AWS EC2 Instance

👉 Step by step guide on AWS EC2 Instances Launch, Types, and Pricing.

👉 Your Comprehensive Tutorial on Elastic Compute Cloud Virtual Servers Types, and Pricing Options.

Table of Contents

Overview

  • AWS EC2 (Elastic Compute Cloud) instance types are pre-configured virtual servers (instances) with a specific combination of CPU, memory, storage, and networking capacity.
  • Think of them as different models of a car, each designed for a specific purpose.
  • For a software architect, understanding instance types isn’t just about picking one; it’s about selecting the right tool for the job to optimize for performance, cost, and scalability.
  • Choosing the wrong instance type can lead to overspending (paying for resources you don’t use) or performance bottlenecks (not having enough resources).

Instance Families

  • AWS organizes EC2 instance types into families based on their core purpose.
  • Each family is identified by a letter. Within a family, there are different generations (a number, e.g., m6 is a newer generation than m5), and then various sizes (e.g., large, xlarge).

The primary instance families are:

  • General Purpose (A, T, M):

    • These instances provide a balanced mix of compute, memory, and networking resources. They’re the go-to for most applications where the workload is not skewed towards a specific resource.

      • T-Series (T2, T3, T4g):Burstable Performance Instances.
        • They provide a baseline CPU performance with the ability to “burst” to a higher level when needed.
        • They earn “CPU Credits” when idle and spend them when bursting. Ideal for workloads with unpredictable CPU usage, like web servers or development environments.
        • T4g instances use AWS Graviton processors, which can offer better price performance.
      • M-Series (M5, M6g, M7g):
        • The most common general-purpose family. Offers a good balance of resources for applications that need consistent performance, like small to medium databases, enterprise applications, and caching fleets.
  • Compute Optimized (C):
    • Designed for compute-intensive workloads that benefit from high-performance processors.

    • They have a high ratio of CPU to memory.

      • Use Cases:
        • Batch processing,
        • High-performance web servers,
        • Media transcoding,
        • High Performace computing (HPC),
        • Dedicated Game Servers and
        • Scientific modeling & machine learning inference engines.
  • Memory Optimized (R, X, Z): Geared towards workloads that process large datasets in memory.

  • They have a high ratio of memory to CPU.

    • Use Cases:
      • High-performance relational/non-relational databases,
      • In-memory databases optimized for BI,
      • Distributed web scale cache stores
      • Real-time big data analytics, and
      • Large-scale data processing engines (e.g., Spark).
  • Storage Optimized (I, D, H):
    • Ideal for workloads that require high, sequential read and write access to very large datasets on local storage.

    • They come with a large amount of fast local storage.

      • Use Cases:
        • Relational & NoSQL databases (Cassandra, MongoDB),
        • High frequency online transcation processing (OLTP) Systems,
        • Cache for in-memory databases (e.g. Redis)
        • Data warehousing applications and
        • Distributed file systems.
  • Accelerated Computing (P, G, F, Inf):

    • Uses hardware accelerators, or co-processors, to perform functions more efficiently than is possible with CPUs alone.

      • P-Series & G-Series:
        • Equipped with NVIDIA GPUs, perfect for machine learning training, scientific simulations, and graphics rendering.
      • Inf-Series:
        • Features AWS Inferentia chips, purpose-built for high-performance machine learning inference at a low cost.
  • High Performance Computing (Hpc):
    • These instances are purpose-built to offer the best price-performance for running HPC workloads at scale.

    • They provide high-throughput networking and are ideal for tightly coupled applications.

      • Use Cases:
        • Complex simulations,
        • Scientific modeling, and
        • Other large-scale computational tasks.

