EdgeRep: Reproducing and benchmarking edge analytic systems

• Topics: video analytics, machine learning
• Skills: Python, PyTorch, Bash scripting, Linux, Machine Learning modeling
• Difficulty: Medium
• Size: Large (350 hours)
• Mentors: Yuyang (Roy) Huang (contact person), Junchen Jiang

Project Idea Description

With the flourishing of ideas like smart cities and smart manufacturing, a massive number of edge devices (e.g., traffic or security cameras, thermometers, flood sensors, etc.) are deployed and connected to the network. These devices collect and analyze data across space and time, aiding stakeholders like city governments and manufacturers in optimizing their plans and operations. However, the sheer number of edge devices and the large amount of communication among the devices and central servers raises significant challenges in how to manage and schedule resources. This includes network bandwidth between the devices and computing power on both edge devices and bare metal servers, all to maintain the reliable service capability of running applications.

Moreover, given the limited resources available to edge devices, there’s an emerging trend to reduce average compute and/or bandwidth usage. This is achieved by leveraging the uneven distribution of interesting events with respect to both time and space in the input data. This, in turn, introduces further challenges in provisioning and managing the amount of resources available to edge devices. The resource demands of running applications can greatly depend on the input data, which is both dynamic and unpredictable.

Keeping these challenges in mind, the team previously designed and implemented a dynamic resource manager capable of understanding the applications and making decisions based on this understanding at runtime. However, such a resource manager has only been tested with a limited number and types of video analytic applications. Thus, through the OSRE24 project, we aim to:

• Collect a wide range of videos to form a comprehensive video dataset
• Reproduce other state-of-art self-adaptive video analytic applications
• Package the dataset as well as the application to publish them on Chameleon Trovi site

Project Deliverable

• Collect a wide range of videos to form a comprehensive video dataset
• Reproduce other state-of-art self-adaptive video analytic applications
• Package the dataset as well as the application to publish them on Chameleon Trovi site