Access to this thesis is limited to Boise State University students and employees or persons using Boise State University facilities.
Off-campus Boise State University users: To download Boise State University access-only theses/dissertations, please select the "Off-Campus Download" button and enter your Boise State username and password when prompted.
Date of Final Oral Examination (Defense)
Type of Culminating Activity
Thesis - Boise State University Access Only
Master of Science in Computer Science
Yantian Hou, Ph.D.
Dianxiang Xu, Ph.D.
Min Long, Ph.D.
Traditional state-of-the-art cloud-computing is intrinsically suitable for processing computation-intensive image processing tasks due to its abundant computing and storage resource. However, the current cloud-based architecture requires remote data offloading, resulting in large transmission delay, making it highly unsuitable for current IoT based applications that are latency-sensitive. Our solution is to use the edge-computing technique to assist the cloud platform by processing the tasks on the edge servers that are placed close to the data sources. In this thesis, we apply edge-computing for two closely-related image processing tasks that are very common in modern IoT applications.
First, we consider a large-scale camera surveillance system for human re-identification. Our proposed solution considers the interplay between edge and cloud server, which we model mathematically and formulate as a bi-criteria optimization problem. We develop a measurement-driven offline algorithm that models the system's heterogeneity in terms of workload complexity. We propose an online algorithm which is based on linearization and a pipage rounding technique that efficiently solves the formulated optimization problem. The solution gives the decision variables that maintain the user-defined trade-off between system utility and responsiveness. In the second work, we develop a novel edge-assisted hierarchical platform. Our platform utilizes the processing capability at the edge to pre-process the workload to approximate the most relevant features, which are then precisely analyzed at the cloud. Considering various responding and placement strategies, we formulate a latency-minimization problem. Using a queuing mechanism, we capture the background workload's influence at the edge server and develop a two-stage algorithm to solve the problem. The solution gives optimal responding and placement strategies to process the body-detection task in the most time-efficient way. We implement our entire system on a commercial cloud platform and use extensive simulations and experiments to validate its efficacy and responsiveness in practice.
Acharya, Anil, "Edge-Assisted Workload-Aware Image Processing System" (2019). Boise State University Theses and Dissertations. 1510.