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Joint Resource Management and Task Scheduling for Mobile Edge Computing
Wei, Xinliang
Wei, Xinliang
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Thesis/Dissertation
Date
2023
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Computer and Information Science
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http://dx.doi.org/10.34944/dspace/8432
Abstract
In recent years, edge computing has become an increasingly popular computing paradigm to enable real-time data processing and mobile intelligence. Edge computing allows computing at the edge of the network, where data is generated and distributed at the nearby edge servers to reduce the data access latency and improve data processing efficiency. In addition, with the advance of Artificial Intelligence of Things (AIoT), not only millions of data are generated from daily smart devices, such as smart light bulbs, smart cameras, and various sensors, but also a large number of parameters of complex machine learning models have to be trained and exchanged by these AIoT devices. Classical cloud-based platforms have difficulty communicating and processing these data/models effectively with sufficient privacy and security protection. Due to the heterogeneity of edge elements including edge servers, mobile users, data resources, and computing tasks, the key challenge is how to effectively manage resources (e.g. data, services) and schedule tasks (e.g. ML/FL tasks) in the edge clouds to meet the QoS of mobile users or maximize the platform's utility. To that end, this dissertation studies joint resource management and task scheduling for mobile edge computing.
The key contributions of the dissertation are two-fold. Firstly, we study the data placement problem in edge computing and propose a popularity-based method as well as several load-balancing strategies to effectively place data in the edge network. We further investigate a joint resource placement and task dispatching problem and formulate it as an optimization problem. We propose a two-stage optimization method and a reinforcement learning (RL) method to maximize the total utilities of all tasks. Secondly, we focus on a specific computing task, i.e., federated learning (FL), and study the joint participant selection and learning scheduling problem for multi-model federated edge learning. We formulate a joint optimization problem and propose several multi-stage optimization algorithms to solve the problem. To further improve the FL performance, we leverage the power of the quantum computing (QC) technique and propose a hybrid quantum-classical Benders' decomposition (HQCBD) algorithm as well as a multiple-cuts version to accelerate the convergence speed of the HQCBD algorithm. We show that the proposed algorithms can achieve the consistent optimal value compared with the classical Benders' decomposition running in the classical CPU computer, but with fewer convergence iterations.
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