MUTECO: CPU-FPGA多租户环境下的协同分配框架

M. Jordan, Guilherme Korol, M. B. Rutzig, A. C. S. Beck
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引用次数: 2

摘要

CPU-FPGA协同环境正逐渐被云仓库所采用。在这种环境中,多个客户端共享相同的基础设施,从而最大限度地利用能源效率和可伸缩性。然而,这样的资源配置是具有挑战性的,因为在可用资源和工作负载特征急剧变化的场景中,内核可能同时分配给CPU和FPGA。为了在这个复杂的环境中充分利用资源,我们提出MUTECO:一个多租户协作资源供应框架。与专注于协作性单租户或非协作性多租户工作负载的现有方法相比,MUTECO优化了多租户和CPU-FPGA协同执行。MUTECO是高度可配置的,并且集成到Hypervisor层,因此可以根据包含租户请求数量、传入内核的行为和可用资源的不同场景对其进行调优,以优化收敛时间、性能和精力。在各种场景中,MUTECO的性能比当前的非协作和单租户方法高出2.91倍和2.39倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MUTECO: A Framework for Collaborative Allocation in CPU-FPGA Multi-tenant Environments
CPU-FPGA collaborative environments are progressively being adopted by Cloud Warehouses. In this environment, multiple clients share the same infrastructure to maximize resource utilization with energy efficiency and scalability. However, such a provisioning of resources is challenging, since kernels may be concurrently assigned to both CPU and FPGA in a scenario where available resources and workload characteristics drastically vary. To make the best use of resources in this complex environment, we propose MUTECO: A MUlti-TEnant COllaborative resource provisioning framework. MUTECO optimizes considering both multitenancy and CPU-FPGA collaborative execution, in contrast to existing approaches that focus on collaborative single-tenant or non-collaborative multi-tenant workloads. MUTECO is highly configurable and integrated to the Hypervisor layer, so it can be tuned to optimize convergence time, performance, and energy, according to different scenarios that comprise number of tenant requests, the incoming kernels' behavior, and the available resources. Over a varied set of scenarios, MUTECO outperforms in up to 2.91x and 2.39x the current non-collaborative and single-tenant approaches.
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