Title: Adaptive Scheduling: Deep Reinforcement Learning for Dynamic Resource Management

Authors: Chen Liu, Zhang Yifei, Wang Haoran

Conference: AAAI 2024 (Association for the Advancement of Artificial Intelligence)

Abstract: We present an adaptive scheduling framework that uses deep reinforcement learning to optimize resource allocation in dynamic environments. Our system learns from historical patterns and adapts to changing workloads in real-time.

Key Contributions:

• Novel DRL architecture for adaptive scheduling
• Real-time workload prediction model
• 25% reduction in resource waste
• Scalable deployment on cloud platforms

System Architecture

Our adaptive scheduling system consists of:

• Workload Predictor: LSTM-based neural network for demand forecasting
• Decision Engine: Deep Q-Network for scheduling decisions
• Resource Monitor: Real-time tracking of system resources
• Feedback Loop: Continuous learning from scheduling outcomes

Benchmark Results

Real-world Deployment

We deployed our system in three different environments:

1. Cloud Computing: AWS EC2 instances with varying workloads
2. Edge Computing: IoT sensor networks with limited resources
3. HPC Clusters: Scientific computing workloads with strict deadlines

In all cases, our adaptive scheduler showed significant improvements over baseline methods.

Live System Demonstration

Experience our adaptive scheduling system in action:

• Real-time Dashboard: Monitor system performance and resource allocation
• Workload Simulation: Generate different workload patterns to test adaptability
• Performance Comparison: Compare against traditional scheduling algorithms
• Parameter Tuning: Adjust system parameters and observe the impact

Launch Interactive Demo →

API Integration

Integrate our scheduling engine into your systems:

from adaptive_scheduler import SchedulingEngine

# Initialize the scheduler
scheduler = SchedulingEngine(
    prediction_window=300,  # 5 minutes
    update_frequency=60     # 1 minute
)

# Submit a job for scheduling
job = scheduler.submit_job(
    resources={'cpu': 4, 'memory': '8GB'},
    deadline=3600,  # 1 hour
    priority='high'
)

# Get scheduling recommendation
allocation = scheduler.get_allocation(job.id)