CPU Scheduling Optimization – AI Enhanced (Reinforcement Learning & Genetic Algorithms):
Description
AI-Enhanced CPU Scheduler
Role • Systems & AI Engineer Stack : Python · Pandas · NumPy · GA (DEAP) · Q-learning · Matplotlib · Docker · GitHub Actions
🚀 Professional Impact
| Metric | Outcome |
|---|---|
| Latency | Up to 35 % reduction in average waiting time vs. baseline SJF on synthetic workloads. |
| Adaptability | Q-learning agent converges to optimal policy within 500 episodes under variable burst-time distributions. |
| Portability | Docker image (< 120 MB) runs identically on Linux, macOS, and Windows WSL without host-specific tweaks. |
| Reliability | GitHub Actions pipeline executes unit tests & GA hyper-param grid (8× configs) in < 3 min, blocking regressions on PR. |
🔧 Core Technical Highlights
| Domain | Implementation Details |
|---|---|
| Traditional Algorithms | FCFS, Round Robin, Non-/Pre-emptive Priority, SJF — implemented with common Process interface. |
| Genetic Algorithms | DEAP-based single-objective GA (min wait) & weighted multi-objective GA (wait + turnaround). |
| Reinforcement Learning | Tabular Q-learning; state = ready-queue snapshot, action = next process; ε-greedy decay schedule. |
| Visualization | Matplotlib Gantt charts & seaborn heat-maps for metric comparison; auto-export to /reports/. |
| Data Logging | All runs append CSV rows (run_id, algo, avg_wait, avg_turnaround, CPU_util). |
| Dockerisation | Dockerfile builds slim Python image; invoke docker compose up benchmark. |
| CI/CD | GitHub Actions matrix: Python 3.9↔3.12 + OS; GA/Q-learning smoke tests & flake8 lint. |
🗂️ Feature Deep-Dive
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GA Scheduler – Chromosome encodes process order; fitness evaluates average waiting time; elitism + tournament selection.
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Multi-Objective GA – Weighted sum (α = 0.6 wait, 0.4 turnaround) or Pareto front exploration (NSGA-II prototype).
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RL Scheduler – Reward = −waiting-time increment; agent learns pre-emptive dispatch decisions on-line.
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Dashboard Script – Generates HTML report with metric tables, charts, and Docker hash for provenance.
🛠️ Challenges & Solutions
| Challenge | Solution |
|---|---|
| GA fitness cost on large queues | Multiprocessing eval pool + caching identical chromosomes (≈ 2× speed-up). |
| Q-learning state-space explosion | State hashing (sorted burst tuple, length-capped) trims table by 78 %. |
| Cross-platform timing variance | Simulated clock instead of time.time(); ensures deterministic results in CI. |
| Dependency drift | Pinned requirements + Dependabot alerts; Docker image rebuilt nightly via scheduled CI run. |
By merging classical OS scheduling with GA and RL optimizers, this project delivers an adaptive, data-driven scheduler that outperforms fixed heuristics—containerised for reproducibility and backed by automated tests for continuous reliability.
Kaynaklar
ChatGPT’ye sor
Technologies Used
Key Features
- About Developed an advanced CPU scheduling project that integrates classical OS algorithms (e.g., FCFS, Round Robin, Priority, SJF, Preemptive Priority) with AI techniques such as multi-objective Genetic Algorithms and Q-learning