Why Use Kubernetes for Digital Service Deployment

I work with a lot of folks in the OpenStack world and discussions come up a lot in regards to how - seemingly overnight - Kubernetes dominated the private cloud industry. It think it's useful to reflect on what kubernetes did right which led to mass adoption so quickly. Here's a list of a few major things in my opinion which got people (like me) so excited: 1. The API is not static - it's flexible and extendible. Kubernetes has the concept of custom resources to extend the logical control plane layer of the system. 2. The Operator pattern - Kubernetes provides a platform as an SDK for developers. Its a platform for developers to develop on. It abstracts and consolidates infrastructure. 3. Web style stateless deployment/replica pattern - HA is core to the design and being able to orchestrate and scale across many instances with near-zero config overhead dramatically simplifies HA. 4. Auto-reconciliation and Self-Healing - These patterns change the paradigm as to where the involvement of the system exists in the concept of failure recovery. The system now has the job of attempting to recover based on health metric based decisions. 5. Eventual consistency - Building on the reconciliation this obsoletes the concept of strict dependency management of services and offloads the management of restarting dependencies until other dependent services are online first. Which means this no longer is managed by the operator. 6. Declarative Design - Also building on the proper reconciliation-loop pattern of the previous points. Kubernetes is interfaced with using a declarative pattern and hands off the cognitive load of the "how" to be managed by the control plane. 7. immutable infrastructure - Being built on top of Docker allowed the declared infrastructure to be declared once and rouge services cannot directly mutate the configuration of the running system. All operators have the north star of converging to declaration. Obsoleting the concept of "configuration drift". 8. Labeling and selecting - instead of dealing with complex under-laying networking. Kubernetes abstracts the management of services and resources with a declarative friendly design that wires up underlying services under the hood. 9. Infrastructure agnostic design - Kubernetes was a clean abstraction that allowed for a similar interface in both cloud and on-prem. Extending "private cloud" into "hybrid cloud". This means we were all free to use whatever infrastructure provider we want. 10. Cloud Native - building on all the previous point. This is why people liked to use the cloud in the first place. This was not aimed to follow existing patterns for infrastructure IT operations teams. Rather aimed at bringing the cloud to on-prem. Platform Engineering and Internal Developer Platforms are a large coming wave in the infrastructure engineering ecosystem. As we start developing platforms on top of kubernetes it's extremely helpful to keep in mind what made Kubernetes such a success from the start.

This reference architecture describes the benefits of exposing applications externally through Google Kubernetes Engine (GKE) Gateways running on multiple GKE clusters within a service mesh. This guide is intended for platform administrators. You can increase the resiliency and redundancy of your services by deploying applications consistently across multiple GKE clusters, where each cluster becomes an additional failure domain. For example, a service with a service level objective (SLO) of 99.9% when deployed in a single GKE cluster achieves an SLO of 99.9999% when deployed across two GKE clusters (1 - (0.001)2). You can also provide users with an experience where incoming requests are automatically directed to the least latent and available mesh ingress gateway.

Kubernetes: More Than Just Apps Kubernetes and containers have truly transformed the application world, and it's all about the agility and power they offer compared to traditional Virtual Machines (VMs). For app deployments, it's pretty clear: containers are lightweight, fire up in a flash, and give us that "build once, run anywhere" consistency we all crave. No more "it worked on my machine!" headaches. Kubernetes then takes that to the next level with automated scaling and self-healing, letting our apps handle traffic spikes and bounce back from issues on their own. This means we can iterate faster and get new features out to users way quicker. Now, I know some folks might raise an eyebrow when we talk about using Kubernetes for heavy-duty networking and network security functions – think VPN concentrators, or proxies securing connections between external clients and outside services. There's often a thought that Kubernetes might introduce too much overhead, especially with how its networking (CNI) works within the host kernel. But here's the cool part: in our team, we've actually seen Kubernetes shine just as brightly for these networking workloads! We're seeing the same benefits: incredible scale-out capabilities (just add more nodes for a performance boost!), and the rock-solid resilience that Kubernetes inherently provides. And those concerns about virtual switching overhead from CNI? Modern CNI plugins like Calico and Cilium are leveraging eBPF in the Linux kernel to address this head-on. They're optimizing packet processing at a super low level, cutting down on latency and making Kubernetes a truly viable, high-performance platform even for critical network and security functions. It's been a journey, but seeing these capabilities proven out by our team has been incredibly validating. If you're still on the fence about K8s for your networking stack, I'd definitely encourage you to dive deeper! #Aryaka #SASE #AIFirewall #SSE #Kubernetes

