Gunaseela Perumal M’s Post

AWS Application load balancing with Auto Scaling 🫡😎 AWS Application Load Balancing (ALB): This is a service provided by Amazon Web Services (AWS) that helps distribute incoming web traffic across multiple targets, such as EC2 instances, containers, or IP addresses. It balances the load to ensure that no single target gets overwhelmed, improving the performance, reliability, and scalability of your applications. Auto Scaling: This is another AWS service that automatically adjusts the number of resources (like EC2 instances) in response to changes in demand for your application. When there's high traffic, it adds more instances to handle the load, and when traffic decreases, it scales down to save costs. The Process: When you use ALB with Auto Scaling, here's what happens: You set up an ALB to distribute incoming traffic to your application. You also set up Auto Scaling to monitor the demand for your application and automatically adjust the number of EC2 instances running your application. When traffic increases, ALB detects this and starts sending traffic to the existing instances. As the load on those instances increases, Auto Scaling notices and automatically adds more instances to handle the extra load. When traffic decreases, Auto Scaling scales down by terminating unnecessary instances to save costs. Throughout this process, ALB ensures that traffic is evenly distributed among all instances, providing a seamless experience for users. In simple terms, it's like having a smart traffic cop (ALB) directing cars to different lanes (instances) of a highway, and when more cars show up, more lanes are opened automatically (Auto Scaling), ensuring smooth traffic flow without any congestion checkout the blog written by sagar Kulkarni on medium ---> https://lnkd.in/g4YYcy7U

AWS Application load balancing with Auto Scaling 🫡😎 AWS Application Load Balancing (ALB): This is a service provided by Amazon Web Services (AWS) that helps distribute incoming web traffic across multiple targets, such as EC2 instances, containers, or IP addresses. It balances the load to ensure that no single target gets overwhelmed, improving the performance, reliability, and scalability of your applications. Auto Scaling: This is another AWS service that automatically adjusts the number of resources (like EC2 instances) in response to changes in demand for your application. When there's high traffic, it adds more instances to handle the load, and when traffic decreases, it scales down to save costs. The Process: When you use ALB with Auto Scaling, here's what happens: You set up an ALB to distribute incoming traffic to your application. You also set up Auto Scaling to monitor the demand for your application and automatically adjust the number of EC2 instances running your application. When traffic increases, ALB detects this and starts sending traffic to the existing instances. As the load on those instances increases, Auto Scaling notices and automatically adds more instances to handle the extra load. When traffic decreases, Auto Scaling scales down by terminating unnecessary instances to save costs. Throughout this process, ALB ensures that traffic is evenly distributed among all instances, providing a seamless experience for users. In simple terms, it's like having a smart traffic cop (ALB) directing cars to different lanes (instances) of a highway, and when more cars show up, more lanes are opened automatically (Auto Scaling), ensuring smooth traffic flow without any congestion checkout the blog written by sagar Kulkarni on medium ---> https://lnkd.in/g4YYcy7U

🫡😎 AWS Application Load Balancing with Auto Scaling 😎🫡 Elevate your application's performance and reliability with the dynamic duo of AWS Application Load Balancing (ALB) and Auto Scaling! AWS Application Load Balancing (ALB): ALB, a premier service by Amazon Web Services (AWS), acts as your traffic orchestrator, efficiently distributing incoming web traffic across multiple targets like EC2 instances, containers, or IP addresses. By balancing the load, ALB ensures that no single target is overwhelmed, thereby enhancing your application's performance, reliability, and scalability. Auto Scaling: Enter Auto Scaling, another ingenious AWS service that intelligently adjusts the number of resources, such as EC2 instances, in response to fluctuations in demand for your application. When traffic surges, Auto Scaling seamlessly adds more instances to accommodate the load, and during downtimes, it scales down to optimize costs. The Process: Here's a glimpse of how ALB and Auto Scaling collaborate: Setting Up ALB: Configure ALB to efficiently distribute incoming traffic to your application. Implementing Auto Scaling: Enable Auto Scaling to monitor your application's demand and automatically adjust the number of EC2 instances accordingly. Dynamic Traffic Handling: ALB detects traffic spikes and directs it to existing instances. Seamless Scaling: As load increases, Auto Scaling dynamically adds more instances to manage the surge efficiently. Cost Optimization: During low traffic periods, Auto Scaling intelligently scales down by terminating unnecessary instances, optimizing costs. Throughout this seamless orchestration, ALB ensures uniform traffic distribution across all instances, delivering a flawless user experience. In essence, it's akin to having a savvy traffic cop (ALB) directing cars to various lanes (instances) of a highway. And when traffic intensifies, additional lanes are opened automatically (Auto Scaling), ensuring smooth traffic flow sans congestion. 📖 Read Sagar Kulkarni's insightful blog on Medium: Link Embrace the power of ALB and Auto Scaling for unparalleled application performance and scalability! 🚀 #AWS #ApplicationLoadBalancing #AutoScaling #CloudComputing #Efficiency #Scalability 🌐

