國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Joint DNN Partitioning and Resource Allocation for Multiple Machine Learning-based Mobile Applications at the Network Edge./
作者:	Cheng, Cheng-Yu.
面頁冊數:	1 online resource (103 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (103 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381163711Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

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