Microtubule-Inspired Quantum Adaptive Scheduling Algorithm Inspiration Operating system (OS) schedulers are the backbone of computing, yet classical approaches (FCFS, Priority, Round Robin) struggle when workloads are dynamic and unpredictable. We drew inspiration from microtubules in the human brain, which self-organize, adapt probabilistically, and sustain cognitive stability. Just as microtubules balance randomness with structure, we wanted to design a scheduler that uses probability and adaptiveness rather than rigid deterministic rules. What it does Simulates and compares four CPU scheduling algorithms: First Come First Serve (FCFS) Shortest Job First (SJF) Round Robin (RR) Quantum-Inspired Microtubule Adaptive Scheduler (our algorithm) Our algorithm introduces a probability-based decision layer that mimics microtubule dynamics: tasks are chosen not strictly by priority but by adaptive probability, giving short jobs a better chance without starving long jobs. Provides visual comparisons (Gantt charts, waiting/turnaround times, throughput, CPU utilization). How we built it Implemented simulation in Python with 20 randomized processes (arrival time, burst time, priority). Designed probabilistic selection inspired by quantum superposition, where multiple tasks "co-exist" in potential execution states until one is probabilistically chosen. Compared metrics across classical and our quantum-inspired algorithm to evaluate adaptability. Challenges we ran into Defining a mathematically valid probabilistic model that is both realistic and efficient. Preventing starvation (long jobs never getting executed). Visualizing scheduling behavior clearly for faculty and evaluators. Accomplishments that we're proud of Successfully created a novel scheduler beyond classical approaches. Achieved balanced results: reduced waiting time variance, better throughput consistency. Established a bridge between neuroscience (microtubules) and computer science (OS scheduling). What we learned OS scheduling is not just about raw efficiency; adaptiveness matters in dynamic workloads. Probabilistic models can outperform deterministic ones when environments are uncertain and fluctuating. Cross-domain inspiration (biology → computing) opens up fresh perspectives for system design. What's next for Microtubule-Inspired Quantum Adaptive Scheduling Algorithm Extending the algorithm to multi-core CPU environments. Exploring real-world workload traces (not just simulated processes). Adding machine learning integration, where the scheduler learns optimal probability distributions over time. Potential integration into modern OS kernels as an experimental scheduler module.
Microtubules Inspired Quantum Adaptive Scheduling Algorithm
A bio inspired quantum adaptive CPU scheduling algorithm that merges microtubule self-organization with probabilistic superposition, ensuring fairness, adaptability and optimal system performance.
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