Latency Optimization Framework

This repository contains the research and accompanying code 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.

The research integrates Queue Theory, Computational Efficiency, and Dynamic Orchestration techniques to propose a Generalized Optimization Framework capable of reducing system latency while balancing cost and resource constraints. It also introduces the Super Ego agent, a chore orchestration neural network that dynamically adapts system behavior to manage indeterminate and adversarial conditions.

Key features include:

• Theoretical foundations of latency optimization in real-time systems
• Mathematical models for managing queue theory and system bottlenecks
• Practical examples of applying these concepts to autonomous vehicle systems
• A generalized framework for latency reduction, cost optimization, and task scheduling

Usage

This repository is intended for researchers, engineers, and practitioners interested in latency optimization, real-time system performance, and computational task management. Contributions are welcome, but proper citation and attribution are required as per the LICENSE.