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RASM: Resource-Aware Service Migration in Edge Computing based on Deep Reinforcement Learning
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| PUBLICATION | Journal of Parallel and Distributed Computing, 2023 |
| AUTHORS | Mwasinga Lusungu Josh, Duc Tai Le, Syed M.Raza, Rajesh Challa, Moonseong Kim, Hyunseung Choo |
ABSTRACT
Abstract
Multi-access Edge Computing (MEC) paradigm allows devices to offload their intensive service tasks that require high Quality of Experience (QoE). Devices mobility forces services to migrate between MECs to maintain QoE in terms of delay. The decision on when to migrate a service requires a cost and QoE tradeoff, and destination MEC selection needs to be done upon latency and resource availability constraints to minimize migrations. To this end, we propose a novel Resource-Aware Service Migration (RASM) mechanism using Deep Q-Network (DQN) to make migration decisions by achieving tradeoff between the QoE in terms of delay and migration cost. Moreover, DQN learns the best policy for maximizing QoE by selecting the migration destination based on the MECs proximity to the device and estimated resource availability at the servers using queuing model. Results show faster convergence to optimal policy, reduced average end-to-end service delay by 27%, and smaller service rejection rate by 24% comparing to the state-of-the-art.
Multi-access Edge Computing (MEC) paradigm allows devices to offload their intensive service tasks that require high Quality of Experience (QoE). Devices mobility forces services to migrate between MECs to maintain QoE in terms of delay. The decision on when to migrate a service requires a cost and QoE tradeoff, and destination MEC selection needs to be done upon latency and resource availability constraints to minimize migrations. To this end, we propose a novel Resource-Aware Service Migration (RASM) mechanism using Deep Q-Network (DQN) to make migration decisions by achieving tradeoff between the QoE in terms of delay and migration cost. Moreover, DQN learns the best policy for maximizing QoE by selecting the migration destination based on the MECs proximity to the device and estimated resource availability at the servers using queuing model. Results show faster convergence to optimal policy, reduced average end-to-end service delay by 27%, and smaller service rejection rate by 24% comparing to the state-of-the-art.