Towards Intelligent Edge Computing: A Resource- and Reliability-Aware Hybrid Scheduling Method on Multi-FPGA Systems

Multi-FPGA systems can form larger and more powerful computing units through high-speed interconnects between chips, and are beginning to be widely used by various computing service providers, especially in edge computing. However, the new computing architecture brings new challenges to efficient and reliable task scheduling. In this context, we propose a resource- and reliability-aware hybrid scheduling method on Multi-FPGA systems. First, a set of models is established based on the resource/time requirements, communication overhead, and state conversion process of tasks to further analyze the constraints of system scheduling. On this basis, the large task is divided into subtasks based on the data dependency matrix, and the Maintenance Multiple Sequence (MMS) algorithm is used to generate execution sequences for each subtask to the Multi-FPGA systems to fully exploit resources and ensure reliable operation. Compared with state-of-the-art scheduling methods, the proposed method can achieve an average increase in resource utilization of 7%; in terms of reliability, it achieves good execution gains, with an average task completion rate of 98.3% and a mean time to failure of 15.7 years.