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Power and performance optimization in FPGA‐accelerated clouds
Author(s) -
Tesfatsion Selome Kostentions,
Proaño Julio,
Tomás Luis,
Caminero Blanca,
Carrión Carmen,
Tordsson Johan
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4526
Subject(s) - computer science , scheduling (production processes) , field programmable gate array , efficient energy use , embedded system , energy consumption , frequency scaling , server , cloud computing , power management , data center , power budget , distributed computing , operating system , power (physics) , power control , engineering , physics , quantum mechanics , operations management , electrical engineering
Summary Energy management has become increasingly necessary in data centers to address all energy‐related costs, including capital costs, operating expenses, and environmental impacts. Heterogeneous systems with mixed hardware architectures provide both throughput and processing efficiency for different specialized application types and thus have a potential for significant energy savings. However, the presence of multiple and different processing elements increases the complexity of resource assignment. In this paper, we propose a system for efficient resource management in heterogeneous clouds. The proposed approach maps applications' requirement to different resources reducing power usage with minimum impact on performance. A technique that combines the scheduling of custom hardware accelerators, in our case, Field‐Programmable Gate Arrays (FPGAs) and optimized resource allocation technique for commodity servers, is proposed. We consider an energy‐aware scheduling technique that uses both the applications' performance and their deadlines to control the assignment of FPGAs to applications that would consume the most energy. Once the scheduler has performed the mapping between a VM and an FPGA, an optimizer handles the remaining VMs in the server, using vertical scaling and CPU frequency adaptation to reduce energy consumption while maintaining the required performance. Our evaluation using interactive and data‐intensive applications compare the effectiveness of the proposed solution in energy savings as well as maintaining applications performance, obtaining up to a 32% improvement in the performance‐energy ratio on a mix of multimedia and e‐commerce applications.