Modern network services are constantly increasing their requirements in terms of bandwidth, latency and cost efficiency. To satisfy these requirements, the concept of network slicing has been introduced in the context of next-generation 5G networks. However, to successfully provision resources to slices, a complex optimization problem must be addressed to allocate resources over a cloud network, i.e., a distributed computing infrastructure interconnected through high-capacity network links. In this study, we propose two new latency and energy-aware optimization models for provisioning 5G slices in cloud networks comprising both distributed computing and network resources. The proposed approaches differ from other existing solutions since we conduct our studies with respect to the end-to-end latency. Relevant models of latency and energy consumption are proposed based on a comprehensive review of the state-of-the-art. To effectively solve those optimization problems, a configurable heuristic is also proposed and investigated over different network topologies. Performance of the proposed heuristic is compared against near-optimal solutions. Moreover, we assess the importance of matching between resource provisioning algorithms and architectural assumptions related to 5G network slices and a proper problem modeling.

现代网络服务在带宽，延迟和成本效率方面不断提高其要求。为了满足这些要求，网络切片的概念已在下一代5G网络的背景下引入。然而，为了成功地将资源提供给切片，必须解决复杂的优化问题以通过云网络（即，通过高容量网络链接互连的分布式计算基础架构）分配资源。在这项研究中，我们提出了两个新的延迟和能源感知优化模型，用于在包含分布式计算和网络资源的云网络中预配5G切片。提议的方法与其他现有解决方案不同，因为我们在端到端延迟方面进行了研究。在对现有技术进行全面回顾的基础上，提出了相关的延迟和能耗模型。为了有效解决这些优化问题，还提出了可配置的启发式方法，并在不同的网络拓扑上进行了研究。将所提出的启发式方法的性能与近乎最优的解决方案进行比较。此外，我们评估了资源供应算法与与5G网络切片有关的架构假设以及适当的问题建模之间进行匹配的重要性。