Data-Priority Aware Fair Task Scheduling for Stream Processing at the Edge

Real-time data stream processing at the edge is crucial for time-sensitive tasks within large-scale IoT systems. Task scheduling plays a key role in managing the Quality of Service (QoS), necessitating a prioritization system to distinguish between high and low-priority tasks, thus ensuring efficient data processing on edge nodes. Existing scheduling algorithms rigidly prioritize tasks deemed as high-priority, often at the expense of fairness and overall system efficiency. In this paper, we propose a Priority-aware Fair Task Scheduling (FTS-Hybrid) algorithm that addresses these challenges by managing priority-based task execution in a controlled manner. Our task scheduling algorithm streamlines resource utilization and enhances system responsiveness, contributing to low latency and high throughput, outperforming competing techniques including First-Come-First-Serve (FCFS), Round Robin (RR), and Priority Scheduling (PS). We implemented FTS-Hybrid on Apache Storm and evaluated its performance using an open-source real-time IoT benchmark (RIoTBench). Experimental results show that the FTS-Hybrid algorithm reduces task execution latency by 24 %, 31 %, and 26 % compared with FCFS, RR, and PS, respectively, by strategically mitigating queuing delays under dynamic workload conditions.