Calculate CPU, RAM for EC2

Calculating the CPU and RAM requirements for an API application that needs to handle 40 million user requests involves several steps. This process generally involves:

1. Understanding the workload:

   • Average request size and complexity
   • Response time requirements
   • Peak load vs. average load

2. Performance Testing:

   • Load testing to determine resource utilization (CPU, RAM) per request.

3. Estimating Resource Requirements:

   • Using data from performance tests to scale up to the expected load.

I. Formula for Estimating CPU and RAM Requirements

Step-by-Step Approach:

1. Determine Requests Per Second (RPS):

   • Assume 40 million requests need to be handled in a given period (e.g., per day).
   • Convert this to seconds: $40,000,000\text{ requests}\mathrm{/}86,400\text{ seconds/day}\approx 463\text{ RPS}$.

2. Load Testing:

   • Perform load tests to measure the CPU and RAM usage per request.
   • Let’s assume during load testing, you found that one request consumes 10ms of CPU time and 10MB of RAM on average.

3. CPU Requirements:

   • CPU requirement per request: $\text{CPU time per request}\times \text{RPS}$.
   • CPU time per second (in terms of cores) = $\text{CPU time per request (in seconds)}\times \text{RPS}$.
   • Example: $0.01\text{ seconds/request}\times 463\text{ requests/second}=4.63\text{ CPU seconds/second}$.
   • Therefore, you would need approximately 4.63 CPU cores to handle the load.

4. RAM Requirements:

   • RAM requirement per request: $\text{RAM usage per request}\times \text{RPS}\times \text{Average request duration}$.
   • Assume the average request duration is 0.1 seconds.
   • Example: $10\text{ MB/request}\times 463\text{ RPS}\times 0.1\text{ seconds}=463\text{ MB}$.
   • This means you would need around 463 MB of RAM to handle the simultaneous requests at peak load.

Final Formulae:

• CPU Cores:

  $$\text{CPU cores}=\text{CPU time per request (in seconds)}\times \text{RPS}$$

• RAM:

  $$\text{RAM (MB)}=\text{RAM per request (MB)}\times \text{RPS}\times \text{Average request duration (seconds)}$$

Considerations:

1. Scaling and Redundancy:

   • Include additional capacity for failover and redundancy.
   • Consider autoscaling to handle variable loads.

2. Overheads:

   • Account for other system overheads (background processes, operating system, etc.).

3. Peak Load vs. Average Load:

   • Design for peak load but consider cost-efficiency for average load.

Example Calculation:

Assume you find the following through load testing:

• CPU time per request: 10ms (0.01 seconds)
• RAM usage per request: 10MB
• Average request duration: 0.1 seconds

For 463 RPS:

• CPU cores:

  $$\text{CPU cores}=0.01\text{ seconds/request}\times 463\text{ requests/second}=4.63\text{ cores}$$

• RAM:

  $$\text{RAM (MB)}=10\text{ MB/request}\times 463\text{ RPS}\times 0.1\text{ seconds}=463\text{ MB}$$

Thus, you'd need approximately 4.63 CPU cores and 463 MB of RAM to handle the load of 40 million requests per day, assuming a consistent load throughout the day.

II. Calculating Number of Tasks Per EC2 Instance

1. Define Task Requirements:

   • Assume each ECS task requires 256 CPU units (0.25 vCPU) and 512 MiB of memory.

2. Select EC2 Instance Type:

   • Suppose you are using an m5.large instance (2 vCPUs and 8 GiB of memory).

3. Calculate Resource Capacity:

   • CPU: m5.large has 2 vCPUs = 2048 CPU units (ECS uses 1024 CPU units per vCPU).
   • Memory: m5.large has 8 GiB = 8192 MiB.

4. Calculate Maximum Number of Tasks per EC2 Instance:

   • CPU: 2048 CPU units / 256 CPU units per task = 8 tasks.
   • Memory: 8192 MiB / 512 MiB per task = 16 tasks.

   The limiting factor here is CPU, so you can run up to 8 tasks per m5.large instance.

Note:

This is a simplified estimation. Real-world applications require more detailed performance testing and consideration of factors such as network latency, database performance, caching strategies, and other infrastructure components