Adaptive Request Scheduling for the I/O Forwarding Layer using Reinforcement Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Future Generation Computer Systems Year : 2020

Adaptive Request Scheduling for the I/O Forwarding Layer using Reinforcement Learning

Abstract

I/O optimization techniques such as request scheduling can improve performance mainly for the access patterns they target, or they depend on the precise tune of parameters. In this paper, we propose an approach to adapt the I/O forwarding layer of HPC systems to the application access patterns by tuning a request scheduler. Our case study is the TWINS scheduling algorithm, where performance improvements depend on the time window parameter, which depends on the current workload. Our approach uses a reinforcement learning technique – contextual bandits – to make the system capable of learning the best parameter value to each access pattern during its execution, without a previous training phase. We evaluate our proposal and demonstrate it can achieve a precision of 88% on the parameter selection in the first hundreds of observations of an access pattern. After having observed an access pattern for a few minutes (not necessarily contiguously), we demonstrate that the system will be able to optimize its performance for the rest of the life of the system (years).
Fichier principal
Vignette du fichier
Bezetal_Adaptive.pdf (635.62 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01994677 , version 1 (25-01-2019)
hal-01994677 , version 2 (26-02-2019)
hal-01994677 , version 3 (16-10-2019)

Identifiers

Cite

Jean Luca Bez, Francieli Zanon Boito, Ramon Nou, Alberto Miranda, Toni Cortes, et al.. Adaptive Request Scheduling for the I/O Forwarding Layer using Reinforcement Learning. Future Generation Computer Systems, 2020, 112, pp.1156-1169. ⟨10.1016/j.future.2020.05.005⟩. ⟨hal-01994677v3⟩
528 View
513 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More