Service Migration Scheduling with Bandwidth Limitation against Crowd Mobility in Edge Computing Environments


IEICE TRANSACTIONS on Communications   Vol.E104-B    No.3    pp.240-250
Publication Date: 2021/03/01
Publicized: 2020/09/11
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2020NVP0003
Type of Manuscript: Special Section PAPER (Special Section on Fusion of Network Virtualization/Softwarization and Artificial Intelligence towards Beyond-5G Innovative IoT Services)
Category: Network
edge computing,  mobility,  service migration,  network congestion,  

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Edge computing offers computing capability with ultra-low response times by leveraging servers close to end-user devices. Due to the mobility of end-user devices, the latency between the servers and the end-user devices can become long and the response time might become unacceptable for an application service. Service (container) migration that follows the handover of end-user devices retains the response time. Service migration following the mass movement of people in the same geographic area and at the same time due to an event (e.g., commuting) generates heavy bandwidth usage in the mobile backhaul network. Heavy usage by service migration reduces available bandwidth for ordinary application traffic in the network. Shaping the migration traffic limits the bandwidth usage while delaying service migration and increasing the response time of the container for the moving end-user device. Furthermore, targets of migration decisions increase (i.e., the system load) because delaying a migration process accumulates containers waiting for migration. In this paper, we propose a migration scheduling method to control bandwidth usage for migration in a network and ensure timely processing of service migration. Simulations that compare the proposal with state-of-the-art methods show that the proposal always suppresses the bandwidth usage under the predetermined threshold. The method reduced the number of containers exceeding the acceptable response time up to 40% of the compared state-of-the-art methods. Furthermore, the proposed method minimized the targets of migration decisions.