Infrastructure as Code

  • While EC2 instance types are a configuration choice, here’s a conceptual representation of how you might define an instance in a Terraform script, which is a common practice for infrastructure as code.
  • This example highlights how the instance type is a critical parameter.
resource "aws_instance" "app_server" {
  # The AMI (Amazon Machine Image) for the instance
  ami           = "ami-0c55b159f2f534149"  

  # The key parameter: selecting the instance type
  instance_type = "m5.large" # A General Purpose instance

  tags = {
    Name = "MyWebAppServer"
  }
}

resource "aws_instance" "ml_trainer" {
  ami           = "ami-0c55b159f2f534149"
  
  # A different instance type for a different workload
  instance_type = "g5.xlarge" # An Accelerated Computing instance with GPUs

  tags = {
    Name = "MLTrainingServer"
  }
}

An overview of EC2 instance types

graph TD
    subgraph "EC2 Instance Families"
        A[General Purpose] --> B(M - Balanced, T - Burstable, A - ARM)
        C[Compute Optimized] --> D(C - High CPU)
        E[Memory Optimized] --> F(R - High Memory, X - Extreme Memory)
        G[Storage Optimized] --> H(I - High I/O, D - Dense Storage)
        I[Accelerated Computing] --> J(P, G - GPU, Inf - ML Inference)
        K[HPC Optimized] --> L(Hpc - High Performance Networking)
    end

    B --> M[Web Servers, Dev/Test]
    D --> N[Batch Processing, Video Encoding]
    F --> O[In-memory Databases, Caching]
    H --> P[Data Warehousing, NoSQL DBs]
    J --> Q[ML Training, Graphics Rendering]
    L --> R[Scientific Simulations]

    style A fill:#f9f,stroke:#333,stroke-width:2px
    style C fill:#f9f,stroke:#333,stroke-width:2px
    style E fill:#f9f,stroke:#333,stroke-width:2px
    style G fill:#f9f,stroke:#333,stroke-width:2px
    style I fill:#f9f,stroke:#333,stroke-width:2px
    style K fill:#f9f,stroke:#333,stroke-width:2px

Real-World Applications

  • Web Servers & Microservices:
    • A common pattern for a low-to-moderate traffic web application is to use T-series instances (e.g., t3.micro) due to their cost-effectiveness and ability to handle occasional traffic spikes.
    • For more consistent, higher-traffic sites, M-series instances (e.g., m5.large) provide stable performance.
  • Data Processing:
    • A Hadoop cluster would likely use Storage Optimized (I-series) instances for fast access to HDFS data, or Memory Optimized (R-series) if a large portion of the processing is in-memory (e.g., Spark).
  • AI/Machine Learning:
    • For training complex deep learning models, you’d use a P-series or G-series instance with powerful GPUs.
    • For deploying the trained model for real-time predictions (inference), you might use a more cost-effective Inf instance.
  • Databases:
    • Relational databases like MySQL or PostgreSQL that require a large amount of RAM for caching would be best suited for Memory Optimized (R-series) instances.

Advantages and Drawbacks

Advantages:

  • Flexibility and Customization:
    • AWS provides a vast array of instance types, allowing you to fine-tune your resource allocation to match your specific application needs.
  • Cost Optimization:
    • By choosing the right instance type, you can avoid paying for unused resources.
    • For example, a t3.micro for a dev environment is far cheaper than a c5.large.
  • Performance:
    • Choosing a purpose-built instance family (e.g., C for CPU-heavy tasks) ensures your application gets the necessary power to run efficiently.

Drawbacks:

  • Complexity of Choice:
    • The sheer number of instance types can be overwhelming, leading to analysis paralysis or a poor choice.
  • Vendor Lock-in:
    • Applications can be tightly coupled to specific AWS instance features, making it difficult to migrate to other cloud providers.
  • Underutilization/Overprovisioning:
    • Despite the options, it’s common for teams to choose a “one-size-fits-all” instance type, leading to wasted resources or performance issues.

Trade-offs

  • T-series vs. M-series:
    • Choose T-series if you prioritize cost and your workload has variable CPU usage.
    • Choose M-series if you need consistent, predictable performance and are willing to pay for it.
    • The trade-off is cost vs. consistent performance.
  • R-series vs. M-series:
    • Use R-series for memory-bound applications (like large caches) and M-series for balanced workloads.
    • The trade-off is specialization vs. versatility.
  • Intel/AMD vs. Graviton:
    • Graviton-based instances (g suffix, e.g., m6g.large) often offer a better price-to-performance ratio for many workloads, but they use ARM processors.
    • You need to ensure your application (and its dependencies) is compatible with the ARM architecture.
    • The trade-off is cost efficiency vs. potential compatibility overhead.

