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Title
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Resource allocation of multi-user workloads in cloud and edge data-centers using reinforcement learning
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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International Conference on Network and Service Management : [proceedings]. - Piscataway, NJ
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Source (book)
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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
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Publication
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New York, N.Y.
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IEEE
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2023
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ISBN
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978-3-903176-59-1
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DOI
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10.23919/CNSM59352.2023.10327797
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Volume/pages
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(2023)
, 5 p.
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ISI
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001117985100006
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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