𝗛𝗼𝘄 𝗪𝗲 𝗦𝗼𝗹𝘃𝗲𝗱 𝗢𝘂𝗿 𝗕𝗶𝗴𝗴𝗲𝘀𝘁 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗮𝗻𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗱 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 A couple of years ago, we decided to migrate one of our large-scale applications to 𝗮 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗲𝗱 and 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. Initially, it felt like a big shift, 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗶𝗻𝗴 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀, 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀, and 𝗿𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 . But the payoff was remarkable. Kubernetes gave us the power to 𝘀𝗰𝗮𝗹𝗲 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝗮𝗹𝗹𝘆 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗿𝗲𝗮𝗹 𝗱𝗲𝗺𝗮𝗻𝗱. automatically scaling up during peak hours to handle traffic and scaling down during off-hours to optimize cost and performance. We also achieved 𝗯𝗲𝘁𝘁𝗲𝗿 𝗳𝗮𝘂𝗹𝘁 𝘁𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲, if a node went down, workloads were seamlessly redistributed without impacting the user experience. The real power of this model lies in its 𝘀𝗲𝗹𝗳-𝗵𝗲𝗮𝗹𝗶𝗻𝗴 and 𝗱𝗲𝘀𝗶𝗿𝗲𝗱-𝘀𝘁𝗮𝘁𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵. You define:  • 𝗛𝗼𝘄 𝗺𝗮𝗻𝘆 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘀 to run  • 𝗪𝗵𝗶𝗰𝗵 𝗶𝗺𝗮𝗴𝗲𝘀 to use  • 𝗪𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝘄𝗵𝗲𝗻 𝗮 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 occurs And the system ensures that your environment matches that desired state. It abstracts away infrastructure complexity, letting teams focus on development 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲. It also fits beautifully into a modern 𝗖𝗜/𝗖𝗗 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲.  • Integrated with 𝗝𝗲𝗻𝗸𝗶𝗻𝘀 and 𝗚𝗶𝘁𝗟𝗮𝗯 𝗖𝗜, every code change triggered a 𝗗𝗼𝗰𝗸𝗲𝗿 𝗶𝗺𝗮𝗴𝗲 𝗯𝘂𝗶𝗹𝗱.  • The image was pushed to our 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝘆.  • Updates were 𝗿𝗼𝗹𝗹𝗲𝗱 𝗼𝘂𝘁 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀𝗹𝘆 𝘄𝗶𝘁𝗵 𝘇𝗲𝗿𝗼 𝗱𝗼𝘄𝗻𝘁𝗶𝗺𝗲. This level of automation drastically reduced 𝗺𝗮𝗻𝘂𝗮𝗹 𝗲𝗳𝗳𝗼𝗿𝘁 and 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗿𝗶𝘀𝗸. Docker became our foundation for 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻, while the 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 layer handled where and how those containers ran. Combined with tools like 𝗣𝗿𝗼𝗺𝗲𝘁𝗵𝗲𝘂𝘀 and 𝗚𝗿𝗮𝗳𝗮𝗻𝗮, we achieved full 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻𝘁𝗼 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 𝗵𝗲𝗮𝗹𝘁𝗵, 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘂𝘀𝗮𝗴𝗲, and 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘁𝗿𝗲𝗻𝗱𝘀, helping us tune and scale proactively. More than just scaling, this shift brought 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 and 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 across environments- 𝗱𝗲𝘃, 𝘀𝘁𝗮𝗴𝗶𝗻𝗴, or 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻, all behaved predictably, reducing release anxiety and making deployments smoother. Looking back, this wasn’t just a technical change, it was a 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘀𝗵𝗶𝗳𝘁 towards 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, and 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆. For anyone working on 𝗳𝗮𝘀𝘁 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 or 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀, investing time in building a 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗺𝗼𝗱𝗲𝗹 is absolutely worth it. #React #Angular #JavaScript #Programming #Java #SpringBoot #Nodejs #FullStackDeveloper #c2c

