Quality/Latency-Aware Real-time Scheduling of Distributed Streaming IoT Applications
Quality/Latency-Aware Real-time Scheduling of Distributed Streaming IoT Applications
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DOI：
10.1145/3358209
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发表时间：
2019-10
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通讯作者：
Kamyar Mirzazad Barijough;Zhuoran Zhao;A. Gerstlauer
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文献类型：
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作者：
Kamyar Mirzazad Barijough;Zhuoran Zhao;A. Gerstlauer
嵌入式系统越来越多地联网和分布，通常，例如在物联网（IoT）中，通过具有潜在无限延迟的开放网络。一个关键的挑战是需要在这种固有的不可靠和不可预测的网络上提供实时保证。通常，超时用于提供定时保证，同时权衡数据丢失和质量。分布式任务执行和网络超时的时间表，从而确定了一个基本的延迟质量权衡，然而，不考虑现有的调度算法。在本文中，我们提出了一种方法调度的分布式，实时流媒体应用程序的质量延迟的目标。我们制定这作为一个问题，分析得出一个静态的最坏情况下的时间表的一个给定的分布式的最小化的质量损失，同时满足保证的延迟约束。为此，我们首先开发了一个质量模型，估计SNR的分布式流媒体应用程序在给定的网络特性和整体线性假设。使用这个质量模型，然后，我们制定和解决调度的分布式图作为一个数值优化问题。随机图的仿真结果表明，质量/延迟感知调度提高了SNR超过基线调度平均50%。当应用于手写数字识别的分布式神经网络应用程序时，我们的调度方法可以在严格的延迟约束下将分类精度提高10%。
Embedded systems are increasingly networked and distributed, often, such as in the Internet of Things (IoT), over open networks with potentially unbounded delays. A key challenge is the need for real-time guarantees over such inherently unreliable and unpredictable networks. Generally, timeouts are used to provide timing guarantees while trading off data losses and quality. The schedule of distributed task executions and network timeouts thereby determines a fundamental latency-quality trade-off that is, however, not taken into account by existing scheduling algorithms. In this paper, we propose an approach for scheduling of distributed, real-time streaming applications under quality-latency goals. We formulate this as a problem of analytically deriving a static worst-case schedule of a given distributed dataflow graph that minimizes quality loss while meeting guaranteed latency constraints. Towards this end, we first develop a quality model that estimates SNR of distributed streaming applications under given network characteristics and an overall linearity assumption. Using this quality model, we then formulate and solve the scheduling of distributed dataflow graphs as a numerical optimization problem. Simulation results with random graphs show that quality/latency-aware scheduling improves SNR over a baseline schedule by 50% on average. When applied to a distributed neural network application for handwritten digit recognition, our scheduling methodology can improve classification accuracy by 10% over a naive distribution under tight latency constraints.