This repository contains open research for "Dynamic Optimization and Latency Management in Autonomous and Real-Time Systems." The framework explores cutting-edge strategies to manage and optimize algorithmic and computational latency in high-performance, real-time systems, such as autonomous vehicles and cloud task systems.
These additions will make the paper stronger in later versions but are too complex to tackle right now. We’ll save them for future iterations once we’ve established the baseline.
1. Hold Off on Stochastic Latency Models (G/G/1)
Why: G/G/1 models allow for handling complex, unpredictable arrival rates, but we don’t need that level of detail yet.
Goal: Mention this as a future enhancement for cases where system load is less predictable.
2. Skip Dynamic Reconfiguration for Now
Why: Adjusting settings based on the environment (like traffic conditions) adds a lot of complexity. Static configurations work fine for our current latency focus.
Goal: Note this for future research when we expand into more adaptive, real-time adjustments.
3. Defer Reinforcement Learning (RL) for Failure Prevention
Why: RL is a great next step but requires complex training and real-time learning. LSTM is more manageable for now.
Goal: Leave RL as a suggestion for future iterations when we’re ready for a self-learning system.
4. Don’t Include Network Latency or Offloading Yet
Why: Right now, we’re focusing on on-vehicle latency. Cloud or edge offloading complicates things with network delays, which aren’t central to our current goal.
Goal: Indicate that network latency will become important as we expand to cloud processing.
5. Hold Off on Graceful Degradation Policies
Why: Graceful degradation keeps the system running under heavy load, but for now, we’re focused on preventing failures rather than managing them after they happen.
Goal: Suggest this as a future area to explore once failure prediction is stabilized.
6. Defer Power/Resource Efficiency Trade-Offs
Why: While balancing power use and latency is important, it complicates the latency focus. Adding power concerns would divert from our main point.
Goal: Leave this for future research, especially as we expand to power-constrained systems like electric vehicles.
Future Direction Summary
Deferred Enhancements more advanced models (stochastic latency, dynamic reconfiguration, RL), network latency, and resource optimization. These can wait until after we’ve fully established the basics.
1. Future Enhancements to Leave Out for Now
These additions will make the paper stronger in later versions but are too complex to tackle right now. We’ll save them for future iterations once we’ve established the baseline.
1. Hold Off on Stochastic Latency Models (G/G/1)
2. Skip Dynamic Reconfiguration for Now
3. Defer Reinforcement Learning (RL) for Failure Prevention
4. Don’t Include Network Latency or Offloading Yet
5. Hold Off on Graceful Degradation Policies
6. Defer Power/Resource Efficiency Trade-Offs
Future Direction Summary
Deferred Enhancements more advanced models (stochastic latency, dynamic reconfiguration, RL), network latency, and resource optimization. These can wait until after we’ve fully established the basics.