Kanakis, Marios Evangelos ; Khalili, Ramin ; Wang, Lin (2022)
Machine Learning for Computer Systems and Networking: A Survey.
In: ACM Computing Surveys, (Early Access)
doi: 10.1145/3523057
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Machine learning has become the de-facto approach for various scientific domains such as computer vision and natural language processing. Despite recent breakthroughs, machine learning has only made its way into the fundamental challenges in computer systems and networking recently. This paper attempts to shed light on recent literature that appeals for machine learning based solutions to traditional problems in computer systems and networking. To this end, we first introduce a taxonomy based on a set of major research problem domains. Then, we present a comprehensive review per domain, where we compare the traditional approaches against the machine learning based ones. Finally, we discuss the general limitations of machine learning for computer systems and networking, including lack of training data, training overhead, real-time performance, and explainability, and reveal future research directions targeting these limitations.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Kanakis, Marios Evangelos ; Khalili, Ramin ; Wang, Lin |
| Art des Eintrags: | Bibliographie |
| Titel: | Machine Learning for Computer Systems and Networking: A Survey |
| Sprache: | Englisch |
| Publikationsjahr: | 9 März 2022 |
| Ort: | New York, NY, USA |
| Verlag: | ACM |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | ACM Computing Surveys |
| (Heft-)Nummer: | Early Access |
| DOI: | 10.1145/3523057 |
| URL / URN: | https://dl.acm.org/doi/abs/10.1145/3523057 |
| Kurzbeschreibung (Abstract): | Machine learning has become the de-facto approach for various scientific domains such as computer vision and natural language processing. Despite recent breakthroughs, machine learning has only made its way into the fundamental challenges in computer systems and networking recently. This paper attempts to shed light on recent literature that appeals for machine learning based solutions to traditional problems in computer systems and networking. To this end, we first introduce a taxonomy based on a set of major research problem domains. Then, we present a comprehensive review per domain, where we compare the traditional approaches against the machine learning based ones. Finally, we discuss the general limitations of machine learning for computer systems and networking, including lack of training data, training overhead, real-time performance, and explainability, and reveal future research directions targeting these limitations. |
| Freie Schlagworte: | machine learning, computer networking, computer systems |
| Zusätzliche Informationen: | Just Accepted |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen > Teilprojekt B2: Koordination und Ausführung |
| Hinterlegungsdatum: | 16 Aug 2022 08:14 |
| Letzte Änderung: | 05 Dez 2022 14:04 |
| PPN: | 502290749 |
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