Best Practices

  1. Start with General Purpose: For new applications or unknown workloads, begin with an M-series or T-series instance.
  2. Monitor Your Workload: Use Amazon CloudWatch to monitor key metrics like CPU utilization, memory usage, and network I/O. This data will tell you if your current instance type is a good fit.
  3. Use AWS Compute Optimizer: This free AWS service analyzes your historical usage and recommends the optimal EC2 instance type for your workloads.
  4. Right-size Regularly: As your application evolves, it’s resource needs change. Periodically review and adjust your instance types to ensure you’re not over-provisioned.
  5. Leverage Burstable Instances: For dev, test, and other non-production environments, use T-series instances to save on costs.
  6. Consider Reserved Instances/Savings Plans: Once you’ve right-sized your instances and have a predictable long-term workload, use these pricing models to significantly reduce costs.

Interview Angle

Possible Questions:

  • “You’re designing a new microservice. How would you choose the right EC2 instance type?”
  • “Explain the difference between a t3.large and an m5.large instance.”
  • “What is a burstable instance, and when would you use one?”
  • “How would you optimize the cost of an EC2-based application?”

How to Answer Confidently:

  • Start by categorizing the instance types into the five main families (General Purpose, Compute, Memory, Storage, Accelerated).
  • This shows you have a structured understanding.
  • Mention the key characteristics of each family (CPU, RAM, Storage).
  • Use the “right tool for the job” analogy.
    • For the microservice question, say you’d start with a general-purpose instance and use CloudWatch to monitor its performance, then right-size it to a more specialized instance if needed.
  • For the t3.large vs m5.large question, highlight the core difference:
    • t3 is burstable (cost-effective for variable workloads),
    • while m5 provides a consistent baseline (better for predictable, heavy workloads).
  • For cost optimization, mention the steps:
    • 1. Right-size (get the right instance type),
    • 2. Use T instances for non-prod, and
    • 3. Use Reserved Instances or Savings Plans for long-term commitment.

Online References

Summary

  • AWS EC2 instance types are categorized into families (General Purpose, Compute, Memory, Storage, Accelerated, HPC) to provide optimized resource configurations for different workloads.
    • The naming convention (family.generation.size) helps identify the type.
  • The key to effective usage is to understand your application’s resource needs and choose the instance that provides the best balance of performance and cost.
  • Use monitoring tools like CloudWatch and services like Compute Optimizer to continuously right-size your instances and avoid overspending.

More Insights

Analogy: Think of EC2 instance families like different professional athletes:

  • T-series is a marathon runner. It’s built for endurance and can sprint when needed, but can’t sustain top speed forever.
  • C-series is a sprinter. It’s built for short, powerful bursts of activity (high CPU).
  • R-series is a weightlifter. It’s all about lifting heavy loads (large datasets) efficiently.
  • G-series is a Formula 1 driver. It uses specialized hardware (a super-powerful engine/GPU) to achieve extreme performance for niche tasks.

Checklist for Choosing an Instance Type:

  1. Workload Profile: Is it CPU-intensive, memory-intensive, or I/O-intensive?
  2. Performance Needs: Does it require consistent, high performance or is it a bursty workload?
  3. Cost Constraints: Is this a dev environment or a production application?
  4. Processor Architecture: Is the application compatible with ARM (Graviton)?
  5. Storage Needs: Does it require fast local storage or is EBS sufficient?

Takeaway for Certification

  • Key Families:
    • You must know the five main families and their primary use cases. T (burstable), M (general purpose), C (compute), R (memory), I/D (storage), and G/P (accelerated).
  • Burstable Instances:
    • The concept of CPU Credits for T-series instances is a frequent test topic. Understand how they earn and spend credits and the difference between Standard and Unlimited modes.
  • Naming Convention:
    • Be able to decode an instance name like m6g.xlarge. Know that m is the family, 6 is the generation, g is the processor type (Graviton), and xlarge is the size.
  • Cost Optimization:
    • Understand that choosing the right instance type is the first step in cost optimization, followed by using cost models like Reserved Instances, Savings Plans, and Spot Instances.
  • Exam Questions:
    • Expect scenario-based questions asking you to recommend the best instance type for a given workload (e.g., “A customer has a large in-memory database.
    • Which instance type should you recommend?”).
      • The answer would be a memory-optimized instance like an R-series or X-series.