What is Auto Scaling in AWS? Auto Scaling in AWS is a feature that automatically adjusts the number of compute resources (like EC2 instances) in response to changing demand. It helps ensure that applications have the right amount of resources at any given time, which can optimize costs and maintain performance. Key Components of AWS Auto Scaling: Auto Scaling Groups (ASG): Auto Scaling Group is a logical grouping of EC2 instances that can be automatically scaled in or out. The group defines the minimum, maximum, and desired number of instances. Minimum Size: The minimum number of instances that should always be running in the group. Maximum Size: The maximum number of instances that the group can scale up to. Desired Capacity: The ideal number of instances that the group tries to maintain. If the desired capacity is different from the actual number of running instances, Auto Scaling will add or remove instances to match the desired capacity. Health Checks: Auto Scaling performs regular health checks on instances. If an instance is found to be unhealthy (e.g., not responding), Auto Scaling can terminate it and launch a new one to replace it. How Auto Scaling Works: Scaling Out (Adding Instances): When the demand for your application increases, Auto Scaling launches additional instances to handle the load. Scaling In (Removing Instances): When demand decreases, Auto Scaling terminates instances to reduce costs while maintaining the minimum required capacity. Benefits of Auto Scaling: Cost Efficiency: Automatically reduces the number of instances when demand is low, minimizing costs. Improved Performance: Ensures enough instances are available to handle traffic spikes, maintaining application performance. High Availability: Distributes instances across multiple Availability Zones, improving fault tolerance and redundancy. Integration with Other AWS Services: Elastic Load Balancing (ELB): Auto Scaling works with ELB to distribute traffic across the scaled instances. Types of Scaling Policies: Dynamic Scaling: Adjusts the number of instances based on real-time metrics (like CPU utilization or request count). Target Tracking Scaling: Automatically adjusts the number of instances to maintain a specific metric (e.g., keeping CPU usage at 50%). Step Scaling: Adjusts the number of instances in increments based on a defined threshold (e.g., add 2 instances if CPU exceeds 70%). Simple Scaling: Adds or removes instances based on a specific alarm condition. Scheduled Scaling: Adjusts the number of instances based on a specific schedule (e.g., scaling up at 9 AM and scaling down at 5 PM). Amazon CloudWatch: Monitors your applications and triggers Auto Scaling based on defined alarms.

🚀 Day 39 :- AWS Part 1 🌟 Topic 1: Auto Scaling Introduction 🌟 Topic 2: Autoscaling Group Hands On 📌 Topic 1: Auto Scaling Introduction 1️⃣ Introduction to Auto Scaling:   - ASG is introduced as a straightforward service, particularly for those familiar with EC2 and CloudWatch.   - It integrates with CloudWatch to monitor resources based on selected metrics like CPU utilization.   - ASG dynamically adjusts capacity by adding or removing instances based on predefined CloudWatch alarms. 2️⃣ Launch Configuration or Launch Template:   - ASG uses launch configuration or launch template to provision instances.   - Launch configuration/template specifies instance launch information. 3️⃣ Scaling Policies:   - Scaling policies dictate how ASG adjusts capacity in response to metrics.   - Policies can be predefined or customized (e.g., step scaling based on CPU utilization thresholds). 4️⃣ Configuration Parameters:   - Parameters such as minimum size, desired capacity, and maximum size are crucial.   - Minimum size ensures a baseline number of instances always running.   - Desired capacity indicates the ideal number of instances.   - Maximum size limits the number of instances ASG can scale up to. 5️⃣ Design and Workflow:   - ASG design involves creating a group powered by launch configuration/template.   - CloudWatch alarms trigger based on specified metrics, leading to scaling events.   - Scaling policies dictate the addition or removal of instances within the ASG based on alarm triggers. 📌 Topic 2: Autoscaling Group Hands On 1️⃣ Preparation:   - Ensure the presence of a launch template, which was previously created in the load balancer section.   - Verify the existence of a load balancer. 2️⃣ Creating Target Group:   - Create a target group named "health-TG" without any instances. 3️⃣ Creating Load Balancer:   - Create a load balancer named "health-elb" and associate it with the target group. 4️⃣ Configuring Auto Scaling Group:   - Name the ASG (healthy-ASG)   - Select the previously created launch template.   - Choose availability zones for launching instances.   - Associate the ASG with the existing load balancer and target group.   - Configure health checks, either using EC2 health check or ELB health check.   - Specify desired capacity (e.g., 2), minimum size, and maximum size.   - Set up scaling policies, such as target tracking scaling policy based on CPU utilization.   - Add any desired notifications.   - Add tags for identification.   - Review and create the ASG. 5️⃣ Monitoring and Management:   - Monitor instances and their health status in the ASG.   - Understand that instances are dynamic and managed by the ASG, so manual changes are discouraged.   - Refresh instances by updating the launch template and triggering an instance refresh.   #devops #devopslearningjourney #devopsengineer #devopscommunity #devopstools #devopsjourney #devopslearning #learninginpublic #LearningJourney #DAY39 #cloudcomputing #AWS #amazonwebservices

