On Reconfiguring 5G Network Slices

Abstract

The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration satisfies the Service-Level Objectives (SLOs) without overloading the substrate. Reconfiguring a network from a given source configuration to the desired target configuration involves identifying an ordered sequence of feasible configurations from the source to the target. The proposed solutions for finding such a sequence are optimized for data centers and cannot be used as-is for reconfiguring 5G network slices. We present Matryoshka, our divide-and-conquer approach for finding a sequence of feasible configurations that can be used to reconfigure 5G network slices. Unlike previous approaches, Matryoshka also considers the bandwidth and latency constraints between the network functions of network slices. Evaluating Matryoshka required a dataset of pairs of source and target configurations. Because such a dataset is currently unavailable, we analyze proof of concept roll-outs, trends in standardization bodies, and research sources to compile an input dataset. On using Matryoshka on our dataset, we observe that it yields closeto-optimal reconfiguration sequences 10X faster than existing approaches.

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

“…At present, most studies on SFC resource configuration adjustment are movitated by the changs of SFC resource demands. The service interruption time is long, and it is hard to meet real needs [23]. Predicting the dynamic changes of network traffic, determining the dynamic adjustment demand of SFC resource configuration, and carrying out the SFC dynamic adjustment in advance, can effectively deal with the degradation of QoS.…”

Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies

“…The network slicing paradigm can be used to define entire network slices to cover the elastic demand for network resources through the day and according to different operating and customer requirements such as bandwidth or a desired reliability level for the customers [29]. The reconfiguration, scaling, and migration of virtual resources (e.g., virtual machines) needed for the dynamic operation of mobile networks corresponding to customer-specific workloads are considerations that can also be addressed efficiently (e.g., in terms of bandwidth and latency constraints) with network slicing technology [30]. In addition, the network slicing architectural framework is flexible enough to allow discrimination in network resources allocation among slices, customers, or services according to specific operational requirements; a feature can be exploited in different scenarios and for various purposes.…”

Data-Driven Adaptive Network Resource Slicing for Multi-Tenant Networks

“…To fully leverage the benefits of network slicing in 5G, it is necessary to dynamically reconfigure slices and allocate resources in a flexible, data-driven manner. The data-driven methods adapt slices to traffic variations and channel states [2]. Data-driven adaptive network slicing enables efficient and flexible allocation of resources to better support users.…”

“…Wang et al use the 1 -norm to promote sparsity in slice reconfigurations in [9]. In [2], Pozza et al propose a heuristic divide-and-conquer approach for finding a sequence of feasible solutions to reconfigure a slice under bandwidth and latency constraints, while RAN is not considered in the formulation. Zhang et al propose a long time-scale resource reservation for a slice and a short time-scale intra-slice resource allocation in [10]; however, the formulation of [10] does not consider the slice reconfiguration cost and the resource reservation in the backhaul.…”