A learning-based framework for optimizing service migration in mobile edge clouds

Florian Brandherm, Lin Wang, Max Mühlhäuser

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationEdgeSys 2019 - Proceedings of the 2nd ACM International Workshop on Edge Systems, Analytics and Networking, Part of EuroSys 2019
PublisherAssociation for Computing Machinery, Inc
Pages12-17
Number of pages6
ISBN (Electronic)9781450362757
DOIs
Publication statusPublished - 25 Mar 2019
Event2nd ACM International Workshop on Edge Systems, Analytics and Networking, EdgeSys 2019, Part of EuroSys 2019 - Dresden, Germany
Duration: 25 Mar 201925 Mar 2019

Conference

Conference2nd ACM International Workshop on Edge Systems, Analytics and Networking, EdgeSys 2019, Part of EuroSys 2019
CountryGermany
CityDresden
Period25/03/1925/03/19

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