Initial commit: Heapsort and Priority Queue Implementation
- Implemented complete Heapsort algorithm with max-heap operations - Implemented binary heap-based Priority Queue with all core operations - Created Task class for task scheduling applications - Implemented Task Scheduler simulation using priority queue - Added comprehensive test suite (70+ tests, all passing) - Implemented sorting algorithm comparison utilities (Heapsort vs Quicksort vs Merge Sort) - Added detailed README with comprehensive analysis and documentation - Included demonstration scripts for all features - Generated performance comparison plots - Modular, well-documented code following academic standards Author: Carlos Gutierrez Email: cgutierrez44833@ucumberlands.edu
This commit is contained in:
Carlos Gutierrez
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examples/scheduler_simulation.py
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163
examples/scheduler_simulation.py
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#!/usr/bin/env python3
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"""
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Task Scheduler Simulation Demonstration
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This script demonstrates the task scheduler implementation using the priority queue.
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It shows various scheduling scenarios and analyzes the results.
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Author: Carlos Gutierrez
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Email: cgutierrez44833@ucumberlands.edu
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"""
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import sys
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import os
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# Add parent directory to path
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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from src.scheduler import TaskScheduler, simulate_scheduler
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from src.task import Task
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def demo_basic_scheduling():
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"""Demonstrate basic priority-based scheduling."""
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print("=" * 80)
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print("BASIC PRIORITY-BASED SCHEDULING")
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print("=" * 80)
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scheduler = TaskScheduler()
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# Create tasks with different priorities
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tasks = [
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Task("T1", priority=10, arrival_time=0.0, execution_time=5.0, description="Low priority task"),
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Task("T2", priority=30, arrival_time=0.0, execution_time=3.0, description="High priority task"),
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Task("T3", priority=20, arrival_time=0.0, execution_time=4.0, description="Medium priority task"),
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Task("T4", priority=15, arrival_time=0.0, execution_time=2.0, description="Medium-low priority task"),
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]
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print("\nTasks to schedule (in priority order):")
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for task in sorted(tasks, key=lambda t: t.priority, reverse=True):
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print(f" {task.task_id}: priority={task.priority}, execution_time={task.execution_time}")
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results = scheduler.schedule_tasks(tasks)
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scheduler.print_schedule(results)
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def demo_deadline_scheduling():
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"""Demonstrate scheduling with deadlines."""
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print("\n" + "=" * 80)
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print("SCHEDULING WITH DEADLINES")
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print("=" * 80)
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scheduler = TaskScheduler()
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# Create tasks with deadlines
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tasks = [
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Task("T1", priority=10, arrival_time=0.0, deadline=100.0, execution_time=5.0),
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Task("T2", priority=20, arrival_time=0.0, deadline=30.0, execution_time=3.0),
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Task("T3", priority=15, arrival_time=0.0, deadline=50.0, execution_time=10.0),
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Task("T4", priority=25, arrival_time=0.0, deadline=20.0, execution_time=2.0),
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Task("T5", priority=12, arrival_time=0.0, deadline=150.0, execution_time=7.0),
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]
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print("\nTasks with deadlines:")
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for task in tasks:
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print(f" {task.task_id}: priority={task.priority}, deadline={task.deadline}, "
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f"execution_time={task.execution_time}")
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results = scheduler.schedule_tasks(tasks)
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scheduler.print_schedule(results)
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def demo_large_workload():
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"""Demonstrate scheduling a large number of tasks."""
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print("\n" + "=" * 80)
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print("LARGE WORKLOAD SCHEDULING")
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print("=" * 80)
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import random
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# Generate random tasks
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num_tasks = 50
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tasks = []
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random.seed(42) # For reproducibility
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for i in range(num_tasks):
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priority = random.randint(1, 100)
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execution_time = random.uniform(0.5, 10.0)
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deadline = random.uniform(10.0, 200.0)
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tasks.append(
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Task(f"T{i+1}", priority=priority, arrival_time=0.0,
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deadline=deadline, execution_time=execution_time)
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)
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print(f"\nScheduling {num_tasks} tasks...")
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stats = simulate_scheduler(tasks, verbose=False)
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print(f"\nScheduling Statistics:")
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print(f" Total tasks: {stats.total_tasks}")
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print(f" Completed: {stats.completed_tasks}")
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print(f" Deadline met: {stats.deadline_met} ({stats.deadline_met/stats.total_tasks*100:.1f}%)")
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print(f" Deadline missed: {stats.deadline_missed} ({stats.deadline_missed/stats.total_tasks*100:.1f}%)")
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print(f" Total execution time: {stats.total_execution_time:.2f}")
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print(f" Average wait time: {stats.average_wait_time:.2f}")
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print(f" Throughput: {stats.throughput:.2f} tasks/time unit")
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def demo_priority_vs_deadline():
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"""Compare priority-based vs deadline-based scheduling."""
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print("\n" + "=" * 80)
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print("PRIORITY-BASED vs DEADLINE-AWARE SCHEDULING")
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print("=" * 80)
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# Create tasks where high priority tasks have tight deadlines
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tasks = [
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Task("T1", priority=30, arrival_time=0.0, deadline=15.0, execution_time=10.0),
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Task("T2", priority=20, arrival_time=0.0, deadline=50.0, execution_time=5.0),
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Task("T3", priority=10, arrival_time=0.0, deadline=100.0, execution_time=3.0),
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]
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print("\nScenario: High priority task (T1) has tight deadline")
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print("Tasks:")
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for task in tasks:
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print(f" {task.task_id}: priority={task.priority}, deadline={task.deadline}, "
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f"execution_time={task.execution_time}")
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# Priority-based scheduling
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scheduler = TaskScheduler()
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results = scheduler.schedule_tasks(tasks)
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print("\nPriority-based scheduling (highest priority first):")
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scheduler.print_schedule(results)
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# Note: This demonstrates that pure priority scheduling may miss deadlines
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# A more sophisticated scheduler could use deadline-aware priority adjustment
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def main():
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"""Run all scheduler demonstrations."""
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print("\n" + "=" * 80)
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print("TASK SCHEDULER SIMULATION DEMONSTRATION")
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print("Author: Carlos Gutierrez")
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print("Email: cgutierrez44833@ucumberlands.edu")
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print("=" * 80)
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demo_basic_scheduling()
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demo_deadline_scheduling()
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demo_large_workload()
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demo_priority_vs_deadline()
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print("\n" + "=" * 80)
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print("DEMONSTRATION COMPLETE")
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print("=" * 80)
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print("\nKey Observations:")
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print("1. Priority-based scheduling ensures high-priority tasks execute first")
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print("2. Pure priority scheduling may miss deadlines for lower-priority tasks")
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print("3. The scheduler efficiently handles large workloads using O(n log n) algorithm")
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print("4. Statistics provide insights into scheduling performance")
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print("=" * 80 + "\n")
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if __name__ == "__main__":
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main()
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