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Arbitrary processor affinities are used in multiprocessor systems to specify the processors on which a task can be scheduled. However, affinity constraints can prevent some high priority real-time tasks from being scheduled, while lower priority tasks execute. This paper presents an implementation and evaluation of the Strong Arbitrary Processor Affinity scheduling on a real-time operating system, an approach that not only respects user-defined affinities, but also supports migration of a higher priority task to allow execution of a task limited by affinity constraints. Results show an improvement in response and turnaround times of higher priority tasks.
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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-FirstServe (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.
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- Award ID(s):
- 2135439
- PAR ID:
- 10530954
- Publisher / Repository:
- IEEE
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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