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Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning

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Publicado en:arXiv.org (Feb 9, 2024), p. n/a
Autor principal: Russo, Enrico
Otros Autores: Blanco, Francesco Giulio, Palesi, Maurizio, Ascia, Giuseppe, Patti, Davide, Catania, Vincenzo
Publicado:
Cornell University Library, arXiv.org
Materias:
Level indicators
Algorithms
Deep learning
Computer aided scheduling
Machine learning
Real time
Artificial neural networks
Acceso en línea:Citation/Abstract
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Resumen:This paper addresses the critical challenge of managing Quality of Service (QoS) in cloud services, focusing on the nuances of individual tenant expectations and varying Service Level Indicators (SLIs). It introduces a novel approach utilizing Deep Reinforcement Learning for tenant-specific QoS management in multi-tenant, multi-accelerator cloud environments. The chosen SLI, deadline hit rate, allows clients to tailor QoS for each service request. A novel online scheduling algorithm for Deep Neural Networks in multi-accelerator systems is proposed, with a focus on guaranteeing tenant-wise, model-specific QoS levels while considering real-time constraints.
ISSN:2331-8422
Fuente:Engineering Database