Context-Aware Heterogeneous Task Scheduling for Multi-Layered Systems
Context-Aware Heterogeneous Task Scheduling for Multi-Layered Systems
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DOI：
10.1109/wowmom57956.2023.00034
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Sharon L. G. Contreras;M. Levorato
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文献类型：
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作者：
Sharon L. G. Contreras;M. Levorato
机器学习正在成为移动应用程序中越来越不可或缺的组成部分。但是，由于其计算能力，内存和能量储备有限，因此在资源约束设备上执行计算重型神经模型（例如，对于计算机视觉任务）。尽管Edge计算缓解这些问题，但信息丰富的信号在容量限制和时变的无线通道上的转移可能会导致较大的延迟和延迟变化。本文中，我们提出了一种方法，以在由移动设备和边缘服务器组成的系统的资源和层中路由异质任务。与先前的工作不同，我们考虑了现实世界系统的各个方面，例如上下文切换，任务积累以及整体管道的通信和计算组件之间的相互作用，这些相互作用很少在抽象模型中捕获。为了优化任务流，我们使用了使用我们开发的系统收集的真实数据培训的深入加强学习代理。该代理使用系统的几个逻辑块对状态图特征的表达定义。结果表明，代理将任务的路由调整为控制其异质性的参数，以及硬件设置和无线通道的状态。
Machine learning is becoming an increasingly integral component of mobile applications. However, the execution of compute-heavy neural models (e.g., for computer vision tasks) on resource-constrained devices is challenging due to their limited computing power, memory, and energy reservoir. While edge computing mitigates these issues, the transfer of information-rich signals over capacity-limited and time-varying wireless channels may result in large latency and latency variations. Herein, we propose a methodology to route heterogeneous tasks across the resources and layers of systems composed of mobile devices and edge servers. Different from prior work, we consider aspects of real-world systems, such as context switching, task accumulation, and the interplay between communications and computing components of the overall pipeline, that are rarely captured in abstract models. To optimize the task flow, we use a deep reinforcement learning agent trained on real-world data collected using a system we developed. The agent uses an articulate definition of state drawing features from several logical blocks of the system. Results indicate that the agent adapts the routing of tasks to parameters controlling their heterogeneity, as well as the hardware setup and the state of the wireless channel.