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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)
doi: 10.1145/3301418.3313939
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Brandherm, Florian ; Wang, Lin ; Mühlhäuser, Max
Type of entry: Bibliographie
Title: A Learning-based Framework for Optimizing Service Migration in Mobile Edge Clouds
Language: English
Date: 25 March 2019
Publisher: ACM
Book Title: Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking
Event Title: EdgeSys
Event Location: Dresden
Event Dates: 25.03.2019
DOI: 10.1145/3301418.3313939
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.

Uncontrolled Keywords: C7
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology > Subproject A1: Modelling
Date Deposited: 29 Apr 2019 06:22
Last Modified: 04 Sep 2019 05:31
PPN:
Projects: CollaborativeResearch Center (CRC) 1053—MAKI
Funders: DFG
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