论文标题
PSACCF:考虑5G/B5G网络中公平性的在线切片入学控制
PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks
论文作者
论文摘要
5G/B5G设想在网络切片的协助下支持各种服务,每个切片实例都要求提供足够的资源,以向其订户提供预先谈判的服务质量。切片录取控制(SAC)算法是切片提供商(SPS)必须保证每个接收请求的QoS和QoE的必要性。在这种情况下,服务的优先关注和资源分配的公平性是对研究人员的有意义的主题。前者源自5G网络支持的各种服务的先天特征,而后者很重要,因为切片是在共享物理设备上实例化的。但是,这两个问题主要在文献中分别研究,或者没有同时接受足够的研究。在这项工作中,我们研究了5G/B5G网络中的SAC问题,旨在在满足必要的优先级要求的前提下增强公平程度。我们首先将优先级重新解释为较高的累积服务接受率(CSAR),并采用相邻的CSAR差距的统一性来反映公平性。基于这些调整,SAC问题被表述为非线性和非凸多目标优化。因此,我们提出了一种启发式算法,称为“公平性”(PSACCF)来解决它,称为优先级入院控制。它介绍了服务的资源效率以修改优先级违规,然后通过为每种服务类型设定目标CSAR并将其实际的CSAR推向目标,从而促进公平性。进行了许多模拟,以将PSACCF的性能与称为MHPF和AHPF的两种现有算法进行比较。结果表明,我们的算法可以与比较达到几乎相同的优先指标,并且公平程度至少提高了33.6%,最低平均资源利用率提高了33.6%。
5G/B5G is envisioned to support various services with the assistance of network slices, each slice instance asks for adequate resources to provide the pre-negotiated service quality to its subscribers. Slice Admission Control (SAC) algorithm is a necessity for Slice Providers (SPs) to guarantee the QoS and QoE of each admitted request with limited resources. In that circumstance, the priority concern of services and the fairness of resource allocation arise as meaningful topics for researchers. The former originates from the innate characteristics of various services supported by 5G networks, and the latter matters because slices are instantiated on shared physical equipment. However, the two issues are mainly investigated separately in the literature or do not receive sufficient research simultaneously. In this work, we study the SAC problem in 5G/B5G networks, aiming at enhancing the fairness degree on the premise of satisfying the necessary priority requirements. We first reinterpret priority as a higher cumulative service acceptance ratio (CSAR), and adopt the uniformity of adjacent CSAR gaps to reflect the fairness. Based on these adjustments, the SAC problem is formulated as a non-linear and non-convex multi-objective optimization. Thus, we propose a heuristic algorithm called Prioritized Slice Admission Control Considering Fairness (PSACCF) to solve it. It introduces the resource efficiency of services to amend priority violations, then promotes fairness by setting the target CSARs for each service type and pushing their actual CSARs toward. Numerous simulations are carried out to compare the performance of PSACCF with two existing algorithms, termed MHPF and AHPF. Results show that our algorithm can achieve a nearly identical priority indicator to the comparisons, as well as at least a 33.6% improvement in fairness degree and a higher minimum average resource utilization.