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.
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.
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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% IncreaseReal-time
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Function Execution
Improve function execution performance by 13% through intelligent resource allocation.
13% EnhancementEfficient
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Instance Management
Manage 8.4% more function instances with optimal resource utilization.
8.4% MoreOptimized
🛠️ 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.