User-centric services proliferated by the smart devices is getting more demanding and characteristically diversified. Fall-risk assessment, augmented reality and similar services coexist in a shared heterogeneous setting. To meet the diversified and often conflicting requirements of the services, the physical network is decomposed into virtual slices. In order to address the optimal network slicing problem for various service types with different performance requirements, this study proposes a Mixed Integer Programming (MIP) model. This optimization model aims to satisfy the demands of the services and provide complete isolation among them through virtual resources reservation, including both networking and computation. Additionally, a heuristic implementation NESECS (NEtwork Slicing for Edge Computing Services) is proposed as an efficient solution for the cases where the optimization tools remain inadequate. The performance of the proposed solutions is evaluated with an extensive set of experiments. The obtained results indicate that the proposed optimization model is capable of providing optimal or near optimal solutions for small network instances, and NESECS algorithm can provide good solutions for larger instances by eliminating the time complexity.

中文翻译：

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用于边缘计算优化的端到端网络切片

智能设备带来的以用户为中心的服务要求越来越高，而且呈现出多样化的特征。跌倒风险评估、增强现实和类似服务共存于共享的异构环境中。为了满足业务多样化且经常相互冲突的需求，物理网络被分解为虚拟切片。为了解决具有不同性能要求的各种服务类型的最优网络切片问题，本研究提出了混合整数规划（MIP）模型。该优化模型旨在满足服务的需求，并通过虚拟资源预留（包括网络和计算）提供服务之间的完全隔离。此外，还提出了一种启发式实施NESECS（边缘计算服务网络切片）作为优化工具仍然不足的情况的有效解决方案。所提出解决方案的性能通过大量实验进行评估。所得结果表明，所提出的优化模型能够为小型网络实例提供最优或接近最优的解决方案，而NESECS算法可以通过消除时间复杂度为较大的实例提供良好的解决方案。