Optimizing Kubernetes with Karpenter, Node Pools, and Fargate: Solving EC2 Metadata Errors and Enhancing Cloud Efficiency Navigating Kubernetes: Karpenter Consolidation, Node Pools, and IAM Policies for Fargate Kubernetes is a powerful container orchestration tool that simplifies the deployment, management, and scaling of applications. However, managing Kubernetes effectively requires addressing various complexities, particularly when dealing with tools like Karpenter, node pools, and Fargate. This blog explores these topics and offers practical solutions to common challenges, especially EC2 metadata errors. Introduction Kubernetes has become the go-to solution for container orchestration due to its scalability and flexibility. As organizations adopt Kubernetes, they often encounter challenges related to resource management, scaling, and security. Tools like Karpenter, node pools, and Fargate are designed to address these issues, but they come with their own set of problems and considerations. What is Karpenter? Karpenter is an open-source, flexible, high-performance Kubernetes cluster autoscaler. It automatically adjusts the size of your cluster, adding and removing nodes based on the demands of your workloads. This helps ensure efficient resource utilization and cost management. Understanding Node Pools Node pools are groups of nodes within a Kubernetes cluster that share the same configuration. They allow for better resource allocation and management by segregating workloads based on their specific requirements, such as compute or memory-intensive tasks. Fargate and IAM Policies AWS Fargate is a serverless compute engine for containers that works with both Amazon ECS and EKS. It allows you to run containers without managing the underlying infrastructure. IAM (Identity and Access Management) policies are crucial for securing access to AWS resources, ensuring that only authorized entities can perform specific actions. Common Problems and Solutions Karpenter Consolidation Problem: Inefficient resource utilization and increased costs due to over-provisioning of nodes.  Solution: Karpenter helps consolidate workloads by dynamically adjusting the number of nodes in your cluster based on real-time demand. This reduces costs by avoiding over-provisioning and ensures that resources are used efficiently. Conclusion Managing Kubernetes with tools like Karpenter, node pools, and Fargate can significantly enhance your container orchestration capabilities. By understanding the common challenges and applying the solutions and best practices outlined in this blog, you can optimize your Kubernetes environment for better performance, cost-efficiency, and security. Remember to regularly monitor and update your configurations to keep up with evolving workload demands and security requirements.

