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Joint DNN Partitioning and Resource Allocation for Multiple Machine Learning-based Mobile Applications at the Network Edge.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Joint DNN Partitioning and Resource Allocation for Multiple Machine Learning-based Mobile Applications at the Network Edge./
Author:	Cheng, Cheng-Yu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	103 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30815625
ISBN:	9798381163711

Joint DNN Partitioning and Resource Allocation for Multiple Machine Learning-based Mobile Applications at the Network Edge.
Cheng, Cheng-Yu.

Joint DNN Partitioning and Resource Allocation for Multiple Machine Learning-based Mobile Applications at the Network Edge. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 103 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--The Catholic University of America, 2024.

This item must not be sold to any third party vendors.

 The increasing use of artificial intelligence (AI) and machine learning (ML) in mobile applications requires stringent quality of service such as low latency and high accuracy. Deep Neural Networks (DNNs) are the core technique used in AI and ML, but they are computation-intensive and power-consuming, leading to limitations in computing capability and energy consumption on user mobile devices (MD). As a result, many AI/ML applications offload DNN processing tasks from MDs to mobile edge computing servers (ES) or cloud data centers. However, this approach puts significant computational pressure on the servers and requires significant amounts of data to be transmitted over wireless networks. DNNs consist of multiple connected layers of neurons, and the computation complexity and amount of intermediate output data vary at each layer. Partitioning the DNN inference operations between mobile devices and edge/cloud servers based on the DNN layer characteristics can significantly reduce computation pressure, power, and data rate requirements at the mobile device, while improving overall operation efficiency, latency, accuracy, and privacy, that is, the optimal DNN partitioning point and offloading strategy should depend on the computation and output data size characteristics of the layer compositions, available resources at the mobile devices and servers, and network environments, especially, when there are multiple concurrent DNN operations by multiple mobile users along with multiple edge servers in a practical mobile edge computing (MEC) network. Thus, there is a need for a framework to optimize performance of multiple DNN operations with multiple mobile devices and multiple edge servers in a MEC network.

ISBN: 9798381163711Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Machine learning

Joint DNN Partitioning and Resource Allocation for Multiple Machine Learning-based Mobile Applications at the Network Edge.
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520 $a  The increasing use of artificial intelligence (AI) and machine learning (ML) in mobile applications requires stringent quality of service such as low latency and high accuracy. Deep Neural Networks (DNNs) are the core technique used in AI and ML, but they are computation-intensive and power-consuming, leading to limitations in computing capability and energy consumption on user mobile devices (MD). As a result, many AI/ML applications offload DNN processing tasks from MDs to mobile edge computing servers (ES) or cloud data centers. However, this approach puts significant computational pressure on the servers and requires significant amounts of data to be transmitted over wireless networks. DNNs consist of multiple connected layers of neurons, and the computation complexity and amount of intermediate output data vary at each layer. Partitioning the DNN inference operations between mobile devices and edge/cloud servers based on the DNN layer characteristics can significantly reduce computation pressure, power, and data rate requirements at the mobile device, while improving overall operation efficiency, latency, accuracy, and privacy, that is, the optimal DNN partitioning point and offloading strategy should depend on the computation and output data size characteristics of the layer compositions, available resources at the mobile devices and servers, and network environments, especially, when there are multiple concurrent DNN operations by multiple mobile users along with multiple edge servers in a practical mobile edge computing (MEC) network. Thus, there is a need for a framework to optimize performance of multiple DNN operations with multiple mobile devices and multiple edge servers in a MEC network.
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