Siddharth Agarwal

PhD Researcher in Cloud Computing & Distributed Systems

I research cloud computing and distributed systems, focusing on serverless computing optimization, cold start reduction, and intelligent autoscaling. My work spans from fundamental research to practical implementations in cloud platforms.

🔬 Research Focus

☁️ Cloud Computing

Core Research Areas

Research Areas:

  • 🚀 Serverless computing optimization
  • ❄️ Cold start frequency reduction
  • 📊 Intelligent autoscaling algorithms
  • 🧠 Reinforcement learning for cloud systems
  • 🔧 Dynamic function configuration

Applications:

  • ☁️ Serverless platforms (AWS Lambda, OpenFaaS)
  • 🔧 Cloud resource management
  • ⚡ Performance optimization
  • 🔄 Distributed systems

🤖 Machine Learning & AI

Advanced Techniques

ML Techniques:

  • 🧠 Deep recurrent-reinforcement learning
  • 🔄 Off-policy reinforcement learning
  • 📊 Ensemble learning methods
  • ⚡ Dynamic memory configuration
  • 🎯 Input-aware function scheduling

Specializations:

  • Cloud performance optimization
  • Resource allocation
  • Intelligent automation
  • Predictive scaling

📚 Publications & Research

🔬 Research Projects

Current & Completed:

  • 🚀 Serverless Optimization Research (2021-Present)
  • ❄️ Cold Start Reduction using RL (2021-2022)
  • 📊 Intelligent Autoscaling Algorithms (2022-2023)
  • 🧠 Dynamic Memory Configuration (2023-2024)
  • 🎯 Input-aware Function Scheduling (2024-Present)

Collaborations:

  • 🏢 Telstra Corporation Ltd. (Vocational Placement)
  • 🎓 University of Melbourne - Cloud Computing and Distributed Systems Lab
  • 🌍 International research community

🚀 Research Projects & Tools

🧠
AI-Powered

Saarthi

Intelligent serverless computing platform with AI-powered workload prediction, mathematical optimization, and intelligent resource management.

OpenFaaS OpenFaaS Python Python Kubernetes Kubernetes
📊
Interactive

Serv-Drishti

Interactive visualization tool for serverless computing internals, request routing, resource management, and performance analytics.

JavaScript JavaScript Chart.js Chart.js Docker Docker
🧠
RL-Based

DRe-SCale

Deep recurrent reinforcement learning method for intelligent autoscaling of serverless functions with LSTM-PPO integration.

Python Python OpenFaaS OpenFaaS LSTM LSTM
🧮
Cost-Optimized

MemFigLess

Input-aware memory configuration for serverless functions using ensemble learning, reducing costs by up to 87%.

AWS Lambda AWS Lambda Python Python DynamoDB DynamoDB
📚
Educational

OpenFaaS Tutorial

Comprehensive 8-chapter tutorial on OpenFaaS internals, from gateway to metrics, with practical code examples.

Go Go Kubernetes Kubernetes Docker Docker
🤖
Serverless RL

FaaSTrainGym

Serverless reinforcement learning training platform combining OpenFaaS with OpenAI Gym for scalable RL pipelines.

OpenFaaS OpenFaaS OpenAI Gym OpenAI Gym Python Python

💼 Professional Experience

🔬 PhD Researcher | University of Melbourne | 2021 - Present

Role: Doctoral Candidate in Cloud Computing

Current Research:

  • 🚀 Serverless computing optimization
  • ❄️ Cold start frequency reduction using RL
  • 📊 Intelligent autoscaling algorithms
  • 🧠 Deep learning for cloud resource management
  • 🔧 Dynamic function configuration

Technologies: Python Python, Java Java, AWS AWS, OpenStack OpenStack, Kubernetes Kubernetes, OpenFaaS OpenFaaS

🏢 Associate System Engineer | IBM India Pvt. Ltd. | 2018 - 2019

Role: CMS Application Development

Key Achievements:

  • 🛠️ Development and management of Archer-CMS applications
  • 🔍 Troubleshooting Archer-CMS applications and SQL databases
  • 👥 Supervised internal team meetings and client presentations
  • 🎓 Trained new resources and conducted knowledge transfer
  • 📚 Created application documentation and led client meetings

