Today, DevOps and Infrastructure teams often reduce EKS Worker Node costs using EKS Cluster Autoscaler. However, scheduling shutdowns to save costs for Non-Production Environments during holidays doesn’t scale the nodes down to zero due to active EKS System Pods. The xPlatform team from SCB TechX has an effective solution. Join us as Khun Few poottipong rueangsuntikornkul, Associate Infrastructure Engineer, shares how Karpenter, a tool that helps with customizable scaling and optimal resource utilization, can reduce costs effectively. Key Features of Karpenter: 1. Watching: Manage and control Pods created by the Kubernetes Scheduler. 2. Evaluating: Assess Pods' constraints such as Resource Requests, Node Selectors, Affinities, Tolerations, and Topology Spread Constraints. 3. Provisioning: Create Nodes from instance types evaluated based on Pods' requirements. 4. Removing: Delete unused Nodes by analyzing non-EKS System Pods. Using Karpenter significantly reduces costs and enhances the management of EKS Worker Nodes, especially during idle periods. Our team has tested and found it effective, That’s why we're sharing this Tech Stack to help you optimize your operations. Next time, we'll share more insightful DevOps use cases. Stay tuned to our page! 📌 If your organization is looking for DevOps solutions to automate workflows and reduce business costs, SCB TechX is here to help you develop and deliver products and services to market, ensuring sustainable growth. Contact us at 👉🏼 contact@scbtechx.io Learn more 👉🏼https://bit.ly/4c2GdZI #SCBTechX #xPlatform #DevOpsaaS #DevOpsSolutions #DevOpsCulture #SDLC
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DevSecOps | CloudArchitect | FullStackDev | CyberSecurity R&D Analyst | DS•AI•ML | SRE | MotivatingMinds | MentoringPupils | MCA, CEH
🌐 Optimizing Performance and Resource Allocation in Kubernetes: Unleashing the Power of Resource Limits! 🚀 As organizations embrace Kubernetes for container orchestration, ensuring efficient resource allocation and performance optimization becomes crucial. Kubernetes allows you to define and manage the allocation of computing resources for your application workloads, allowing for better scalability, stability, and reliability. Now, let's delve into setting resources and limits in Kubernetes deployments and StatefulSets: 1️⃣ Resource Requests: When defining resources for a Kubernetes deployment or StatefulSet, specifying resource requests helps the cluster scheduler allocate the necessary resources for the pods to run. Resource requests define the minimum amount of CPU and memory required by a pod, ensuring that the cluster provides adequate resources for the workload. Accurately setting resource requests allows Kubernetes to make informed scheduling decisions, preventing resource contention and ensuring stable performance. 2️⃣ Resource Limits: While resource requests define the minimum required resources, setting resource limits establishes an upper boundary on the amount of CPU and memory a pod can consume. Resource limits prevent a pod from consuming excessive resources and causing performance degradation or impacting other workloads. By setting appropriate resource limits, you safeguard your applications and maintain stability, ensuring that each pod operates within predefined boundaries. 3️⃣ Quality of Service (QoS): Kubernetes categorizes pods into three Quality of Service classes based on their resource requirements and limits: Guaranteed, Burstable, and BestEffort. The QoS class determines the level of priority and eviction policies applied to the pods. By properly configuring resource requests and limits, you can ensure that your pods fall into the appropriate QoS class, optimizing resource utilization and providing a stable environment for your workloads. 4️⃣ Monitoring and Tuning: After setting initial resource requests and limits, it's crucial to monitor your applications' resource utilization and performance. Kubernetes provides robust monitoring and logging capabilities, allowing you to analyze resource usage patterns and identify potential bottlenecks or areas for optimization. By regularly reviewing and fine-tuning your resource allocation based on monitoring data, you can continuously optimize performance and ensure efficient resource utilization. Properly configuring resources and limits in Kubernetes deployments and StatefulSets empowers you to achieve optimal performance, stability, and scalability for your applications. By leveraging these capabilities, you can ensure that your workloads receive the necessary resources while avoiding resource contention, maximizing efficiency, and maintaining a healthy cluster environment.💡 #Kubernetes #Containerization #ResourceAllocation #DevOps #CloudNative #Technology #Innovation
