The demand and interest in serving Computer Vision applications at the Edge, where Edge Devices generate vast quantities of data, clashes with the reality that many Devices are largely unable to process their data in real time. While computational offloading, not to the Cloud but to nearby Edge Nodes, offers convenient acceleration for these applications, such systems are not without their constraints. As Edge networks may be unreliable or wireless, offloading quality is sensitive to communication bottlenecks. Unlike seemingly unlimited Cloud resources, an Edge Node, serving multiple clients, may incur delays due to resource contention. This project describes relevant Computer Vision workloads and how an effective offloading framework must adapt to the constraints that impact the Quality of Service yet have not been effectively nor properly addressed by previous literature. We design an offloading controller, based on closed-loop control theory, that enables Devices to maximize their throughput by appropriately offloading under variable conditions. This approach ensures a Device can utilize the maximum available offloading bandwidth. Finally, we constructed a realistic testbed and conducted measurements to demonstrate the superiority of our offloading controller over previous techniques. / Master of Science / Devices like security cameras and some Internet of Things gadgets produce valuable real-time video for AI applications. A field within AI research called Computer Vision aims to use this visual data to compute a variety of useful workloads in a way that mimics the human visual system. However, many workloads, such as classifying objects displayed in a video, have large computational demands, especially when we want to keep up with the frame rate of a real-time video. Unfortunately, these devices, called Edge Devices because they are located far from Cloud datacenters at the edge of the network, are notoriously weak for Computer Vision algorithms, and, if running on a battery, will drain it quickly. In order to keep up, we can offload the computation of these algorithms to nearby servers, but we need to keep in mind that the bandwidth of the network might be variable and that too many clients connected to a single server will overload it. A slow network or an overloaded server will incur delays which slow processing throughput. This project describes relevant Computer Vision workloads and how an effective offloading framework that effectively adapts to these constraints has not yet been addressed by previous literature. We designed an offloading controller that measures feedback from the system and adapts how a Device offloads computation, in order to achieve the best possible throughput despite variable conditions. Finally, we constructed a realistic testbed and conducted measurements to demonstrate the superiority of our offloading controller over previous techniques.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115511 |
| Date | 26 June 2023 |
| Creators | Jackson, Matthew Norman |
| Contributors | Computer Science and Applications, Nikolopoulos, Dimitrios S., Ji, Bo, Hu, Liting |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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