Network Slicing (NS) is recognized as a key technology for the 5G network in providing tailored network services towards various types of verticals over a shared physical infrastructure. It offers the flexibility of on-demand provisioning of diverse services based on tenants' requirements in a dynamic environment. In this work, we focus on two important issues related to 5G Core slices: the deployment and the reconfiguration of 5G Core NSs. Firstly, for slice deployment, balancing the workloads of the underlying network is beneficial in mitigating resource fragmentation for accommodating the future unknown network slice requests. In this vein, we formulate a load-balancing oriented 5G Core NS deployment problem through an Integer Linear Program (ILP) formulation. Further, for slice reconfiguration, we propose a reactive strategy to accommodate a rejected NS request by reorganizing the already-deployed NSs. Typically, the NS deployment algorithm is reutilized with slacked physical resources to find out the congested part of the network, due to which the NS is rejected. Then, these congested physical nodes and links are reconfigured by migrating virtual network functions and virtual links, to re-balance the utilization of the whole physical network. To evaluate the performance of deployment and reconfiguration algorithms we proposed, extensive simulations have been conducted. The results show that our deployment algorithm performs better in resource balancing, hence achieves higher acceptance ratio by comparing to existing works. Moreover, our reconfiguration algorithm improves resource utilization by accommodating more NSs in a dynamic environment.

Leveraging on Network Function Virtualization (NFV) and Software Defined Networking (SDN), network slicing (NS) is recognized as a key technology that enables the 5G Infrastructure Provider (InP) to support diversified vertical services over a shared common physical infrastructure. 5G end-to-end (E2E) NS is a logical virtual network that spans across the 5G network. Existing works on improving the reliability of the 5G mainly focus on reliable wireless communications, on the other hand, the reliability of an NS also refers to the ability of the NS system to provide continued service. Hence, in this work, we focus on enhancing the reliability of the NS to cope with physical network node failures, and we investigate the NS deployment problem to improve the reliability of the system represented by the NS. The reliability of an NS is enhanced by two means: firstly, by considering the topology information of an NS, critical virtual nodes are backed up to allow failure recovery; secondly, the embedding of the augmented NS virtual network is optimized for failure avoidance. We formulate the embedding of the augmented virtual network (AVN) to maximize the survivability of the NS system as the survivable AVN embedding (S-AVNE) problem through an Integer Linear Program (ILP) formulation. Due to the complexity of the problem, a heuristic algorithm is introduced. Finally, we conduct intensive simulations to evaluate the performance of our algorithm with regard to improving the reliability of the NS system.

Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Defined Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable flexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC traffic flow measurement data provided by INT, the framework firstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classifier to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.