FUTURE.md

Sanity Check: Is This Approach Worth Continuing?

I wanted to share my thoughts on whether it's worth pursuing further. Short answer: yes, but we need to make some tweaks.

Current Approach

Right now, we're doing this:

1. Simulating and logging traffic per region
2. Storing historical data
3. Using average traffic to decide on deployments/removals
4. Executing those actions

It's a decent start, but I've noticed some issues:

• Our static thresholds and averages aren't great at handling dynamic traffic
• There's a lag between deciding to deploy/remove and it actually happening
• We might be scaling too often, which could destabilize things and rack up costs
• Simple averages aren't cutting it for predicting traffic accurately

Pitfalls We Need to Watch Out For

1. Our prediction model is too basic

   • We should look into more advanced time series forecasting

2. We're reacting too slowly to traffic changes

   • Need to add some real-time monitoring and faster scaling

3. Performance might not scale well

   • Time to optimize our data handling

4. We might be hitting Fly.io limits

   • Gotta dig into their docs and maybe chat with support

5. Costs could get out of hand

   • Let's add some cost-aware decision making

Cool Stuff We Could Use

1. Better forecasting models

   • ARIMA, Holt-Winters, or even some ML like LSTM or Prophet
   • Here's a quick example with Prophet:

   from fbprophet import Prophet
   import pandas as pd
   
   df = pd.DataFrame({'ds': our_timestamps, 'y': our_traffic_data})
   model = Prophet()
   model.fit(df)
   future = model.make_future_dataframe(periods=24, freq='H')
   forecast = model.predict(future)
   # Use this forecast for decisions

2. Real-time scaling

   • Maybe use Kafka or RabbitMQ for monitoring

3. Fly.io's built-in features

   • They might have some autoscaling stuff we can use

4. Fancy data structures

   • Count-Min Sketch or HyperLogLog could be useful for big data

5. Smart thresholds

   • Let's make our thresholds adapt:

   def calculate_adaptive_threshold(historical_data, window_size=24):
       recent_data = historical_data[-window_size:]
       mean = np.mean(recent_data)
       std_dev = np.std(recent_data)
       traffic_threshold = mean + 2 * std_dev
       deployment_threshold = mean - std_dev
       return traffic_threshold, deployment_threshold

6. Better state management

   • Etcd or Consul could be handy

What's Next?

1. Upgrade our prediction game

2. Add real-time monitoring (Prometheus + Grafana?)

3. Smarter scaling (cooldowns, batching)

4. Add hysteresis to prevent flip-flopping:

   def hysteresis_scaling(current_traffic, current_instances, up_threshold, down_threshold):
       if current_traffic > up_threshold * current_instances:
           return 'scale_up'
       elif current_traffic < down_threshold * current_instances:
           return 'scale_down'
       return 'no_action'

5. Deep dive into Fly.io's capabilities

6. Containerize everything and set up CI/CD

Watch Out For

• Over-engineering: Let's not make it too complex
• Fly.io limits: We need to stay within their boundaries

Wrapping Up

I think we've got a solid foundation here. With some tweaks and by leveraging some existing tools, we can make this system pretty robust.

TODO:

1. Research those forecasting models
2. Dive into Fly.io docs
3. Set up some small-scale tests
4. Keep an eye on performance and iterate

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Wild Idea: If Fly.io's autoscaling is good enough, we might not need all this custom stuff. Also, we could look into serverless functions (Fly.io Webhooks or Cloudflare Workers) for even better scalability. Just a thought!