Using correct design metrics and understanding the limitations of the underlying technology is critical to developing effective scheduling algorithms. Unfortunately, existing scheduling techniques used \emph{incorrect} metrics and had \emph{unrealistic} assumptions for fair scheduling of multi-tenant FPGAs where each tenant is aimed to share approximately the same number of resources both spatially and temporally. This paper introduces an enhanced fair scheduling algorithm for multi-tenant FPGA use, addressing previous metric and assumption issues, with three specific improvements claimed First, our method ensures spatiotemporal fairness by considering both spatial and temporal aspects, addressing the limitation of prior work that assumed uniform task latency. Second, we incorporate energy considerations into fairness by adjusting scheduling intervals and accounting for energy overhead, thereby balancing energy efficiency with fairness. Third, we acknowledge overlooked aspects of FPGA multi-tenancy, including heterogeneous regions and the constraints on dynamically merging/splitting partially reconfigurable regions. We develop and evaluate our improved fair scheduling algorithm with these three enhancements. Inspired by the Greek goddess of law and personification of justice, we name our fair scheduling solution THEMIS: \underline{T}ime, \underline{H}eterogeneity, and \underline{E}nergy \underline{Mi}nded \underline{S}cheduling. We used the Xilinx Zedboard XC7Z020 to quantify our approach's savings. Compared to previous algorithms, our improved scheduling algorithm enhances fairness between 24.2--98.4\% and allows a trade-off between 55.3$\times$ in energy vs. 69.3$\times$ in fairness. The paper thus informs cloud providers about future scheduling optimizations for fairness with related challenges and opportunities.

翻译：采用正确的设计指标并理解底层技术的局限性对开发高效的调度算法至关重要。然而，现有调度技术在多租户FPGA公平调度中使用了不正确的指标，且对每个租户在空间和时间上共享大致相同资源数量的假设不切实际。本文提出了一种增强型多租户FPGA公平调度算法，解决了先前指标与假设问题，并声称实现了三项具体改进：第一，通过同时考虑空间和时间维度确保时空公平性，弥补了先前工作假设统一任务延迟的局限；第二，通过调整调度间隔并纳入能耗开销，将能耗考量融入公平性中，从而平衡能效与公平性；第三，关注了FPGA多租户中被忽视的方面，包括异构区域以及动态合并/拆分部分可重构区域的约束。我们基于这三项改进开发并评估了该增强型公平调度算法。受希腊法律女神与正义化身启发，我们将公平调度解决方案命名为THEMIS：时间（Time）、异构性（Heterogeneity）与能耗（Energy）导向的调度（Minded Scheduling）。通过Xilinx Zedboard XC7Z020平台量化了本方法的收益。与先前算法相比，本调度算法将公平性提升24.2%–98.4%，并允许在55.3倍的能耗与69.3倍的公平性之间进行权衡。该文为云服务商提供了未来公平性调度优化的方向及相关挑战与机遇。