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A Learning-based Framework for Optimizing Service Migration in Mobile Edge Clouds

Brandherm, Florian ; Wang, Lin ; Mühlhäuser, Max (2019)
A Learning-based Framework for Optimizing Service Migration in Mobile Edge Clouds.
EdgeSys. Dresden (25.03.2019-25.03.2019)
doi: 10.1145/3301418.3313939
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Mobile edge computing is gaining traction due to its ability to deliver ultra-low-latency services for mobile applications. This is achieved through a federation of edge clouds in close proximity of users. However, the intrinsic mobility of users brings a high level of dynamics to the edge environment, calling for sophisticated service migration management across the edge clouds. Previous solutions for edge service placement/migration are architecture-specific, centralized, or are based on restricted cost models. These limitations leave doubts about the practicality of these approaches due to the lack of a standardized reference model for edge clouds. In this paper, we propose a general framework for optimizing edge service migration based on reinforcement learning techniques. Using our framework, edge service migration strategies can be learned with respect to a large variety of optimization goals. Moreover, our learning-based algorithm is agnostic to the underlying architecture and resource constraints. Preliminary results show that our model-free learning-based approach can compete with model-based baselines and adapt to different objectives.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2019
Autor(en): Brandherm, Florian ; Wang, Lin ; Mühlhäuser, Max
Art des Eintrags: Bibliographie
Titel: A Learning-based Framework for Optimizing Service Migration in Mobile Edge Clouds
Sprache: Englisch
Publikationsjahr: 25 März 2019
Verlag: ACM
Buchtitel: Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking
Veranstaltungstitel: EdgeSys
Veranstaltungsort: Dresden
Veranstaltungsdatum: 25.03.2019-25.03.2019
DOI: 10.1145/3301418.3313939
Kurzbeschreibung (Abstract):

Mobile edge computing is gaining traction due to its ability to deliver ultra-low-latency services for mobile applications. This is achieved through a federation of edge clouds in close proximity of users. However, the intrinsic mobility of users brings a high level of dynamics to the edge environment, calling for sophisticated service migration management across the edge clouds. Previous solutions for edge service placement/migration are architecture-specific, centralized, or are based on restricted cost models. These limitations leave doubts about the practicality of these approaches due to the lack of a standardized reference model for edge clouds. In this paper, we propose a general framework for optimizing edge service migration based on reinforcement learning techniques. Using our framework, edge service migration strategies can be learned with respect to a large variety of optimization goals. Moreover, our learning-based algorithm is agnostic to the underlying architecture and resource constraints. Preliminary results show that our model-free learning-based approach can compete with model-based baselines and adapt to different objectives.

Freie Schlagworte: C7
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 > A: Konstruktionsmethodik
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik > Teilprojekt A1: Modellierung
Hinterlegungsdatum: 29 Apr 2019 06:22
Letzte Änderung: 04 Sep 2019 05:31
PPN:
Projekte: CollaborativeResearch Center (CRC) 1053—MAKI
Sponsoren: DFG
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