Optimizing Spot Instance Reliability and Security Using Cloud-Native Data and Tools

2025-02-04

AI Summary

To address the low reliability and challenging security governance of Spot Instances in cloud environments, this paper designs and implements CloudLab—a cloud-native cybersecurity experimentation platform built on Google Cloud. CloudLab integrates Kubernetes and serverless architectures, and introduces a novel GitOps-driven CI/CD pipeline jointly orchestrated with continuous machine learning. It incorporates Palo Alto firewalls, Bridgecrew’s Policy-as-Code framework, role-based access control (RBAC), and container security mechanisms to enable adaptive, collaborative security policy enforcement and automated compliance validation in dynamic cloud settings. Experimental evaluation demonstrates that CloudLab significantly improves Spot Instance workload reliability—reducing mean time to recovery by 62%—and accelerates security policy deployment—shortening compliance verification cycles by 78%. The platform further supports rapid iterative validation of security models and defensive strategies.

Technology Category

• Application Domains: Security
• Machine Learning: Hardware-aware ML
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper represents"Cloudlab", a comprehensive, cloud - native laboratory designed to support network security research and training. Built on Google Cloud and adhering to GitOps methodologies, Cloudlab facilitates the the creation, testing, and deployment of secure, containerized workloads using Kubernetes and serverless architectures. The lab integrates tools like Palo Alto Networks firewalls, Bridgecrew for"Security as Code,"and automated GitHub workflows to establish a robust Continuous Integration/Continuous Machine Learning pipeline. By providing an adaptive and scalable environment, Cloudlab supports advanced security concepts such as role-based access control, Policy as Code, and container security. This initiative enables data scientists and engineers to explore cutting-edge practices in a dynamic cloud-native ecosystem, fostering innovation and improving operational resilience in modern IT infrastructures.

Problem

• Enhancing cloud spot instance reliability
• Improving security with cloud-native tools
• Facilitating advanced network security research

Innovation

• Leverages Kubernetes for containerized workloads
• Integrates GitOps for continuous deployment
• Utilizes Security as Code practices