支持截止期敏感应用的数据流任务调度方法

Stream Task Scheduling Method for Deadline-Sensitive Applications

当前,数据流上的实时处理系统大多关心平均元组延时最小化要求,而很少考虑每个元组的截止期要求.提出一种实时的自适应批任务调度策略——ATS(adaptive batch task scheduling),以支持时变突发的数据流上关键任务的严格截止期需求.ATS调度策略可以降低调度开销和过期处理开销,从而实现截止期错失率最小化和有效任务完成率最大化.提出了最优调度单位概念——批粒度,设计了闭环反馈控制机制,以在不可预测的数据流环境中自适应地动态选择最优批大小.理论分析和实验表明了ATS批调度策略的有效性和高效性.

Most of the existing real-time processing systems over data streams focus on minimizing average tuple latency while less attention has been paid to deadline of each individual tuple. This paper presents a real-time adaptive batch task scheduling （ATS） mechanism to support the strict deadline requirements of mission-critical applications over time-varying and bursting data streams. The ATS strategy aims at maximizing task throughput and minimizing deadline miss ratio by minimizing both scheduling overheads and deadline miss overheads. The paper proposes a concept of the optimal scheduling unit-batch granularity, and designs a closed-loop feedback control mechanism to adaptively select the dynamic optimal batch size in a non-predictable data stream environment. The theoretical analyses and experimental results show the efficiency and effectiveness of the ATS batching technique.