🚀 Day 10: AWS Project - Classic Load Balancer & Auto Scaling Implementation 🌐

Step - by - Steps

I recently completed an AWS project focusing on deploying and managing a highly available infrastructure using Classic Load Balancer and Auto Scaling. Here's the process I followed:

Launched Two EC2 Instances:

• Configured two instances and installed Apache server using shell commands to serve web traffic.

Created Classic Load Balancer:

• Set up the load balancer to evenly distribute incoming traffic across both instances, ensuring fault tolerance and availability.

Auto Scaling Setup:

• Created an AMI from the configured instances.

• Launched an Auto Scaling template and Auto Scaling Group.

• Monitored Auto Scaling, which automatically launched two additional instances to manage increased traffic loads.

Tested Auto Scaling:

• Simulated instance failure by manually terminating two instances.

• Auto Scaling automatically launched two new instances, maintaining seamless availability.

This project strengthened my understanding of:

• Load balancing and auto-scaling strategies.

• Effective cloud infrastructure management and ensuring high availability using AWS services.

Why Use Load Balancers?

Load balancers distribute incoming traffic across multiple servers to prevent any single server from being overwhelmed, ensuring high availability and reliability.

Types of AWS Load Balancers:

• Classic Load Balancer (CLB): Basic load balancing across multiple EC2 instances; supports HTTP/HTTPS and TCP.

• Application Load Balancer (ALB): Ideal for routing traffic at the application layer (Layer 7) with advanced request routing (based on URL, headers, etc.).

• Network Load Balancer (NLB): Optimized for high-performance, low-latency routing at the network layer (Layer 4), capable of handling millions of requests per second.

Use Cases for Load Balancers:

• Web Applications: Ensures high availability and efficient traffic distribution.

• Microservices Architectures: Routes traffic between different services based on specific rules.

• Distributed Systems: Maintains reliability by balancing workloads across components in distributed systems.

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