Lifting the Fog of Uncertainties: Dynamic Resource Orchestration for the Containerized Cloud

TL;DR

This paper introduces Drone, a resource orchestration framework for containerized cloud environments that adaptively manages resources using contextual bandit techniques, improving performance and reducing costs amid uncertainties.

Contribution

The paper presents Drone, a novel adaptive resource orchestration framework leveraging contextual bandits to handle uncertainties in containerized cloud environments.

Findings

Up to 45% performance improvement

20% reduction in resource footprint

Achieves sub-linear growth in cumulative regret

Abstract

The advances in virtualization technologies have sparked a growing transition from virtual machine (VM)-based to container-based infrastructure for cloud computing. From the resource orchestration perspective, containers' lightweight and highly configurable nature not only enables opportunities for more optimized strategies, but also poses greater challenges due to additional uncertainties and a larger configuration parameter search space. Towards this end, we propose Drone, a resource orchestration framework that adaptively configures resource parameters to improve application performance and reduce operational cost in the presence of cloud uncertainties. Built on Contextual Bandit techniques, Drone is able to achieve a balance between performance and resource cost on public clouds, and optimize performance on private clouds where a hard resource constraint is present. We show that our algorithms can achieve sub-linear growth in cumulative regret, a theoretically sound convergence guarantee, and our extensive experiments show that Drone achieves an up to 45% performance improvement and a 20% resource footprint reduction across batch processing jobs and microservice workloads.