DiPET: Distributed Stream Processing on Fog and Edge Systems via Transprecise Computing

DiPET项目研究了使分布式流处理应用程序能够在雾和边缘计算系统之间无缝跨越和重新分布的模型和技术。其目标是利用分散在网络中地理位置更接近用户的设备，以减少网络延迟并增加可用网络带宽。但是，用户设备连接到的网络是动态的。例如，移动的设备在漫游时连接到不同的基站，并且雾设备可能间歇性地不可用于计算。为了最大限度地利用这些动态网络中的异构计算和网络资源，DiPET项目采用了一种基于透明精确计算的大胆方法。Transprecise计算指出，计算不需要总是精确的，并提出了一个有纪律的权衡精度与准确性，这对计算工作量，能源效率，内存使用和通信带宽和延迟的影响。Transprecise计算允许根据上下文和可用资源动态调整计算精度。这为在雾和边缘计算环境中调度分布式流应用程序的问题创造了新的维度，并将导致具有上级性能、能效和用户体验的调度。DiPET项目将通过开发transprecise流处理应用框架和transprecision感知中间件来证明这种独特方法的可行性。视频分析和网络入侵检测的用例将指导研究并支持技术演示。

The DiPET project investigates models and techniques that enable distributed stream processing applications to seamlessly span and redistribute across fog and edge computing systems. The goal is to utilize devices dispersed through the network that are geographically closer to users to reduce network latency and to increase the available network bandwidth. However, the network that user devices are connected to is dynamic. For example, mobile devices connect to different base stations as they roam, and fog devices may be intermittently unavailable for computing. In order to maximally leverage the heterogeneous compute and network resources present in these dynamic networks, the DiPET project pursues a bold approach based on transprecise computing. Transprecise computing states that computation need not always be exact and proposes a disciplined trade-off of precision against accuracy, which impacts on computational effort, energy efficiency, memory usage and communication bandwidth and latency. Transprecise computing allows to dynamically adapt the precision of computation depending on the context and available resources. This creates new dimensions to the problem of scheduling distributed stream applications in fog and edge computing environments and will lead to schedules with superior performance, energy efficiency and user experience. The DiPET project will demonstrate the feasibility of this unique approach by developing a transprecise stream processing application framework and transprecision-aware middleware. Use cases in video analytics and network intrusion detection will guide the research and underpin technology demonstrators.