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.

中文翻译：

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利用惩罚感知深度强化学习对 DCI-EON 中的多维资源进行动态切片

随着动态云计算服务需求的不断增长，基于弹性光网络（DCI-EON）的数据中心互连需要高效的频谱、接入IP带宽和计算资源的分配方法。 DCI-EON 中多维资源的动态切片已成为一种有前景的解决方案。然而，资源重新分配不当可能会削弱切片重新配置的好处，并且不同的资源重新配置技术可能会导致现有服务不同程度的服务降级。在本文中，我们提出了一种基于预测的动态切片方法（DS-DRL-RW），该方法利用惩罚感知深度强化学习（DRL）来优化资源分配，同时考虑切片重新配置的收益和惩罚之间的权衡。 DS-DRL-RW采用统计预测来获得不区分多维资源的动态切片的粗粒度解决方案。随后，通过基于粗粒度解决方案的集中DRL训练，获得了多维资源切片的准确结果。此外，DS-DRL-RW综合考虑了片重配置后与不同重配置技术相关的利弊，从而能够确定合适的重配置策略。仿真结果表明，DS-DRL-RW通过将分片流量预测与DRL相结合，提高了训练效率，降低了动态业务的阻塞率。它有效解决了重新配置带来的直接惩罚和资源浪费带来的间接惩罚，从而提高了多维资源利用率。 DS-DRL-RW 有效地处理与各种重新配置技术相关的各种惩罚，并选择适当的重新配置策略。此外，DS-DRL-RW优先考虑片内服务的不同质量要求，例如完成时间，以避免服务降级。