Distributed Cloud InfrastructureNov 2022 – Dec 2023AWS Backend Developer & DevOps Engineer (V-Nova)

PaPeR (Parallel Performance Runner)

Distributed Video Compression Infrastructure

High-throughput distributed cloud infrastructure built on AWS with Terraform, collaborating with Meta, Intel, and SPIE to automate video compression quality evaluation across thousands of parallel transcoding jobs.

-99%Setup Time

1,800+Hours Saved

+50%Throughput

The Problem & Engineering Constraint

The Core Challenge

Benchmarking next-generation video compression codecs across multi-gigabyte video libraries required hours of manual AWS instance provisioning, shell script orchestration, and disparate metric aggregation.

Technical Architecture & Approach

Engineering Solution & Implementation

Engineered an automated cloud pipeline using Flask, AWS EC2, S3, RDS, Lambda, and modular Terraform infrastructure. Provided instant containerized test runners that automatically execute transcoding experiments, collect PSNR/VMAF metrics, and output standardized SPIE evaluation datasets.

DISTRIBUTED CLUSTER ARCHITECTURE: PaPeR

AWS TERRAFORM VIDEO RUNNER (META / SPIE / INTEL)

Figure 5: PaPeR's distributed AWS cloud architecture for automated parallel video quality benchmarking.

Measured Production Impact

Verified Outcomes & Deliverables

Achieved a 99% reduction in SPIE experimental setup times on AWS (from 3 hours down to seconds).

Saved over 1,800 engineering hours across 600+ large-scale distributed video experiments.

Boosted session processing throughput by 50% and slashed infrastructure provisioning time by 75%.

Accelerated integration of V-Nova video compression technologies into Meta services (WhatsApp, Instagram, XR platforms).

Technologies & Components

System Tooling & Technologies

AWS (EC2, S3, RDS, Lambda)TerraformPythonFlaskDockerMySQLFFmpegVMAF