Implement VM on Google Cloud Platform
Budget: $25 – $50 USD
VM Provisioning & Configuration
Recommend and deploy VM size based on workload needs (CPU, RAM, storage, cost).
Choose the optimal operating system (Ubuntu)
Set up networking: define VPC, subnets, firewall rules, and private/public IP configuration
Secure Connectivity
Implement OS Login for centralized SSH access via IAM, instead of managing SSH keys manually
Secure administrative access using IAP (Identity-Aware Proxy) for SSH tunneling, avoiding public exposure
Enable Shielded VMs for boot integrity and protection against firmware/boot-level threats
Apply firewall rules adhering to the principle of least privilege—only open needed ports and restrict by source IP/tags
Logging, Monitoring & Security
Enable VPC Flow Logs, Cloud Audit Logs, and Cloud Logging/Monitoring for visibility and anomaly detection
Performance Testing
Design realistic workloads and test both baseline and peak performance (CPU, memory, disk I/O, network latency)
Monitor and analyze key metrics: latency, error rates, request counts, backend response times, connection counts
Provide documentation of test methodology, results, and recommendations for scaling or optimization.
Recommend and deploy VM size based on workload needs (CPU, RAM, storage, cost).
Choose the optimal operating system (Ubuntu)
Set up networking: define VPC, subnets, firewall rules, and private/public IP configuration
Secure Connectivity
Implement OS Login for centralized SSH access via IAM, instead of managing SSH keys manually
Secure administrative access using IAP (Identity-Aware Proxy) for SSH tunneling, avoiding public exposure
Enable Shielded VMs for boot integrity and protection against firmware/boot-level threats
Apply firewall rules adhering to the principle of least privilege—only open needed ports and restrict by source IP/tags
Logging, Monitoring & Security
Enable VPC Flow Logs, Cloud Audit Logs, and Cloud Logging/Monitoring for visibility and anomaly detection
Performance Testing
Design realistic workloads and test both baseline and peak performance (CPU, memory, disk I/O, network latency)
Monitor and analyze key metrics: latency, error rates, request counts, backend response times, connection counts
Provide documentation of test methodology, results, and recommendations for scaling or optimization.