Dr. Siddharth Agarwal — Cloud Computing Researcher & Engineer

Research

Contributions

Three interlocking problems in serverless resource management — each investigated empirically and validated against industry baselines.

Cold Start Frequency Reduction

RL

Static keep-alive policies cannot adapt to on-demand invocation patterns, causing unavoidable cold starts that spike tail latency.

Both on-policy (PPO) and off-policy (Q-learning) RL policies investigated
Validated on AWS Lambda & OpenFaaS under diverse workload traces
Significantly reduced cold start events vs. keep-warm & reactive baselines

CCGrid 2021 ★ ICCIDA 2024

Input-Aware Memory Configuration

ML

Fixed memory limits are chosen conservatively, systematically over-provisioning compute and inflating cost regardless of actual function workload.

Multi-output Random Forest trained on function input characteristics
57–87% cost reduction vs. static allocation baselines
R² up to 98% for execution time · 96% for memory prediction

UCC 2024

Adaptive Autoscaling under Workload Uncertainty

RPPO

Threshold-based HPA cannot anticipate bursty or time-varying workloads, leading to under-scaling, SLA violations, and wasted idle replicas.

Recurrent PPO (RPPO) proposes on-policy scaling — outperforms DRQN baseline
22% throughput improvement vs. standard Kubernetes HPA
37% reduction in resource wastage across heterogeneous workloads

IEEE TSC 2024

Work

Publications

Google Scholar

Published · 5 papers

IEEE TSC 2024 Journal

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

CCGrid 2021 Conference

A Reinforcement Learning Approach to Reduce Serverless Function Cold Start Frequency ★ Best Paper (Runner-Up)

ICCIDA 2024 Conference

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

UCC 2024 Conference

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

ITNAC 2025 Conference

Serv-Drishti: An Interactive Serverless Function Request Simulation Engine and Visualiser

Under Review · 2 papers

ICDCS 2026 Conference

Saarthi: An End-to-End Intelligent Platform for Optimising Distributed Serverless Workloads

ACM Surveys 2026 Journal

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions

Software

Projects & Tools

Saarthi AI-Powered

Intelligent serverless platform with workload prediction, mathematical optimisation, and automated resource management. Achieves 1.84× cost reduction and 97.2% SLA compliance benchmarked against AWS Power Tuning and standard HPA.

OpenFaaS

Python

Kubernetes

Live Demo

Serv-Drishti Interactive

Interactive visualisation engine for serverless computing internals — request routing, resource management, and performance analytics in real time.

JavaScript

Chart.js

Docker

Live Demo

DRe-SCale RL-Based

Proposes Recurrent PPO (RPPO) for intelligent serverless autoscaling. Outperforms DRQN and standard HPA — achieving 22% throughput improvement and 37% resource wastage reduction. Published at IEEE TSC 2024.

Python

Kubernetes PyTorch

PyTorch

GitHub

MemFigLess Cost-Optimised

Input-aware dynamic memory configuration for serverless functions using Random Forest ensemble learning. Achieves 57–87% cost savings and 54–82% memory allocation reduction vs. static baselines on AWS Lambda.

AWS Lambda Python

Python

DynamoDB

GitHub

OpenFaaS Tutorial Educational

Comprehensive 8-chapter deep dive into OpenFaaS internals: gateway architecture, watchdog, metrics, and practical implementations with code examples.

Go

Kubernetes Docker

Docker

GitHub

FaaSTrainGym Serverless RL

Serverless reinforcement learning training platform integrating OpenFaaS with OpenAI Gym for scalable, distributed RL experiment pipelines.

OpenFaaS

Python

PyTorch

GitHub

Career

Experience

Sep 2021 — Feb 2026

Graduate Research Fellow (PhD)

qCLOUDS Laboratory · University of Melbourne

Led 4+ end-to-end research projects. Designed Kubernetes clusters (5–20 nodes) on Melbourne Research Cloud (NeCTAR) and AWS. Built Saarthi, MemFigLess, DRe-SCale, and Serv-Drishti. Supervised by Prof. Rajkumar Buyya & Dr. Maria Read.

Python

AWS

Kubernetes

OpenStack

Mar 2023 — Present

Sessional Academic — Head Tutor

University of Melbourne

Leading postgraduate tutorials for Distributed Systems (COMP90015, 100+ students/semester) and Distributed Algorithms (COMP90020). Also tutored Cluster & Cloud Computing, Advanced Database Systems, and Statistical Machine Learning.

Mar 2026 — Present

Sessional Academic

Monash University & RMIT University

Postgraduate cloud architecture, distributed systems, and cloud security tutorials at Monash. Weekly algorithm tutorials (25–30 students), assignment feedback, and curriculum collaboration at RMIT.

Aug 2020 — Jun 2021

Research Intern (Vocational Placement)

Telstra / University of Melbourne

Deployed serverless environments on Melbourne Research Cloud; investigated cold-start optimisation; delivered architectural recommendations to Telstra engineering stakeholders.

Apr 2018 — Jun 2019

Associate Systems Engineer

IBM India, Bengaluru

Developed and managed Archer CMS enterprise applications and SQL databases; led automation initiatives; client presentations and team knowledge transfer.