Predictive Worker Resource Characterization at the Extreme Edge
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Authors
Kain, Ruslan
Date
2025-05-06
Type
thesis
Language
eng
Keyword
Distrbuted Computing , Edge Computing , Extreme Edge Computing , Internet of Things , Multi-step Time Series Prediction , Task Allocation
Alternative Title
Abstract
Extreme Edge Computing (EEC) promises to enhance the Quality of Service of data-intensive and delay-critical IoT applications. Deploying EEC is challenging due to reliance on user-owned Extreme Edge Devices (EEDs) with heterogeneous computational and communication capabilities and dynamic user behaviors causing resource volatility. This volatility complicates the accurate estimation of EED computational capabilities, a critical step for efcient task allocation in EEC systems. Previous research characterizes EED capabilities simplistically by allocating benchmark tasks without accounting for resource volatility, using insufcient performance indicators, and reactively adjusting EEC task allocation only after volatility impacts system operations. In this thesis, we propose a comprehensive framework for predictively characterizing the computational resources of EEDs according to their dynamic resource usage to address this challenge. The framework consists of four key components that: 1) emulate dynamic user-access behaviors and generate resource usage data, 2) forecast resource usage states efciently over multi-step horizons, 3) allocate benchmark tasks based on predicted states to characterize EED capabilities, and 4) dynamically allocate EEC tasks based on adaptive worker characterization. Extensive experiments on a realistic EED testbed with an interactive monitoring dashboard demonstrate our frameworkâs efcacy in improving EEC task allocation performance, reducing execution latency, and increasing throughput without overloading device resources, compared to prominent existing schemes.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 4.0 International
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 4.0 International