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Embark on a journey of containerized excellence with our advanced Kubernetes 2-node cluster setup, designed for peak performance and scalability. Leveraging the latest in cloud-native technology, our solution ensures optimal orchestration, resource utilization, and fault tolerance. Running your nodes in virtual machines? No problem! Our setup seamlessly integrates with VM environments, providing a flexible and efficient foundation for your Kubernetes workloads. Explore the realm of automated scaling, efficient load balancing, and simplified management. Elevate your DevOps game with the future of container orchestration! #Kubernetes #ClusterSetup #CloudNative #DevOps #Virtualization
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Embracing Modernity with Microservices and Cutting-Edge Technologies 🚀 Continuing from our last discussion on traditional architectures, let’s explore the transformative shift towards microservices architecture and the key technologies that support it. Microservices Architecture: Definition: Consists of small, autonomous services that handle specific business functions. Advantages: Scalability: Each service can be scaled independently, allowing for efficient resource use. Flexibility: Enables quick updates and reduces the risk of widespread system disruptions. Technology Diversity: Teams can use different technologies best suited for their specific services. Enabling Technologies: Kafka: Essential for robust, scalable communication between microservices. GitLab: Enhances continuous integration and deployment (CI/CD), critical for managing multiple services. Datadog: Provides comprehensive monitoring across services to maintain optimal performance. Docker: Offers a standardized environment for services, facilitating consistent deployments. Kubernetes: Manages and scales containerized applications, ensuring reliability and efficiency. Prometheus: Specialized in monitoring microservices, providing valuable operational insights. These technologies are pivotal in managing the complexities of microservices architectures, offering tools that promote scalability, reliability, and operational efficiency. #Microservices #DevOps #CloudComputing #SoftwareEngineering
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The Role of Worker Nodes in Kubernetes Cluster Operations Let’s go over the role of worker nodes, their components, responsibilities, and significance in executing containerized workloads within a Kubernetes cluster. 1️⃣ Understanding Worker Nodes: Worker nodes are responsible for executing containerized workloads and running applications. Each worker node is a virtual or physical machine that contributes computing resources to the cluster. 2️⃣ Key Components of Worker Nodes: Worker nodes consist of several key components, including the kubelet, container runtime, and kube-proxy. These components work together to manage containers, communicate with the control plane, and provide networking services within the cluster. 3️⃣ The Kubelet: The kubelet is responsible for managing containers and ensuring that pods (groups of containers) are running and healthy. It interacts with the control plane to receive pod specifications and execute container operations. 4️⃣ Container Runtime: The container runtime, such as Docker or containerd, is responsible for running containers on worker nodes. It manages container lifecycle operations, including image management, container creation, and resource isolation. 5️⃣ Kube-proxy: Kube-proxy facilitates communication between pods and external clients. It manages network rules and load balancing, enabling pod-to-pod and pod-to-service communication within the cluster. 6️⃣ Executing Workloads: Worker nodes execute containerized workloads by running pods, which encapsulate one or more containers. Each pod is scheduled onto a worker node by the Kubernetes scheduler based on resource availability and affinity rules, ensuring optimal resource utilization. 7️⃣ Scaling and Resilience: Worker nodes provide scalability and resilience to Kubernetes clusters. They can be dynamically scaled up or down to accommodate changing workload demands, while features like pod rescheduling and node auto-recovery enhance cluster reliability. 8️⃣ Conclusion: Worker nodes are essential components of a Kubernetes cluster, powering the execution of containerized workloads and enabling efficient resource utilization. By hosting the kubelet, container runtime, and kube-proxy, worker nodes play a crucial role in ensuring the reliability, scalability, and performance of Kubernetes deployments. #Kubernetes #WorkerNodes #Orchestrators #Containers #ApplicationManagement #Microservices #DevOps #CloudNative