Technologies: Archer-CMS Archer-CMS, SQL SQL, Java Java, Application Automation Application Automation

🎓 Education

🎯 PhD in Engineering & IT

University of Melbourne | 2021 - Present

Research Focus: Cloud Computing, Distributed Systems

Thesis: Cloud Computing Optimization and Serverless Computing

Key Achievements:

  • 🏆 Melbourne Research Scholarship recipient
  • 📚 Multiple publications in top-tier conferences
  • 🌟 Best Paper Award (Runner-Up) at CCGrid 2021

🎓 Master of Science (Computer Science)

University of Melbourne | 2021

GPA: 89.85 WAM (H1 Grade)

Achievement: Dean's Honour's List Award

Focus: Advanced Computer Science and Cloud Computing

🏆 Bachelor of Technology (Honours)

Jaypee Institute of Information Technology | 2017

Major: Computer Science and Engineering

GPA: 8.5/10

Honors: Computer Science and Engineering with Honours

👨‍🏫 Teaching Experience

👨‍🏫 Head Tutor | University of Melbourne | 2023 - Present

Courses:

  • 🔄 Distributed Systems (COMP90015)
  • 🧮 Distributed Algorithms (COMP90020)

Responsibilities:

  • 📚 Course material development and delivery
  • 🎯 Leading tutorial sessions
  • 📊 Student assessment and feedback
  • 🤝 Academic support and mentoring

👨‍🏫 Sessional Tutor | University of Melbourne | 2023 - 2024

Courses Taught:

  • ☁️ Cluster and Cloud Computing (COMP90024)
  • 🗄️ Advanced Database Systems (COMP90050)
  • 🤖 Statistical Machine Learning (COMP90051)
  • 🧮 Design of Algorithms (COMP20007)
  • 📊 Database Systems (INFO20003)

Total Teaching Experience: 2+ years at postgraduate levels

🛠️ Skills & Technologies

☁️ Cloud & Serverless

AWS AWS OpenStack OpenStack Kubernetes Kubernetes

Platforms:

  • 🚀 OpenFaaS & Kubeless
  • ☁️ Melbourne Research Cloud
  • 🔧 Serverless computing platforms
  • 🐳 Container orchestration

💻 Programming & Development

Languages:

Java Java Python Python SQL SQL

Tools:

  • 🛠️ Eclipse & VS Code
  • 🔧 Ansible (DevOps)
  • 📊 Development frameworks
  • 🎯 Vibe-Coding

🔬 Research & ML

Machine Learning:

  • 🧠 Reinforcement Learning
  • 🔄 Deep Recurrent Networks
  • 📊 Ensemble Learning
  • 🎯 Off-policy RL

Areas:

  • Cloud performance optimization
  • Resource allocation
  • Predictive scaling
  • System automation

📚 Recent Blog Posts

🚀 Mastering AWS Lambda Cold Starts: Strategies for Peak Serverless Performance

Published: August 24, 2025

Topics: Performance, Optimization, AWS Lambda

Read Time: 13 min read

Read More →

🏗️ Serverless Function Scaling Explained

Published: August 24, 2025

Topics: Architecture, Scalability, Best Practices

Read Time: 5 min read

Read More →

🏗️ More on Serverless Computing

Published: Coming Soon

Topics: Serverless, Cloud Computing, Research

Read Time: Coming Soon

Read More →

🎯 Current Research Areas

☁️ Cloud Computing

Serverless Optimization

  • 🚀 Cold start reduction
  • 📊 Intelligent autoscaling
  • 🔧 Dynamic configuration
  • 🎯 Performance optimization

🤖 Machine Learning

Reinforcement Learning

  • 🧠 Deep recurrent networks
  • 🔄 Off-policy algorithms
  • 📊 Ensemble methods
  • ⚡ Real-time optimization

🏗️ Distributed Systems

System Architecture

  • 🔄 Distributed algorithms
  • 📊 Resource management
  • 🚀 Scalable systems
  • 🔧 System optimization

🌟 Open Source

Community Contribution

  • 🛠️ Cloud computing tools
  • 📦 Serverless frameworks
  • 🔧 Research utilities
  • 📚 Educational resources

🌐 Connect With Me

📧 Stay Updated with My Research

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