Even 𝗡𝗩𝗜𝗗𝗜𝗔 𝗡𝗜𝗠 𝗶𝘀 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗼𝗻 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, what is 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 and why is it worth learning 𝗮𝘀 𝗠𝗟𝗢𝗽𝘀/𝗠𝗟/𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿? Today we look into the Kubernetes system from a bird's eye view. 𝗦𝗼, 𝘄𝗵𝗮𝘁 𝗶𝘀 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 (𝗞𝟴𝘀)? 𝟭: It is a container orchestrator that performs the scheduling, running and recovery of your containerised applications in a horizontally scalable and self-healing way. Kubernetes architecture consists of two main logical groups: 𝟮: Control plane - this is where K8s system processes that are responsible for scheduling workloads defined by you and keeping the system healthy live. 𝟯: Worker nodes - this is where containers are scheduled and run. 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂? 𝟰: You can have thousands of Nodes (usually you only need tens of them) in your K8s cluster, each of them can host multiple containers. Nodes can be added or removed from the cluster as needed. This enables unrivaled horizontal scalability. 𝟱: Kubernetes provides an easy to use and understand declarative interface to deploy applications. Your application deployment definition can be described in yaml, submitted to the cluster and the system will take care that the desired state of the application is always up to date. 𝟲: Users are empowered to create and own their application architecture in boundaries pre-defined by Cluster Administrators. ✅ In most cases you can deploy multiple types of ML Applications into a single cluster, you don’t need to care about which server to deploy to - K8s will take care of it. ✅ You can request different amounts of dedicated machine resources per application. ✅ If your application goes down - K8s will make sure that a desired number of replicas is always alive. ✅ You can roll out new versions of the running application using multiple strategies - K8s will safely do it for you. ✅ You can expose your ML Services for other Product Apps to use with few intuitive resource definitions. ✅ … ❗️Having said this, while it is a bliss to use, usually the operation of Kubernetes clusters is what is feared. It is a complex system. ❗️Master Plane is an overhead, you need it even if you want to deploy a single small application. Are you deploying your ML applications in Kubernetes? What are the main pain points you are facing? Let me know in the comments 👇 #MachineLearning #GenAI #LLM #LLMOps

What is Kubernetes? Kubernetes is an open-source container orchestration platform originally developed by Google. Think of it as the "operating system" for managing containerized applications across multiple servers. What it does: Automates deployment of containerized applications Scales applications up or down based on demand Manages container health and restarts failed containers Load balances traffic between containers Handles storage, networking, and secrets management Simple analogy: If Docker is like a shipping container for your application, Kubernetes is like the entire shipping port - managing where containers go, ensuring they're running, replacing broken ones, and directing traffic to the right places. Why use it? High availability: If a server fails, K8s moves your apps to healthy servers Scalability: Automatically scale from 1 to 1000s of instances Self-healing: Restarts crashed containers automatically Efficient resource use: Packs containers efficiently across servers

A Visual Overview of Kubernetes Containers revolutionized modern application development and deployment. Unlike bulky virtual machines, containers package up just the application code and dependencies, making them lightweight and portable. However, running containers at scale brings challenges. Enter Kubernetes! Kubernetes helps deploy, scale, and manage containerized applications across clusters of machines. 𝗖𝗼𝗿𝗲 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 Control Plane: The brains behind cluster management, handling scheduling, maintaining desired state, rolling updates etc. Runs on multiple machines for high availability. Worker Nodes: The machines that run the containerized applications. Each node has components like kubelet and kube-proxy alongside the application containers. The smallest deployable units in Kubernetes are Pods. A Pod encapsulates one or more tightly coupled containers that comprise an application. Kubernetes assigns Pods to worker nodes through its API server. 𝗞𝗲𝘆 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 - Scalability: It's easy to scale applications up and down on demand. Just specify the desired instance count, Kubernetes handles the rest! - Portability: Applications can run anywhere - on premise, cloud, hybrid environments etc. No vendor lock-in! - Resiliency: Kubernetes restarts failed containers, replaces unhealthy nodes, and maintains desired state, reducing downtime. - Automation: Manual tasks like rolling updates, rollbacks are automated, freeing teams to focus on development. 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀 The power of Kubernetes comes with complexity. Installing, configuring, and operating Kubernetes has a steep learning curve. For many teams, it's overkill. Managed Kubernetes services help by handling control plane management, letting teams focus only on applications and pay for just the worker resources used. 𝗜𝘀 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗮 𝗚𝗼𝗼𝗱 𝗙𝗶𝘁? Consider: - Are you running containers already at meaningful scale? - Will portability or resiliency resolve production issues? - Is your team willing to invest in learning and operating Kubernetes? If you answered yes, Kubernetes may suit your needs. Otherwise, containers without orchestration may still get the job done. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/496keA7

You dockerized your .NET Web apps. Great, but next you'll face these: - How to manage the lifecycle of your containers? - How to scale them? - How to make sure they are always available? - How to manage the networking between them? - How to make them available to the outside world? To deal with those, you need 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, the container orchestration platform designed to manage your containers in the cloud. I started using Kubernetes about 6 years ago when I joined the ACR team at Microsoft, and never looked back. It's the one thing that put me ahead of my peers given the increasing move to Docker containers and cloud native development. Every single team I joined since then used Azure Kubernetes Service (AKS) because of the impressive things you can do with it like: - Quickly scale your app up and down as needed - Ensure your app is always available - Automatically distribute traffic between containers - Roll out updates and changes fast and with zero downtime - Ensure the resources on all boxes are used efficiently How to get started? Check out my step-by-step AKS guide for .NET developers here 👇 https://lnkd.in/gBPJT6wv Keep learning!