A Quick knowledge check on AWS EKS: 1. Fargate Integration: AWS EKS supports AWS Fargate, allowing you to run Kubernetes pods without managing the underlying infrastructure. This serverless compute engine helps simplify Kubernetes operations by abstracting away the need to manage EC2 instances. 2. IAM Roles for Service Accounts: AWS EKS supports assigning IAM roles to Kubernetes service accounts, providing fine-grained access control for Kubernetes workloads to AWS services, thus enhancing security and governance. 3. Custom AMIs: You can use custom Amazon Machine Images (AMIs) with AWS EKS. This allows you to tailor the underlying EC2 instances to meet specific compliance, security, and operational requirements. 4. EKS Distro (EKS-D): AWS offers the EKS Distro, which is the same Kubernetes distribution used by EKS. It allows you to run Kubernetes clusters on-premises or in environments outside AWS, ensuring consistency with EKS-managed clusters. 5. Security Group for Pods: AWS EKS supports the use of security groups for pods. This feature enables network isolation at the pod level, enhancing security by allowing fine-grained control over pod communications. 6. Cluster Autoscaler Integration: AWS EKS integrates with the Kubernetes Cluster Autoscaler, allowing clusters to automatically adjust the number of nodes based on the resource needs of the workloads, optimizing cost and performance. 7. EKS Connector: AWS offers EKS Connector, which lets you connect and manage your Kubernetes clusters outside of AWS (on-premises or in other cloud environments) through the EKS console, providing a unified management experience. 8. Managed Node Groups: AWS EKS provides managed node groups, which automatically handle the provisioning and lifecycle management of EC2 instances, including updates and scaling operations, reducing operational overhead. 9. Bottlerocket OS: AWS developed Bottlerocket, a Linux-based operating system optimized for running containers. It is supported by EKS and designed to improve security and operational efficiency with minimal overhead. 10. Service Mesh Integration: AWS EKS supports integration with AWS App Mesh, enabling you to manage microservices traffic within your Kubernetes clusters with enhanced observability, security, and resiliency features. Comment below if you have learned something new from this post!

Ahh, the good old scaling. Easy peasy, right? Just create a trust auto scaling group, and you are done. Not so fast! Scaling is an important topic for both interviews and real-world projects, and in today's newsletter, we are going to learn how it differs between EC2, Lambda, and Kubernetes. Heads up - Kubernetes scaling is the trickiest, and we will spend the most amount of time explaining that part. Let's get started: 1. Scaling vanilla EC2 Quite straightforward. You create an auto-scaling group that automatically adjusts the number of EC2 instances based on defined policies. You can set up scaling policies based on metrics such as CPU utilization, memory usage, or custom CloudWatch metrics. You can mention the minimum, maximum, and desired number of EC2s in this auto-scaling group to control cost. Pro tip - Use scheduled scaling to pre-warm EC2 instances before a peak traffic event for your business 2. Scaling Lambda This is the easiest service to scale among the three. Unlike vanilla EC2, and Kubernetes pods, one Lambda instance only serves one connection. As the concurrent traffic increases, each connection creates additional instances of the Lambda function. For that reason, no scaling policy is required. AWS Lambda function will automatically scale out of the box. You need to adjust the max concurrency limit for your function based on peak traffic Pro tip - If you want to avoid Lambda cold start during heavy traffic bursts, enable Lambda Provisioned Concurrency, which will pre-warm pre-defined numbers of Lambda instances before a traffic surge happens. You can configure provisioned Concurrency either by schedule or dynamic target tracking (i.e. maintain the Provisioned Concurrency utilization at 80% of the total Provisioned Concurrency) 3. Scaling Kubernetes In most Cloud interviews, you'd get this question: "Tell me how the Kubernetes cluster EC2 worker nodes scale." Most candidates answer: set Auto Scaling Groups to scale at a certain EC2 metric utilization like scaling regular EC2s. This answer is WRONG! Instead here is how K8s cluster worker EC2, also known as VMs or Virtual Machines, scale: Step 1: You configure HPA (Horizontal Pod Autoscaler) to increase the replica of your pods at a certain CPU/Memory/Custom Metrics threshold. Step 2: As traffic increases and the pod metric utilization crosses the threshold, HPA increases the number of pods. If there is capacity in the existing worker VMs, then the Kubernetes kube-scheduler binds that pod into the running VMs. Step 3, 4, 5, and protip I am out of room here. To read the rest of the steps and the pro tip for Kubernetes scaling, check out the full article here: https://lnkd.in/gHHSYFPd Readers - What kind of scaling challenges are you running into?

