Network-Aware Container Scheduling in Multi-Tenant Data Center

TL;DR

This paper presents a network-aware container scheduling approach for multi-tenant data centers that optimizes network and container requirements using a novel MILP formulation and GPU acceleration, improving performance over traditional methods.

Contribution

It introduces a new multi-criteria scheduling framework that jointly considers network QoS and container requirements, with GPU-accelerated modules for efficiency.

Findings

Outperforms traditional container orchestrator algorithms.

Effectively balances network and container performance metrics.

Demonstrates scalability with GPU acceleration.

Abstract

Network management on multi-tenant container-based data centers has critical impact on performance. Tenants encapsulate applications in containers abstracting away details on hosting infrastructures, and entrust data centers management framework with the provisioning of network QoS requirements. In this paper, we propose a network-aware multi-criteria container scheduler to jointly process containers and network requirements. We introduce a new Mixed Integer Linear Programming formulation for network-aware scheduling encompassing both tenants and providers metrics. We describe two GPU-accelerated modules to address the complexity barrier of the problem and efficiently process scheduling requests. Our experiments show that our scheduling approach accounting for both network and containers outperforms traditional algorithms used by containers orchestrators.