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Ethical Hacker 👨🏻💻 | DevOps & Cloud Enthusiast ☁️ | Linux 🐧 | AWS ☁️ | Docker 🐳 | Kubernetes ☸️ | EKS | GitHub | Jenkins | Ansible | Terraform
🔵 Exciting News! 🌐 I'm delighted to share some valuable insights about Kubernetes Nodes, the backbone of a robust and scalable containerized infrastructure! 🚀 In the world of container orchestration, Kubernetes Nodes play a pivotal role in executing workloads and running containers. Each node is a virtual or physical machine responsible for hosting one or more containers, forming the building blocks of a Kubernetes cluster. 💪 Nodes are composed of two essential components: the kubelet and the container runtime. The kubelet is an agent that communicates with the Kubernetes control plane, ensuring containers are running and healthy on the node. The container runtime, like Docker or containerd, is responsible for pulling, running, and managing containers. ⚙️ During cluster operation, nodes collaborate to achieve high availability and fault tolerance. If a node becomes unavailable, Kubernetes orchestrates the rescheduling of affected containers to healthy nodes, maintaining application availability and performance. This dynamic nature ensures a resilient and self-healing environment. 🔒 Kubernetes nodes are versatile, supporting various workloads and applications. From microservices to stateful applications, nodes efficiently accommodate diverse scenarios. Administrators can assign specific resource quotas and constraints to nodes, optimizing resource utilization and guaranteeing fair share among applications. 🛡️ Node management is a crucial aspect of cluster administration. Operators can add or remove nodes as needed, scaling the infrastructure dynamically. This capability empowers businesses to meet changing demands, whether during traffic spikes or resource-intensive tasks. Additionally, taints and tolerations can be applied to nodes, allowing selective placement of workloads based on specific requirements. 📜 In conclusion, Kubernetes Nodes are the backbone of a resilient and scalable containerized infrastructure. Their role in executing workloads, managing containers, and collaborating within the cluster is indispensable. By efficiently managing nodes, businesses can achieve optimal resource utilization, deliver high availability, and ensure smooth scalability of applications. Embrace the power of Kubernetes Nodes and unlock new possibilities for your containerized environments! 💡💼 Ashish Agrawal #Kubernetes #KubernetesNodes #ContainerOrchestration #HighAvailability #Scalability #ResourceManagement #CloudInfrastructure
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The #1 reason the engineering teams I’m talking to say they need a better alerting and incident management tool: Their technology architectures are becoming more complex. Microservice architectures, more integrations and complex ecosystems, globally distributed environments, etc. More incidents (not just Sev 0 or Sev 1's but also lower severity incidents) are becoming a major challenge to manage efficiently at scale. FireHydrant #devops #microservices #incidentmanagement #sre
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Driving Improved Efficiency and Scalability in Continuous Delivery of Software Applications | DevOps Lead Consultant | Kubernetes | Ansible | Jenkins | GIT | AWS
𝐃𝐢𝐝 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝟕𝟎% 𝐨𝐟 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐮𝐬𝐞𝐫𝐬 𝐫𝐞𝐩𝐨𝐫𝐭 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐰𝐢𝐭𝐡𝐢𝐧 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐲𝐞𝐚𝐫 𝐨𝐟 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭? Efficiently scaling Kubernetes presents multiple challenges, including performance maintenance, effective resource management, ensuring security, and reducing operational complexity. Here are some practical solutions and key considerations to streamline the scaling process: 𝟏. **𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲** Utilize Kubernetes’ Horizontal Pod Autoscaler (HPA) 📊 which automatically adjusts the number of pod replicas based on observed CPU utilization or other selected metrics. 𝟐.. **𝐒𝐭𝐚𝐭𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭** Stateful applications can complicate Kubernetes scaling because they require persistent storage and complex management 🛠️. 𝟑. **𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐮𝐧𝐢𝐧𝐠** Utilizing tools like Prometheus for monitoring and Grafana for visualization 📊 can provide insights into how well the deployments are scaling and help pinpoint bottlenecks. 