A Quick Knowledge Check on AWS EKS: 1. Service Mesh Integration: AWS EKS supports integration with AWS App Mesh, enabling you to manage microservices traffic within your Kubernetes clusters with enhanced observability, security, and resiliency features. 2. Bottlerocket OS: AWS developed Bottlerocket, a Linux-based operating system optimized for running containers. It is supported by EKS and designed to improve security and operational efficiency with minimal overhead. 3. Fargate Integration: AWS EKS supports AWS Fargate, allowing you to run Kubernetes pods without managing the underlying infrastructure. This serverless compute engine helps simplify Kubernetes operations by abstracting away the need to manage EC2 instances. 4. IAM Roles for Service Accounts: AWS EKS supports assigning IAM roles to Kubernetes service accounts, providing fine-grained access control for Kubernetes workloads to AWS services, thus enhancing security and governance. 5. Custom AMIs: You can use custom Amazon Machine Images (AMIs) with AWS EKS. This allows you to tailor the underlying EC2 instances to meet specific compliance, security, and operational requirements. 6. EKS Distro (EKS-D): AWS offers the EKS Distro, which is the same Kubernetes distribution used by EKS. It allows you to run Kubernetes clusters on-premises or in environments outside AWS, ensuring consistency with EKS-managed clusters. 7. Security Group for Pods: AWS EKS supports the use of security groups for pods. This feature enables network isolation at the pod level, enhancing security by allowing fine-grained control over pod communications. 8. Cluster Autoscaler Integration: AWS EKS integrates with the Kubernetes Cluster Autoscaler, allowing clusters to automatically adjust the number of nodes based on the resource needs of the workloads, optimizing cost and performance. 9. EKS Connector: AWS offers EKS Connector, which lets you connect and manage your Kubernetes clusters outside of AWS (on-premises or in other cloud environments) through the EKS console, providing a unified management experience. 10. Managed Node Groups: AWS EKS provides managed node groups, which automatically handle the provisioning and lifecycle management of EC2 instances, including updates and scaling operations, reducing operational overhead. Comment below if you have learn something new from this post .

Scaling Your App: Lambda vs EC2 - Making the Right Choice 🚀 As your app grows, choosing the right infrastructure becomes crucial. Let's dive into when AWS Lambda shines and when it might be time to consider other options like EC2. Key Metrics to Consider: • High Traffic: 100,000+ requests per day • Concurrent Users: 1,000+ simultaneous connections • Data Transfer: Over 1 TB per month Lambda Pros: ✅ Pay-per-use: Only pay for what you use ✅ Auto-scaling: Scales automatically ✅ No server management: Zero server maintenance Lambda Cons at Scale: ❗ Cold starts: Potential delay in response times ❗ Higher costs at volume: Can get expensive with very high usage ❗ 15-minute execution limit: Not suitable for long-running tasks Cost Comparison Scenarios: 1. Low Traffic (50,000 requests/month):   • Lambda + API Gateway: ~$20/month   • EC2 t3.micro: ~$30/month 2. High Traffic (10 million requests/month):   • Lambda + API Gateway: ~$3,500/month   • EC2 c5.xlarge: ~$300/month 3. Very High Traffic (100 million requests/month, 10TB data transfer):   • Lambda + API Gateway: ~$2,125/month   • EC2 with Node.js (10 x m5.large): ~$1,731/month When to Choose: • Lambda: Perfect for unpredictable workloads or low usage periods • EC2 with Node.js: More cost-effective for consistent, high-volume traffic Understanding Traffic Levels: Requests per Day: • Low: Up to 10,000 • Moderate: 10,000 to 1 million • High: Over 1 million • Very High: Tens of millions or more Concurrent Users: • Low: Hundreds • Moderate: Thousands • High: Tens of thousands • Very High: Hundreds of thousands or more Data Transfer per Month: • Low: Up to 1TB • Moderate: 1TB to 10TB • High: 10TB to 100TB • Very High: Over 100TB Additional Lambda Considerations for Rapid Scaling: 1. Cold Start Latency: Can impact performance for infrequent functions 2. Execution Duration Limits: Max 15 minutes per execution 3. Resource Limits: Capped memory and temporary disk space 4. Concurrency Limits: Default 1,000 concurrent executions (can be increased) 5. Cost Efficiency: Potentially expensive at very high volumes 6. Complexity in Orchestration: Managing numerous Lambda functions can be challenging Bottom Line: Lambda is excellent for low to moderate traffic or highly variable loads. For sustained high traffic, EC2 often wins in terms of cost-effectiveness. However, the choice depends on your specific use case, considering factors like: • Traffic patterns • Scalability requirements • Operational overhead • Budget constraints • Performance needs Balancing cost efficiency with performance and scalability is key. The right choice can significantly impact your application's success and your bottom line. What's your take on serverless vs. traditional deployments? Have you faced similar scaling decisions? Share your experiences in the comments! #AWS #Serverless #CloudComputing #TechScaling #EC2 #Lambda #Scalability #HighTraffic #CostEfficiency