Tech 101 for PMs: Understanding Kubernetes 🌐 Kubernetes is an open-source platform designed to help engineering teams manage their app deployment and maintenance in an efficient and streamlined manner. It is like a super-efficient manager for your computer programs. It helps keep these programs running smoothly and efficiently, especially when you have a lot of them. Example: Imagine you have a big party (think of Big fat Indian wedding) and you need to make sure everyone has the right food and drinks (so that nobody complaints about it later on). Kubernetes is like a really smart waiter who knows exactly where everything is, who wants what and when, and can bring it to the right people at the right time. Kubernetes serves multiple use cases for engineering team. Below are a few use cases with analogies to understand better. [1] Manage high traffic When an application gets more users, Kubernetes can automatically add more servers to handle the load. This ensures the app runs smoothly, even during high traffic times, without needing manual intervention. ➡ Imagine running a restaurant. When more customers come in, you need more waiters to serve them. Kubernetes acts like a manager who automatically calls in more staff when the restaurant is full and sends them home when the demand is less. [2] Minimize downtime If one server (or computer) running part of your application crashes, Kubernetes automatically shifts that part to another working server, so the app keeps running without downtime. ➡ Talking about restaurant example again: If one chef in your restaurant gets sick, the manager immediately calls another chef to fill in, ensuring that the restaurant keeps serving customers without delay. [3] Cost savings Kubernetes optimizes resource usage by automatically scaling applications up or down based on the actual demand. This reduces wasted resources and saves costs, as you only use what you need. ➡ In the restaurant, the manager only brings in the exact number of staff needed, no more, no less. If there are too many waiters, the manager sends some back to avoid wasting money on idle staff. [4] Easy updation Engineers can use Kubernetes to update or patch applications without shutting them down. It rolls out the updates gradually, so if something goes wrong, it can quickly roll back to the previous version without affecting the entire system. ➡ If your restaurant wants to update the menu, the manager doesn't close the restaurant. Instead, they update one table at a time, ensuring the customers at other tables continue getting their orders. Liked this? Follow Vishal Bagla for more. #Technology #Kubernetes #ProductManagers #ProductManagement

🧩 𝐅𝐫𝐨𝐦 𝐌𝐨𝐧𝐨𝐥𝐢𝐭𝐡𝐢𝐜𝐬 𝐭𝐨 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬: 𝐖𝐡𝐲 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐈𝐬 𝐭𝐡𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐌𝐨𝐝𝐞𝐫𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 In earlier times, monolithic applications were typical, with a single codebase performing all the things. However, as applications grew larger, it got more difficult to expand, upgrade, and keep them up to date. Kubernetes is an open-source container management solution that allows you to split huge structures into different small services, everyone functioning in its own container and being operated automatically over scalability, resilience, and productivity. This modification not only allows the system to function better and more flexibly, but it also sets an example for modern cloud-based architectures. ⚙️ Kubernetes Stack in Production As illustrated in the stack, a robust Kubernetes environment that is production-ready comprises numerous critical layers: 🏗️ Infrastructure Includes Container Registry, DNS, Load Balancing, and IP Management — ensuring seamless deployment and routing across nodes and services. These components automate infrastructure provisioning and make horizontal scaling easy. 🔒 Security Security is woven into the fabric with Kubernetes RBAC, Secret Management, and tools like Vault or External Secret Synchronization. They protect sensitive credentials and apply least-privilege access, reducing the attack surface in production. ⚡ Automation (AUTO) Integrations like IAM, Single Sign-On, and QAUTO/CIDS streamline authentication and security policies — ensuring consistent governance across clusters and users. 🔍 Observability Comprises Logging, Monitoring, Tracing, and Dashboards. These help teams visualize cluster health, performance, and usage in real-time — enabling faster troubleshooting and proactive scaling decisions. 💻 Development Core Kubernetes components such as Ingress, ConfigMaps, Secrets, and Liveness/Readiness Probes ensure smooth application deployments. They help developers push updates independently without affecting the entire system — a huge leap from monolithic release cycles. 🚀 Releases & Deployment With CI/CD, Rolling Deployments, Autotesting, and GitOps Platforms, this layer enables faster and safer delivery. Teams can automate build pipelines, perform zero-downtime rollouts, and revert instantly if issues arise. 🛡️ Secure, Scalable, and Cost-Optimized This complete Kubernetes stack strengthens your security posture through centralized identity, policy management, and secret handling. It also helps reduce cloud costs by: 1. Scaling resources automatically based on load. 2. Optimizing workloads across clusters and regions. 3. Autoscaling reduces overprovisioning and idle compute costs. #devops #kubernetes #cloudairy