核心网络切片通过虚拟网络功能(VNF)的组合链接实现灵活组网. 软件故障和硬件故障均会导致VNF失效, 从而导致切片服务中断. 由于网络切片共享资源, 需要特定的隔离机制以满足切片健壮性需求. 现有的可用性保障机制大多面向随机VNF故障, 一些涉及外部攻击的研究很少考虑网络切片特殊的隔离需求. 为了在隔离机制下实现切片可用性保障, 提出一种基于多级隔离的网络切片可用性保障方法. 首先, 建立核心网切片资源感知的可用性保障问题模型, 旨在满足隔离需求的同时, 消耗最少的备份资源来达到可用性目标. 然后, 提出一种隔离级别评估模型对VNF的隔离级别进行评估. 最后, 提出一种基于多级隔离的备份算法MLIBA解决所提出的可用性保障问题. 此外, 针对共享备份可用性计算这一PP-complete问题, 提出一种基于等效备份实例的计算方法. 仿真结果表明, 所提可用性计算方法具有较高的准确性, 引入多级隔离的可用性保障方法可以使切片的健壮性提高一倍. 与现有研究的对比表明, 在相同的隔离约束和可用性目标下, 所提方法可减少20%–70%资源消耗, 提高5%–30%的有效资源占比.
Core network slicing achieves flexible networking by combining virtualized network functions (VNFs). However, the failure of any VNF due to software and hardware failures will cause an interruption of the slice service. Since network slices share resources, a specific isolation mechanism is required to meet slice robustness demands. Most of the existing availability guarantee mechanisms focus on random VNF failures, and some of them involving external attacks rarely consider special isolation requirements of network slices. To realize slice availability guarantee under isolation mechanisms, this study proposes a method to guarantee network slice availability based on multi-level isolation. First, an availability guarantee model of core network resource awareness is built to meet the isolation requirements with consuming the least number of backup resources. Then, an isolation level assessment model is proposed to evaluate the isolation level of VNFs. Finally, a multi-level isolated backup algorithm (MLIBA) is proposed to solve the availability guarantee problem. In addition, an equivalent backup instance-based calculation method is put forward to address the PP-complete problem of availability calculation for a shared backup. Simulation results show that the proposed availability calculation method has high accuracy, and the introduction of multi-level isolation can double the robustness of slices. The comparison with existing studies shows that under the same isolation constraints and availability targets, the proposed method can reduce resource consumption by 20%–70% and increase the proportion of effective resources by 5%–30%.