Energy-aware fully-adaptive resource provisioning in collaborative CPU-FPGA cloud environments

Autor(es)

Jordan, Michael Guilherme ; Korol, Guilherme ; Knorst, Tiago ; Rutzig, Mateus Beck ; Beck, Antonio Carlos Schneider

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING; v. 176, p. 15-pg., 2023-03-02.

Resumo

Cloud warehouses have been exploiting multi-tenancy in CPU-FPGA collaborative environments, so clients can share the same infrastructure, achieving scalability and maximizing resource utilization. Therefore, the distribution of tasks across CPU and FPGA must be well-balanced so performance and energy are optimized in a highly variant workload scenario. In this paper, we take a step further and, in contrast to existing approaches, exploit DVFS (Dynamic Voltage and Frequency Scaling) on the CPU, together with an intelligent CPU-FPGA resource provisioning mechanism, to further improve energy. For that, we propose EASER, an end user-transparent framework that employs multiple strategies and dynamically selects the most appropriate one to optimize resource provisioning and DVFS according to the warehouse needs, workload properties, and target architecture. Our synergistic DVFS optimization brings up to 22% additional energy gains over our dynamic provisioning alone. Compared to fixed single strategies with DVFS, EASER brings, on average, 71% of energy gains. (c) 2023 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 21/06825-8 - ADAPTT: provendo eficiência de recursos na classificação de tráfego através do uso sinergético e adaptativo de FPGAs e CNNs
• Beneficiário: Antonio Carlos Schneider Beck Filho
• Modalidade de apoio: Auxílio à Pesquisa - Regular