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Efficient and Fair Scheduling for Multi-Tenant Cloud FPGAs


Core Concepts
This paper proposes an improved fair scheduling algorithm, THEMIS, that considers both the spatial and temporal aspects of tenant workloads, as well as the energy overhead, to provide fair and efficient multi-tenant scheduling on cloud FPGAs.
Abstract
The paper proposes an improved fair scheduling algorithm, THEMIS, for multi-tenant cloud FPGAs. The key highlights are: Latency-aware fair scheduling: THEMIS factors in tenants' timing/throughput requirements differences when establishing a fair scheduling policy, optimizing for both area demand and computation time. Energy-aware fair scheduling: THEMIS can adjust the scheduling decision intervals based on energy–fairness needs, allowing flexibility and trade-off between energy efficiency and fairness. Heterogenous region management: THEMIS considers the differences in tenant regions and makes realistic assumptions about the inflexibility of run-time merging/splitting of partially reconfigurable regions. Support for random tenant demands: THEMIS can handle scenarios where tenants demand slots in random order, in addition to the always-demand workload. The proposed THEMIS algorithm is implemented and evaluated on a Xilinx Zedboard XC7Z020 FPGA. Compared to prior works, THEMIS improves fairness between 24.2–98.4% and allows a trade-off between 55.3× in energy vs. 69.3× in fairness.
Stats
The FPGA slots have sizes S ∈ [4, 10, 18] on the PL side. The benchmarks have the following area and time demands: AES: A=2, CT=7 FFT: A=17, CT=5 SHA: A=6, CT=8 BFS: A=12, CT=15 KMP: A=3, CT=9 GEMM: A=14, CT=28 SORT: A=1, CT=14 SPMV: A=5, CT=14
Quotes
"Inspired by the Greek goddess of law and personification of justice, we name our fair scheduling solution THEMIS: Time, Heterogeneity, and Energy Minded Scheduling." "Compared to previous algorithms, our scheduling improves fairness between 24.2%–98.4%, and allows a trade-off between 55.3× in energy vs. 69.3× in fairness."

Key Insights Distilled From

by Emre Karabul... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00507.pdf
THEMIS

Deeper Inquiries

How can the proposed THEMIS algorithm be extended to support dynamic tenant arrival and departure in a multi-tenant cloud FPGA environment

To extend the THEMIS algorithm to support dynamic tenant arrival and departure in a multi-tenant cloud FPGA environment, several modifications and enhancements can be implemented. One approach is to incorporate a dynamic resource allocation mechanism that can adjust the allocation of resources based on the arrival and departure of tenants. This can involve real-time monitoring of resource usage and tenant demands, allowing for quick adjustments to accommodate new tenants or free up resources when tenants depart. Additionally, the algorithm can be designed to prioritize fairness in resource allocation while efficiently managing the dynamic changes in tenant composition. By integrating dynamic scheduling policies and resource management strategies, THEMIS can effectively handle the fluctuating nature of tenant arrivals and departures in a multi-tenant cloud FPGA environment.

What are the potential challenges in implementing THEMIS on a large-scale cloud FPGA infrastructure with hundreds of tenants and slots

Implementing THEMIS on a large-scale cloud FPGA infrastructure with hundreds of tenants and slots may pose several challenges. One major challenge is the scalability of the algorithm to handle a large number of tenants and slots efficiently. As the number of tenants and slots increases, the complexity of the scheduling algorithm grows, requiring optimized data structures and algorithms to maintain performance. Additionally, ensuring fairness and energy efficiency becomes more challenging with a larger scale, as the algorithm needs to balance resource allocation among numerous tenants while minimizing energy consumption. Managing the communication and coordination between a large number of tenants and slots can also be a challenge, requiring robust synchronization mechanisms to prevent conflicts and ensure smooth operation. Overall, the key challenges lie in scalability, efficiency, and coordination in handling the increased scale of the cloud FPGA infrastructure.

How can the energy-fairness trade-off in THEMIS be further optimized by incorporating machine learning techniques for predicting tenant workloads and resource demands

Incorporating machine learning techniques into THEMIS for predicting tenant workloads and resource demands can further optimize the energy-fairness trade-off. By leveraging machine learning algorithms, THEMIS can analyze historical data on tenant behavior, workload patterns, and resource utilization to predict future demands accurately. This predictive capability can enable proactive resource allocation and scheduling decisions, optimizing energy efficiency while maintaining fairness. Machine learning models can also adapt to changing workload dynamics and adjust scheduling parameters in real-time to achieve the desired energy-fairness balance. Additionally, reinforcement learning algorithms can be employed to continuously learn and improve the scheduling policies based on feedback from the system performance, leading to more adaptive and efficient resource management in the cloud FPGA environment. By integrating machine learning techniques, THEMIS can enhance its decision-making capabilities and achieve a more optimized energy-fairness trade-off.
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