vance 0.1.0

A Python CPU scheduling simulation package

vance

A unified Python module of CPU schedulers for educators and students, and for educational and simulation purposes.

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How to use?

Here's an example implementation of SJF scheduling:

# Sample implementation using the SJF scheduler
from aevum import Process, SimulationEngine, SJF, Visualizer

p = [
Process(pid=1, burst_time=5, arrival_time=0),
Process(pid=2, burst_time=2,  arrival_time=2),
Process(pid=3, burst_time=6,  arrival_time=5),
Process(pid=4, burst_time=6,  arrival_time=5),
Process(pid=5, burst_time=2,  arrival_time=10)
]

dispatch_latency = 3 # Optional, defaults to 0

# Run simulation, get results
engine = SimulationEngine(SJF(), dispatch_latency)
res = engine.run(p)

# To visualize your output
v = Visualizer()
v.render_gantt(res)
v.display_summary(res)
v.display_audit(res)

Creating a Custom Scheduler:

If you wish to create a custom scheduler, you can follow the blueprint below:

from aevum.policies import SchedulerPolicy

class MyCustomPolicy(SchedulerPolicy):
    def get_next_process(self, ready_queue, current_process, current_runtime, remaining_times):
        """
        Args:
            ready_queue: A list of Process objects currently waiting for the CPU.
            current_process: The process currently occupying the CPU (if any).
            current_runtime: How many ticks the current process has been running without interruption (for RR quantums).
            remaining_times: A dictionary {pid: int} tracking exactly how much burst time is left for every process in the system. """

        pass

        # We recommend prefixing parameters with '_' if they are not used.
        # Here's an example implementation of FCFS and STCF

        # FCFS
        if current_process:
            return current_process
        if ready_queue:
            return ready_queue.pop(0)
        return None

        # STCF
        if not ready_queue and not current_process:
            return None
        best_in_queue = None
        if ready_queue:
            best_in_queue = min(ready_queue, key=lambda p: (remaining_times[p.pid], p.pid))
        if current_process:
            current_rem = remaining_times[current_process.pid]
            if best_in_queue and remaining_times[best_in_queue.pid] < current_rem:
                ready_queue.append(current_process)
                ready_queue.remove(best_in_queue)
                return best_in_queue
            return current_process
        ready_queue.remove(best_in_queue)
        return best_in_queue

Accessing Raw Telemetry

If you want to perform custom analysis, you can access the data directly from the simulation results:

# Simulation code here
...
# Accessing results for the first process
p1 = res["individual_results"][0]
print(f"P{p1['pid']} finished at {p1['completion']} with a TAT of {p1['turnaround']}")


avg = res["averages"]
print(f"System Efficiency: {avg['hardware_efficiency']}") # Work vs. Context Switch Overhead
print(f"CPU Utilization: {avg['cpu_utilization']}")      # Busy Time vs. Total Time

# Extract only context switch events
traces = res["structured_trace"]
for trace in traces:
    print(f"Time {trace.time}: {trace.event_type} for P{trace.pid}")

# The total CPU clock time that has been used
total_time = res["total_time"]

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Initial TODO lists

Supported:

• First Count First Serve (FCFS)
• Shortest Job First (SJF)
• Shortest Time to Completion (STCF)
• Round Robin (RR)
• Dispatcher Feature
• Visualizer Module

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Why do this?

During my OS class, we are tasked to perform simulations of CPU scheduling algorithms in Python, and since there are no Python modules (as far as I know) for schedulers, I had to scour through the internet to look for sample implementation, and somehow refactor every algorithm that I need to fit my use case.

Because of this, it took me a few hours to perform the simulation. It could've been far more faster if there was a module, and our teacher could've provide a demo as well.

So I took the initiative in starting this project, and thought of it as my first ever open source project to give back to the community.

This project will also be a platform for me (I hope it does for you too), to practice my coding skills and strengthen our OS scheduling knowledge.