𝟒. **𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲𝐢𝐧𝐠 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧𝐬** Use Kubernetes operators and custom resources to create higher-level abstractions that simplify complex configurations required for scaling large environments 🌐. 𝟓. **𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐂𝐈/𝐂𝐃 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬** Ensure that continuous integration and delivery pipelines are scalable and robust 🔄. This automation is key in managing multiple deployments and can significantly reduce the complexity and risk of human error in scaled environments 🚧. Whether you’ve successfully mitigated such challenges or currently navigating through them, your contributions can provide immense value to others facing similar situations. Let’s discuss this further in the comments below! Follow Mohammed Shabaz for more such valuable content. #Kubernetes #CloudComputing #DevOps
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Rui G. has just published a must-read piece on Medium: "Managing Resource Request Failures in Kubernetes" (https://lnkd.in/dSACPnVk). Dive into the complexities of resource management within Kubernetes environments, the crucial role of quotas, and the intricacies of capacity planning in large-scale operations. #Marionete #Kubernetes #ResourceManagement #DevOps #HighAvailability
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Analyst DevOps Senior | Thomson Reuters | Mentor DevOps | AWS | Azure | SRE | Developer | MBA | Inventor Patents IBM | Autistic | Giftedness | Speaker Global | Autistic in the Technology DevOps Senior
Think About: We are Autistic, we can do more than you think. Today we can talk about technology. Kubernetes Kubernetes, also known as k8s, is a robust and extensible open-source platform for automating deployment, scaling and operations of containerized applications. It groups containers that make up an application into logical units for easy management and service discovery. Technically, Kubernetes introduces an abstraction over infrastructure hardware, allowing containers to be deployed across a cluster of servers. It uses a set of APIs to control how and where these containers will run, relying on declarative configurations to automate the desired state of the applications. Continuous integration and continuous delivery (CI/CD) in Kubernetes are facilitated through automated pipelines that can build, test, and deploy applications directly to production or staging environments, promoting an agile DevOps culture. In terms of scalability, Kubernetes allows automatic adjustments to the amount of computing resources needed, through the Horizontal Pod Autoscale, which adjusts the number of pods in a Deployment, Replication Controller, Replica Set or Stateful Set, based on CPU utilization or other metrics selected. High availability is ensured by planning control and fault tolerance. Kubernetes constantly monitors nodes and containers, and in case of failures, it can automatically reschedule and launch new container instances on healthy cluster nodes. For hybrid or multi-cloud operations, Kubernetes is agnostic to the underlying infrastructure. It can run in any environment, whether in a public or private cloud or even in an on-premises data centre, which allows companies to avoid being locked into a single cloud provider (vendor lock-in). Additionally, features such as Namespaces and Network Policies offer isolation and management of traffic between services, while Persistent Volumes and Stateful Sets allow stateful applications, such as databases, to be managed effectively. By implementing Kubernetes, organizations gain flexibility, operational efficiency, and a significant improvement in software delivery speed, all while maintaining governance and compliance with IT security policies. #Kubernetes #ContainerOrchestration #DevOps #CICD #CloudComputing #Microservices #TechInnovation #ITAutomation #CloudAgnostic #CostEfficiency #HighAvailability #TechLeadership #InfrastructureAsCode #Scalability
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Microservices: Building Scalable, Resilient Systems Microservices offer scalable and resilient systems by: Scalability: Each service scales independently to meet demand, ensuring efficient resource usage. Resilience: Failures are isolated within services, minimizing impact on the overall system. Fault Isolation: Issues in one service don't affect others, maintaining system functionality. Flexibility: Teams can choose the best tools for each service, fostering innovation. Continuous Deployment: Services can be deployed independently, enabling rapid updates and time-to-market. #systemdesign #devops #architecture #microservices
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