By covering these aspects in your blog post, you'll provide a comprehensive guide to managing OpenShift clusters, helping your readers ensure their clusters are efficient, secure, and reliable.

For more details click www.qcsdclabs.com

[ericvanderburg]

ClearML Unveils AI Orchestration and Management Capabilities, Now Supporting Kubernetes, Slurm, PBS, and Bare Metal

http://securitytc.com/T7235L