THEMIS: Time, Heterogeneity, and Energy Minded Scheduling for Fair Multi-Tenant Use in FPGAs

2024-03-31

arXiv.org

AI Summary

Existing FPGA multi-tenant scheduling approaches rely on unrealistic assumptions—including uniform latency and homogeneous resource models—and employ inadequate fairness metrics, leading to spatiotemporal resource imbalance, uneven energy consumption, and poor scheduling alignment with heterogeneous reconfigurable regions. Method: We propose the first three-dimensional co-scheduling algorithm integrating spatiotemporal fairness, dynamic energy regulation, and explicit modeling of heterogeneous region constraints. Implemented on a Xilinx ZedBoard XC7Z020 platform, it enables timing-aware scheduling, fine-grained energy cost modeling, and dynamic merging/splitting of partially reconfigurable regions. Contribution/Results: Our approach improves fairness by 24.2–98.4% over baselines and achieves a Pareto-optimal trade-off—55.3× energy reduction and 69.3× fairness improvement—while breaking conventional assumptions. It significantly enhances resource utilization efficiency and service fairness in multi-tenant FPGA cloud platforms.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Hardware-aware ML

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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$ imes$ in energy vs. 69.3$ imes$ in fairness. The paper thus informs cloud providers about future scheduling optimizations for fairness with related challenges and opportunities.

Problem

• Ensures spatiotemporal fairness in multi-tenant FPGA scheduling
• Incorporates energy considerations to balance efficiency and fairness
• Addresses heterogeneous regions and reconfigurable constraints in FPGAs

Innovation

• Ensures spatiotemporal fairness in scheduling
• Incorporates energy considerations into fairness
• Addresses heterogeneous FPGA regions constraints