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 close-to-optimal reconfiguration sequences 10X faster than existing approaches.

5G networks leverage network slices for serving use cases with different and potentially conflicting requirements. Current approaches for composing network slices largely overlook the presence of multiple ingress and egress nodes in each network slice. This results in inefficient resource usage even when co-locating the Virtual Network Functions (VNFs) of a slice. The network is thus quickly saturated, and no more use cases can be served. We address this issue by introducing μslices, where each μslice is tied to a specific pair of ingress and egress nodes of a slice. Then, we optimize the bandwidth consumed by each μslice by co-locating its VNF instances that communicate the most. We observed that enabling μslicing was able to save about two times more control and data plane traffic and it also resulted in a more even distribution of computational and link load.

Mobile networks are expected to serve a wide range of verticals, however the Long Term Evolution (LTE) network is optimized for basic mobile operator services only. The network functions serving LTE networks are largely implemented as dedicated single function devices that offer poor customization options. This intrinsic inflexibility makes current LTE networks unable to meet the requirements of future mobile networks. For example, LTE networks experience signaling storms because the signals exchanged by the network functions cannot be optimized according to the current usage pattern of mobile services. Modularizing these network functions would enable a refactoring of the LTE network, allowing operators to compose networks that adapt and evolve with the influx of verticals. In this article, we present a new approach for refactoring the network functions serving LTE networks which can be leveraged to compose a modular mobile network optimized for the verticals using its services. As an example, we demonstrate that deploying network functions at the edge significantly reduces the signals exchanged within a mobile network.

Recent research has proposed swarming protocols as a possible approach to offload the Internet infrastructure when some content can be shared by several users. However, simulations have been generally used as experimental means. Instead, we present an application platform that allows a rapid development and testing of swarming protocols using off-the-shelf smartphones.