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BigMEC: Scalable Service Migration for Mobile Edge Computing

Brandherm, Florian ; Gedeon, Julien ; Abboud, Osama ; Mühlhäuser, Max (2022)
BigMEC: Scalable Service Migration for Mobile Edge Computing.
7th ACM/IEEE Symposium on Edge Computing. Seattle, USA (05.-08.12.2022)
doi: 10.1109/SEC54971.2022.00018
Conference or Workshop Item, Bibliographie

Abstract

The proximity of Mobile Edge Computing offers the potential for offloading low latency closed-loop applications from mobile devices. However, to repair decreases in quality of service (QoS), e.g., resulting from user mobility, the placement of service instances must be continually updated – essential for mission critical applications that cannot tolerate decreased QoS, for example virtual reality or networked control systems. This paper presents BigMEC, a decentralized service placement algorithm that achieves scalable, fast, and high-quality placements by making local service migration decisions immediately when a drop in QoS is detected. The algorithm relies on reinforcement learning to adapt to unknown scenarios and to approximate long-term optimal placement updates by taking future transition costs into account. BigMEC limits each decentralized migration decision to nearby edge sites. Thus, decision computation times are independent of the number of nodes in the network and well below 10ms in our experimental setup. Our ablation study validates that, using its scalable approach to decentralized resource conflict resolution, BigMEC quickly approaches optimal placement with increasing local view size, and that it can reliably learn to approximate long-term optimal migration decisions, given only a black-box optimization objective.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Brandherm, Florian ; Gedeon, Julien ; Abboud, Osama ; Mühlhäuser, Max
Type of entry: Bibliographie
Title: BigMEC: Scalable Service Migration for Mobile Edge Computing
Language: English
Date: 6 December 2022
Publisher: IEEE
Book Title: The Seventh ACM/IEEE Symposium on Edge Computing
Event Title: 7th ACM/IEEE Symposium on Edge Computing
Event Location: Seattle, USA
Event Dates: 05.-08.12.2022
DOI: 10.1109/SEC54971.2022.00018
Corresponding Links:
Abstract:

The proximity of Mobile Edge Computing offers the potential for offloading low latency closed-loop applications from mobile devices. However, to repair decreases in quality of service (QoS), e.g., resulting from user mobility, the placement of service instances must be continually updated – essential for mission critical applications that cannot tolerate decreased QoS, for example virtual reality or networked control systems. This paper presents BigMEC, a decentralized service placement algorithm that achieves scalable, fast, and high-quality placements by making local service migration decisions immediately when a drop in QoS is detected. The algorithm relies on reinforcement learning to adapt to unknown scenarios and to approximate long-term optimal placement updates by taking future transition costs into account. BigMEC limits each decentralized migration decision to nearby edge sites. Thus, decision computation times are independent of the number of nodes in the network and well below 10ms in our experimental setup. Our ablation study validates that, using its scalable approach to decentralized resource conflict resolution, BigMEC quickly approaches optimal placement with increasing local view size, and that it can reliably learn to approximate long-term optimal migration decisions, given only a black-box optimization objective.

Uncontrolled Keywords: mobile edge computing, service migration, reinforcement learning, distributed algorithms
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
TU-Projects: DFG|SFB1053|SFB1053 TPA01 Mühlhä
Date Deposited: 05 Dec 2022 09:00
Last Modified: 15 Mar 2024 07:42
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