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المؤلفMakhlouf, Ahmed
المؤلفAbdellatif, Alaa Awad
المؤلفBadawy, Ahmed
المؤلفMohamed, Amr
تاريخ الإتاحة2023-11-09T06:28:03Z
تاريخ النشر2023-01-01
اسم المنشورInternational Conference on Wireless and Mobile Computing, Networking and Communications
المعرّفhttp://dx.doi.org/10.1109/WiMob58348.2023.10187766
الاقتباسMakhlouf, A., Abdellatif, A. A., Badawy, A., & Mohamed, A. (2023, June). Optimized Resource and Deep Learning Model Allocation in O-RAN Architecture. In 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 155-160). IEEE.‏
الترقيم الدولي الموحد للكتاب 9798350336672
الرقم المعياري الدولي للكتاب21619646
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85167621483&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/49122
الملخصIn the era of 5G and beyond, telecommunication networks tend to move Radio Access Network (RAN) from centralized architecture to a more distributed architecture for greater interoperability and flexibility. Open RAN (O-RAN) architecture is a paradigm shift that is proposed to enable disaggregation, virtualization, and cloudification of RAN components, possibly offered from multiple vendors, to be connected through open interfaces. Leveraging this O-RAN architecture, Deep Learning (DL) models may be running as a service close to the end users, rather than on the core network, to benefit from reduced latency and bandwidth consumption. If multiple DL models learn on the virtual edge, they will compete for the available communication and computation resources. In this paper, we introduce Optimized Resource and Model Allocation (ORMA), a framework that provides optimized resource allocation for multiple DL models learning at the edge, that aims to maximize the aggregate accuracy while respecting the limited physical resources. Distinguished from related works, ORMA optimizes the learning-related parameters, such as dataset size and number of epochs, as well as the amount of communication and computation resources allocated to each DL model to maximize the aggregate accuracy. Our results show that ORMA consistently outperforms a baseline approach that adopts a fixed, fair resource allocation (FRA) among different DL models, at different total bandwidths and CPU combinations.
اللغةen
الناشرIEEE Computer Society
الموضوعNetwork Slicing
Open-RAN
Optimization
Resource Allocation
Virtual Edge
العنوانOptimized Resource and Deep Learning Model Allocation in O-RAN Architecture
النوعConference Paper
الصفحات155-160
رقم المجلد2023-June
dc.accessType Abstract Only


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