부산대학교 도서관

With the increasing demand for dynamic cloud computing services, data center interconnections based on elastic optical networks (DCI-EON) require efficient allocation methods for spectrum, access IP bandwidth, and compute resources. Dynamic slicing of multidimensional resources in DCI-EON has emerged as a promising solution. However, improper reallocation of resources can diminish the benefits of slice reconfiguration, and different resource reconfiguration techniques can lead to varying degrees of service degradation for existing services. In this paper, we propose a prediction-based dynamic slicing approach (DS-DRL-RW) that leverages penalty-aware deep reinforcement learning (DRL) to optimize resource allocation while considering the trade-off between the benefits and penalties of slice reconfiguration. DS-DRL-RW employs statistical prediction to obtain a coarse-grained solution for dynamic slicing that does not differentiate among multidimensional resources. Subsequently, through focused DRL training based on the coarse-grained solution, the accurate result for multidimensional resource slicing is achieved. Moreover, DS-DRL-RW comprehensively considers the benefits and penalties associated with different reconfiguration techniques after slice reconfiguration, enabling the determination of a suitable reconfiguration strategy. Simulation results demonstrate that DS-DRL-RW improves training efficiency and reduces the blocking rate of dynamic services by integrating slice traffic prediction and DRL. It effectively addresses both direct penalties from reconfiguration and indirect penalties from resource waste, thereby enhancing multidimensional resource utilization. DS-DRL-RW effectively handles the diverse penalties associated with various reconfiguration techniques and selects the appropriate reconfiguration strategy. Furthermore, DS-DRL-RW prioritizes the different quality requirements of services in slices, such as completion time, to avoid service degradation.