Siddharth Agarwal - PhD Researcher in Cloud Computing & Distributed Systems

🔬 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

📄 Published Papers (2021-2024)

"A Deep Recurrent-Reinforcement Learning Method for Intelligent Autoscaling of Serverless Functions"

IEEE TSC 2024

"A Reinforcement Learning Approach to Reduce Serverless Function Cold Start Frequency"

CCGrid 2021 - Best Paper Award (Runner-Up)

"On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing"

ICCIDA 2024

"Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless Computing Functions"

UCC 2024

🔬 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

Python

Kubernetes

📊

Interactive

Serv-Drishti

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

JavaScript

Chart.js

Docker

🧠

RL-Based

DRe-SCale

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

Python

OpenFaaS

LSTM

🧮

Cost-Optimized

MemFigLess

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

AWS Lambda

Python

DynamoDB

📚

Educational

OpenFaaS Tutorial

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

Go

Kubernetes

Docker

🤖

Serverless RL

FaaSTrainGym

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

OpenFaaS

OpenAI Gym

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, Java, AWS, OpenStack, Kubernetes, 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, SQL, Java, 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

OpenStack

Kubernetes

Platforms:

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

💻 Programming & Development

Languages:

Java

Python

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

🏗️ Serverless Function Scaling Explained

Published: August 24, 2025

Topics: Architecture, Scalability, Best Practices

Read Time: 5 min read

🏗️ More on Serverless Computing

Published: Coming Soon

Topics: Serverless, Cloud Computing, Research

Read Time: Coming Soon

🎯 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

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