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

Lead Research Organisation: Queen's University Belfast
Department Name: Sch of Electronics, Elec Eng & Comp Sci

Abstract

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.

Planned Impact

n/a
 
Description Various video analytics tasks are highly time-consuming and require significant power to complete. This places minimal requirements on the computing hardware that must be used. However, highly capable computing devices reduce battery life, increase heat dissipation, increase weight and form factor. Many video analytics tasks are most accurately solved using deep neural networks, a popular machine learning technique that requires a lot of compute power.
We were able to demonstrate that demanding video analytics tasks can be performed by devices with limited computational capacity. We achieved this by creating a new piece of software that controls which out of multiple algorithms is used to analyse a particular frame. This controller selects simple algorithms when the video's content can be analysed easily, and selects complex algorithms only when the video content requires this. Moreover, the system can adapt seamlessly to background workloads, and hence to dynamically varying availability of compute resources.
As a result, analytics can be achieved on cheaper, less powerful devices. The system even achieves a slightly higher accuracy.
Exploitation Route Any company or individual developing applications for environments with constrained computations, such as distributed edge computing, internet of things, mobile devices and autonomous vehicles, may benefit from these findings.
Sectors Digital/Communication/Information Technologies (including Software)
 
Description (SoftNum) - Software-Defined Number Formats: Bridging the Gap between Performance, Accuracy, and Security
Amount € 224,934 (EUR)
Funding ID 101031148 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 09/2022 
End 08/2024
 
Description Asynchronous Scientific Continuous Computations Exploiting Disaggregation (ASCCED)
Amount £202,212 (GBP)
Funding ID EP/X01794X/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 03/2023 
End 06/2024
 
Description RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories
Amount £403,636 (GBP)
Funding ID EP/V02860X/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 06/2022 
End 05/2025
 
Title Dataset for Anomaly Detection in a Production Wireless Mesh Community Network 
Description CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 01:55:00 2021-04-14 18:10:00 there is the failure of a gateway in the mesh (node 24). 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact None yet. 
URL https://zenodo.org/record/6169917#.Yh-rQy-l1pS
 
Description Real-time video analytics on edge devices for animal welfare monitoring 
Organisation Queen's University Belfast
Department Centre for Secure Information Technologies (CSIT)
Country United Kingdom 
Sector Academic/University 
PI Contribution We are investigating the application of the transprecise object detection (TOD) step in video analytics that is being developed in DiPET to the use case of monitoring animal welfare using a video analytics pipeline. The pipeline consists of object detection, object tracking and behavioural analysis. The pipeline is to run in real-time on an edge computing environment or a smart camera.
Collaborator Contribution The Centre for Secure information Technology are partners the FlockFocus project, sponsored by The Foundation for Food and Agriculture Research (FFAR) and McDonald's (https://www.qub.ac.uk/ecit/News/Queensacademicreceivesfundingtoenhancefarmedchickenwelfareresearch.html). The project aims to develop a vision-based system that leverages novel crowd analysis research and applies it to the tracking and behavioural analysis of a flock of chickens. This will enable researchers to monitor large numbers of birds, track their activity patterns and gather welfare indicators such as gait, feather cleanliness and incidents of play behaviour. CSIT is developing the object tracking and behavioural analysis techniques.
Impact Not outputs or outcomes yet.
Start Year 2021
 
Title Real-time transprecise object detection in constrained computing environments 
Description Object detection in video stream is the act of identifying specific objects and describing them by their coordinates in a video frame, typically a bounding box. A transprecise object detector uses a variety of object detection techniques with varying accuracy of identification and varying amounts of computation. We have developed a transprecise object detector that uses size of bounding boxes in the frame to predict which object detection algorithm can provide sufficient accuracy with minimal computation and thus minimal energy consumption. When algorithms require too much computation, it will not be possible to complete them by the time the next frame arrives (soft real-time deadline) and frames need to be dropped. This results in reduced identification accuracy. Our transprecise object detector varies the underlying algorithm from frame to frame in order to meet deadlines as much as possible while maintaining sufficient accuracy (contradictory constraints). Over a video stream of several minutes, our method achieves higher accuracy than any of the underlying algorithms in isolation. The software is not available yet, but will be made available when sufficiently mature. 
Type Of Technology Software 
Year Produced 2021 
Impact The software has not been shared publicly yet. We are looking into licensing the technology. We also have an updated version called ROMA which has additional features. 
 
Description Invited Talk - HiPEAC Computing Systems Week - Lyon 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact Presenting ideas on managing services in an edge computing or IoT context to an audience of academics and industry practitioners.
Year(s) Of Engagement Activity 2021
URL https://www.hipeac.net/csw/2021/lyon/#/program/