The Serverless Scheduled Job Scheduler is an advanced cloud computing project that combines Cold start handling and Function handlers, built with DynamoDB. The project follows a cloud-native architecture where resources are provisioned, scaled, and managed as code, making it reliable, cost-effective, and easy to reproduce. It showcases professional cloud engineering techniques while delivering a complete, demo-ready platform.
Managing this workload on traditional infrastructure is slow, expensive, and hard to scale. Without a cloud platform built on Function handlers and DynamoDB, there is no automated, resilient, and pay-per-use way to run the service reliably for growing demand.
This project applies advanced cloud engineering practices through Cold start handling, orchestrated with DynamoDB and Function handlers. The system is designed for automation, observability, and cost control, with security and resilience built in. It delivers consistent, scalable results and can be extended to additional cloud services and regions.
DynamoDB
Terraform
AWS / Azure / GCP
Docker / Kubernetes
Terraform / IaC
Serverless services
Monitoring and CI/CD
AWS Lambda / Azure Functions
API Gateway
S3
Cloud-native system with Cold start handling and Function handlers
Automated provisioning and scaling on demand
Observability with metrics, logs, and alerts
Security controls, encryption, and access management
Reusable cloud modules for related features
Cost-aware, documented, maintainable cloud architecture
The system follows a cloud-native architecture: the compute layer runs Cold start handling; the orchestration and logic layer uses DynamoDB and Function handlers; and the data and storage layer persists state via Event triggers. Shared IaC, security, and observability modules support all layers, keeping the platform automated, resilient, and easy to extend.
Set up cloud account, project, and infrastructure as code.
Provision core resources and networking for Cold start handling.
Implement the workload logic using DynamoDB and Function handlers.
Add the data and storage layer via Event triggers.
Wire up automation, monitoring, and cost controls.
Test scaling, failures, and security, then refine.
Document the architecture, run a demo, and prepare the viva report.
Architect cloud-native, scalable systems
Apply Serverless cost optimization and Designing serverless functions
Provision infrastructure with automation
Secure, monitor, and optimize cloud workloads
Present and defend a complete cloud project in viva
Add multi-cloud and hybrid deployment
Introduce advanced AI and analytics on the platform
Add disaster recovery and global replication
Optimize cost with predictive autoscaling
The Serverless Scheduled Job Scheduler delivers a complete, production-grade cloud platform — from automated infrastructure to secure, scalable services and observability. It is practical, cost-effective, and easy to explain, making it an excellent final year project that demonstrates advanced cloud computing skills.
