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

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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 '19
Subtitle of host publicationProceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking
PublisherAssociation for Computing Machinery, Inc
Pages12-17
Number of pages6
ISBN (Electronic)9781450362757
DOIs
Publication statusPublished - 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
Country/TerritoryGermany
CityDresden
Period25/03/1925/03/19

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