Publication
Title
Resource allocation of multi-user workloads in cloud and edge data-centers using reinforcement learning
Author
Abstract
Cloud and edge Data-center (DC) are designed to allocate computing resources dynamically to users based on the agreed Service Level Agreement (SLA). However, the ever-increasing demand for beyond 5G services necessitates an efficient workload management. A key challenge in this regard is auto-scaling, a dynamic process that adjusts computing resources to meet fluctuating system demands, optimizing resource utilization and cost efficiency. Traditional auto-scaling algorithms, which rely on fixed thresholds or control-theory, may face limitations in modern DC which are characterized by diverse, dynamic, and multi-user workloads. In this paper, we propose a Reinforcement Learning (RL)-based controller that extends the capacity of the state-of-the-art RL-based auto-scalers to the multi-user workload scenario. We compare the proposed RL agent against the well-known Proportional-Integral (PI) controller and a Threshold (THD)-based controller in a multi-user workload scenario in terms of created Cloud-native Network Functions (CNFs) and peak latency performed in a discrete event simulator.
Language
English
Source (journal)
International Conference on Network and Service Management : [proceedings]. - Piscataway, NJ
Source (book)
19th International Conference on Network and Service Management (CNSM) -, Network and Service Management in the Era of Generative AI and Digital, Twins, OCT 30-NOV 02, 2023, Niagara Falls, Canada
Publication
New York, N.Y. : IEEE , 2023
ISBN
978-3-903176-59-1
DOI
10.23919/CNSM59352.2023.10327797
Volume/pages
(2023) , 5 p.
ISI
001117985100006
Full text (Publisher's DOI)
Full text (open access)
The author-created version that incorporates referee comments and is the accepted for publication version Available from 28.05.2024
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Project info
IMEC-Network intelligence for adaptive and self-learning mobile networks (DAEMON).
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 29.03.2024
Last edited 03.05.2024
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