797472bd060fb38076d9d2698ca43dde pmc_34960400.pdf 1665637725cf84677998e6de9d891477681f6f57 pmc_34960400.pdf 098b7ef03e77a1b9fb5f5a163bd7548885f606b8e22945601518c035a954b2f6 pmc_34960400.pdf Title: Packet Flow Capacity Autonomous Operation Based on Reinforcement Learning Subject: As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, particularly Reinforcement Learning, seems to be an enabler for autonomicity as a result of its inherent capacity to learn from experience. However, precisely because of that, RL methods might not be able to provide the required performance (e.g., delay, packet loss, and capacity overprovisioning) when managing the capacity of packet flows, until they learn the optimal policy. In view of that, we propose a management lifecycle with three phases: (i) a self-tuned threshold-based approach operating just after the packet flow is set up and until enough data on the traffic characteristics are available; (ii) an RL operation based on models pre-trained with a generic traffic profile; and (iii) an RL operation with models trained for real traffic. Exhaustive simulation results confirm the poor performance of RL algorithms until the optimal policy is learnt and when traffic characteristics change over time, which prevents deploying such methods in operators’ networks. In contrast, the proposed lifecycle outperforms benchmarking approaches, achieving noticeable performance from the beginning of operation while showing robustness against traffic changes. Keywords: reinforcement learning; autonomous network operation; offline/online learning Author: Sima Barzegar, Marc Ruiz and Luis Velasco Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Dec 14 10:36:26 2021 CET ModDate: Tue Dec 14 10:41:38 2021 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 24 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 16026891 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HAAEVE+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 QJUKVG+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 HEZQKU+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 FYDUZL+VnURWPalladioL Type 1 Custom yes yes no 23 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 32 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 54 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom 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Identity-H yes yes yes 921 0 ABCDEE+Calibri TrueType WinAnsi yes yes no 928 0 ABCDEE+Calibri,Bold TrueType WinAnsi yes yes no 932 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-04-01 02:31:56 CEST RepresentationInformation: pmc_34960400.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-03-31 12:08:12 CEST Size: 16026891 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 1091 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Children: Item: Title: Background on RL methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Item: Title: Related Work Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Item: Title: Contributions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: The Flow Capacity Autonomous Operation (CRUX) Problem Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Children: Item: Title: Flow Capacity Autonomous Operation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Proposed Architecture Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: CRUX Problem Definition and RL Methodology Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Children: Item: Title: Problem Definition and Basic Modeling Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Generic RL-Based Methodology Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Specific Adaption of RL Approaches Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Cycles for Robust RL Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Illustrative Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Children: Item: Title: Online RL-Based Operation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Offline Leaning + Online RL-Based Operation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: Concluding Remarks Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@3aeaafa6 Info: Title: Packet Flow Capacity Autonomous Operation Based on Reinforcement Learning Author: Sima Barzegar, Marc Ruiz and Luis Velasco Subject: As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators is the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, particularly Reinforcement Learning, seems to be an enabler for autonomicity as a result of its inherent capacity to learn from experience. However, precisely because of that, RL methods might not be able to provide the required performance (e.g., delay, packet loss, and capacity overprovisioning) when managing the capacity of packet flows, until they learn the optimal policy. In view of that, we propose a management lifecycle with three phases: (i) a self-tuned threshold-based approach operating just after the packet flow is set up and until enough data on the traffic characteristics are available; (ii) an RL operation based on models pre-trained with a generic traffic profile; and (iii) an RL operation with models trained for real traffic. Exhaustive simulation results confirm the poor performance of RL algorithms until the optimal policy is learnt and when traffic characteristics change over time, which prevents deploying such methods in operators’ networks. In contrast, the proposed lifecycle outperforms benchmarking approaches, achieving noticeable performance from the beginning of operation while showing robustness against traffic changes. 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Link Rect: 304, 629, 341, 640 Checksum: bc2fae87 Type: CRC32 Checksum: 797472bd060fb38076d9d2698ca43dde Type: MD5 Checksum: 1665637725cf84677998e6de9d891477681f6f57 Type: SHA-1