Recursive cubes of rings as models for interconnection networks

We study recursive cubes of rings as models for interconnection networks. We first redefine each of them as a Cayley graph on the semidirect product of an elementary abelian group by a cyclic group in order to facilitate the study of them by using algebraic tools. We give an algorithm for computing shortest paths and the distance between any two vertices in recursive cubes of rings, and obtain the exact value of their diameters. We obtain sharp bounds on the Wiener index, vertex-forwarding index, edge-forwarding index and bisection width of recursive cubes of rings. The cube-connected cycles and cube-of-rings are special recursive cubes of rings, and hence all results obtained in the paper apply to these well-known networks

Methods and problems of wavelength-routing in all-optical networks

We give a survey of recent theoretical results obtained for wavelength-routing in all-optical networks. The survey is based on the previous survey in [Beauquier, B., Bermond, J-C., Gargano, L., Hell, P., Perennes, S., Vaccaro, U.: Graph problems arising from wavelength-routing in all-optical networks. In: Proc. of the 2nd Workshop on Optics and Computer Science, part of IPPS'97, 1997]. We focus our survey on the current research directions and on the used methods. We also state several open problems connected with this line of research, and give an overview of several related research directions

Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform

In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.Comment: Accepted for publication in IEEE Sensors Journal. A video is available on https://youtu.be/IR5NnZvZBL

Rethinking Software Network Data Planes in the Era of Microservices

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Framework for eBPF-Based Network Functions in an Era of Microservices

By moving network functionality from dedicated hardware to software running on end-hosts, Network Functions Virtualization (NFV) pledges the benefits of cloud computing to packet processing. While most of the NFV frameworks today rely on kernel-bypass approaches, no attention has been given to kernel packet processing, which has always proved hard to evolve and to program. In this article, we present Polycube, a software framework whose main goal is to bring the power of NFV to in-kernel packet processing applications, enabling a level of flexibility and customization that was unthinkable before. Polycube enables the creation of arbitrary and complex network function chains, where each function can include an efficient in-kernel data plane and a flexible user-space control plane with strong characteristics of isolation, persistence, and composability. Polycube network functions, called Cubes, can be dynamically generated and injected into the kernel networking stack, without requiring custom kernels or specific kernel modules, simplifying the debugging and introspection, which are two fundamental properties in recent cloud environments. We validate the framework by showing significant improvements over existing applications, and we prove the generality of the Polycube programming model through the implementation of complex use cases such as a network provider for Kubernetes