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Fog computing emerges as a great candidate to mitigate the unsolved problems of cloud computing. Data migration between fog nodes and datacenters is placing huge bandwidth requirements on substrate optical networks. To increase network capacity and to improve network flexibility, broad attention has been given to flexible-grid technology. This paper addresses the issue of how to migrate substrate optical networks from the fixed-grid network era to the flexible-grid network era gradually, due to the unbearable capital expenditure and service stability. First, we model the network migration and elucidate the upgrade probability by calculating the node demands for upgrading. Then, based on the upgrade probability, three schemes are proposed to construct Potential Upgrade Nodes Group (PUNG). Besides, to maintain the traffic stability, a Migration-aware Service Provisioning (MSP) scheme is proposed based on PUNG. Numerical results illustrate that the proposed MSP scheme can effectively enhance the traffic stability compared to Non-migration-aware Service Provisioning (NSP) scheme, and the connection interrupted ratio is excessively reduced. INDEX TERMS Measurable migration, flexible optical networks, potential upgrade nodes group (PUNG), migration-aware service provisioning (MSP) scheme.

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We propose a novel energy efficient traffic grooming algorithm based on deep reinforcement learning in fog-cloud elastic optical networks. Simulation results show that it can achieve much lower energy consumption than the state-of-art algorithm.