KubFBS: A fine‐grained and balance‐aware scheduling system for deep learning tasks based on kubernetes

Abstract

The past decade witnessed a remarkable increase in deep learning (DL) workloads which require GPU resources to accelerate the training process. However, the existing coarse‐grained scheduling mechanisms are agnostic to information other than the number of GPUs or GPU memory, which results in performance degradation of DL tasks. Moreover, the common assumption held by the existing balance‐aware DL task scheduling strategies, a DL task consumes resources once it starts, fails to reduce resource contention, and further limits execution efficiency. To address these problems, this article proposes a fine‐grained and balance‐aware scheduling model (FBSM) which considers the resource consumption characteristic of the DL task. Based on FBSM, we propose customized GPU sniffer (GPU‐S) and balance‐aware scheduler (BAS) modules to construct a scheduling system called KubFBS. The experimental results demonstrate KubFBS accelerates the execution of DL tasks while improving the load balancing capability of the cluster.

A survey of Kubernetes scheduling algorithms

“…In Liu et al [58], the authors suggest a scheduling strategy for deep learning tasks on Kubernetes that takes into account the tasks' resource usage characteristics. To increase task execution efficiency and load balancing, the suggested paradigm, dubbed FBSM, has modules for a GPU sniffer and a balance-aware scheduler.…”

An Improved Genetic Algorithm with Chromosome Replacement and Rescheduling for Task Offloading

“…1. x i,j,k is 1 if t k is assigned to jth core in computing node s i , and 0 otherwise. In this paper, we consider the resource granularity as computing core instead of computing node, because considering fine granularity of resources helps to improve the resource efficiency [13].…”