A QoE-Aware Strategy for Supporting Service Continuity in an MCC Environment

Haung, Yieh-Ran

doi:10.1007/s11277-020-07731-2

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…The authors in [19] propose a quality-of-experience (QoE)aware scheme to ensure service continuity for mobile cloud computing environment. The scheme relays on the bufferoccupancy threshold policy that classifies the new arriving request from the mobile users.…”

Section: Related Workmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks

Abouaomar¹,

Mlika²,

Filali³

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MECenabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance.

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Section: Related Workmentioning

confidence: 99%