SAGE -- A Tool for Optimal Deployments in Kubernetes Clusters

TL;DR

SAGE is a tool designed to optimize application deployment in Kubernetes clusters by providing optimal placement plans, improving over default schedulers through comprehensive analysis of application demands and cluster resources.

Contribution

The paper introduces SAGE, a novel tool that enhances Kubernetes scheduling by computing optimal deployment plans considering application constraints and cloud resources.

Findings

SAGE outperforms default and other schedulers in various test scenarios.

SAGE provides consistently optimal deployment solutions.

The tool is publicly available for integration and testing.

Abstract

Cloud computing has brought a fundamental transformation in how organizations operate their applications, enabling them to achieve affordable high availability of services. Kubernetes has emerged as the preferred choice for container orchestration and service management across many Cloud computing platforms. The scheduler in Kubernetes plays a crucial role in determining the placement of newly deployed service containers. However, the default scheduler, while fast, often lacks optimization, leading to inefficient service placement or even deployment failures. This paper introduces SAGE, a tool for optimal solutions in Kubernetes clusters that can also assist the Kubernetes default scheduler and any other custom scheduler in application deployment. SAGE computes an optimal deployment plan based on the constraints of the application to be deployed and the available Cloud resources. We show the potential benefits of using SAGE by considering test cases with various characteristics. It turns out that SAGE surpasses other schedulers by comprehensively analyzing the application demand and cluster image. This ability allows it to better understand the needs of the pods, resulting in consistently optimal solutions across all scenarios. The accompanying material of this paper is publicly available at https://github.com/SAGE-Project/SAGE-Predeployer.