APPLICATION OF ANT COLONY OPTIMIZATION FOR SCHEDULING CONTAINER DEPLOYMENTS IN CLOUD NATIVE CI/CD PIPELINES

Osamu Shikibu Keigo

APPLICATION OF ANT COLONY OPTIMIZATION FOR SCHEDULING CONTAINER DEPLOYMENTS IN CLOUD NATIVE CI/CD PIPELINES

Authors

Osamu Shikibu Keigo

Cloud-Native Optimization Engineer, Indonesia.

Keywords:

Ant Colony Optimization, Container Orchestration, CI/CD Pipelines, Cloud-native Deployment, Metaheuristics, Scheduling Algorithms, Kubernetes Optimization, Software Delivery Automation, DevOps, Deployment Latency

Synopsis

The evolution of software delivery methodologies, particularly with the adoption of cloud-native continuous integration and continuous deployment (CI/CD) pipelines, has led to complex challenges in resource allocation and scheduling of containerized workloads. Traditional heuristic methods often fall short in addressing the dynamic and distributed nature of such environments. This paper proposes the use of Ant Colony Optimization (ACO) as a metaheuristic approach to optimize the scheduling of container deployments in cloud-native CI/CD pipelines. By mimicking the foraging behavior of ants, ACO adapts to changing system states and ensures efficient resource utilization and reduced deployment latency. Simulation-based experiments demonstrate the viability of the approach in handling high-volume, multi-tenant CI/CD tasks across distributed cloud infrastructure.

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