🧠 RL for Autoscaling

DRe-SCale: Intelligent Serverless Autoscaling

Revolutionary deep recurrent reinforcement learning method for intelligent autoscaling of serverless functions. Improve throughput by 18% and function execution by 13% through advanced AI-powered scaling.

Why Choose DRe-SCale?

Transform serverless autoscaling from threshold-based guessing to AI-powered intelligent optimization.

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Deep Recurrent RL

Advanced LSTM-PPO integration for capturing temporal dependencies and environment dynamics in complex cloud environments.

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Proven Performance

Achieve 18% throughput improvement, 13% function execution enhancement, and 8.4% more function instances.

Intelligent Adaptation

Automatically adapts to fluctuating workloads and performance constraints without manual threshold configuration.

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POMDP Modeling

Handles partial observability in dynamic cloud environments through sophisticated decision process modeling.

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OpenFaaS Native

Built and tested on OpenFaaS with MicroK8s deployment for production-ready serverless environments.

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Research-Driven

Published in IEEE Transactions on Services Computing with comprehensive experimental validation.

🏗️ LSTM-PPO Architecture

Advanced neural network architecture combining recurrent units with reinforcement learning for intelligent autoscaling decisions.

Core Components

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LSTM Integration

Long-Short Term Memory networks for temporal dependency capture

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Actor-Critic Networks

Dual network architecture with policy and value function learning

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PPO Algorithm

Proximal Policy Optimization with clipped surrogate objectives

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Recurrent Policies

Hidden state maintenance for environment memory and adaptation

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LSTM-PPO Architecture
🧠 LSTM Layer
🎭 Actor Network
🎯 Critic Network
⚡ PPO Updates

📊 Superior Performance Results

DRe-SCale outperforms traditional threshold-based autoscaling across all key metrics.

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Throughput Improvement

Achieve 18% better throughput compared to traditional threshold-based autoscaling approaches.

18% Increase Real-time

Function Execution

Improve function execution performance by 13% through intelligent resource allocation.

13% Enhancement Efficient
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Instance Management

Manage 8.4% more function instances with optimal resource utilization.

8.4% More Optimized

🛠️ Technical Implementation

Built on cutting-edge reinforcement learning and deployed on production-ready cloud infrastructure.

Core Technologies

🤖 Deep Recurrent Reinforcement Learning
🧠 LSTM Networks with PPO Algorithm
🐳 OpenFaaS on MicroK8s (v1.27.2)
☁️ NeCTAR Melbourne Research Cloud
📊 Prometheus Monitoring & Metrics

Advanced Features

🎯 POMDP environment modeling
🔄 Recurrent policy networks
⚖️ PPO clipped surrogate objectives
📈 Real-time autoscaling decisions
💾 Multi-node cluster deployment

🔬 Research Impact & Recognition

DRe-SCale represents breakthrough research in intelligent serverless autoscaling and reinforcement learning.

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Published Research

Published in IEEE Transactions on Services Computing, a premier journal in cloud computing and services.

Journal: IEEE Transactions on Services Computing

Year: 2024

DOI: 10.1109/TSC.2024.3387661

Pages: 1-12

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Key Contributions

Novel integration of recurrent units with reinforcement learning for serverless autoscaling.

LSTM-PPO integration
POMDP environment modeling
Production deployment
Comprehensive evaluation

Ready to Revolutionize Your Serverless Autoscaling?

Join the future of intelligent serverless management with DRe-SCale. Experience unprecedented performance improvements through deep recurrent reinforcement learning.