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Resource Aware GPU Scheduling in Kubernetes Infrastructure

Authors Aggelos Ferikoglou, Dimosthenis Masouros , Achilleas Tzenetopoulos, Sotirios Xydis , Dimitrios Soudris



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Author Details

Aggelos Ferikoglou
  • Microprocessors and Digital Systems Laboratory, ECE, National Technical University of Athens, Greece
Dimosthenis Masouros
  • Microprocessors and Digital Systems Laboratory, ECE, National Technical University of Athens, Greece
Achilleas Tzenetopoulos
  • Microprocessors and Digital Systems Laboratory, ECE, National Technical University of Athens, Greece
Sotirios Xydis
  • Department of Informatics and Telematics, DIT, Harokopio University of Athens, Greece
Dimitrios Soudris
  • Microprocessors and Digital Systems Laboratory, ECE, National Technical University of Athens, Greece

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Aggelos Ferikoglou, Dimosthenis Masouros, Achilleas Tzenetopoulos, Sotirios Xydis, and Dimitrios Soudris. Resource Aware GPU Scheduling in Kubernetes Infrastructure. In 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021). Open Access Series in Informatics (OASIcs), Volume 88, pp. 4:1-4:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.PARMA-DITAM.2021.4

Abstract

Nowadays, there is an ever-increasing number of artificial intelligence inference workloads pushed and executed on the cloud. To effectively serve and manage the computational demands, data center operators have provisioned their infrastructures with accelerators. Specifically for GPUs, support for efficient management lacks, as state-of-the-art schedulers and orchestrators, threat GPUs only as typical compute resources ignoring their unique characteristics and application properties. This phenomenon combined with the GPU over-provisioning problem leads to severe resource under-utilization. Even though prior work has addressed this problem by colocating applications into a single accelerator device, its resource agnostic nature does not manage to face the resource under-utilization and quality of service violations especially for latency critical applications. In this paper, we design a resource aware GPU scheduling framework, able to efficiently colocate applications on the same GPU accelerator card. We integrate our solution with Kubernetes, one of the most widely used cloud orchestration frameworks. We show that our scheduler can achieve 58.8% lower end-to-end job execution time 99%-ile, while delivering 52.5% higher GPU memory usage, 105.9% higher GPU utilization percentage on average and 44.4% lower energy consumption on average, compared to the state-of-the-art schedulers, for a variety of ML representative workloads.

Subject Classification

ACM Subject Classification
  • Computing methodologies
  • Computer systems organization → Cloud computing
  • Computer systems organization → Heterogeneous (hybrid) systems
  • Hardware → Emerging architectures
Keywords
  • cloud computing
  • GPU scheduling
  • kubernetes
  • heterogeneity

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