Breather solutions for a semilinear Klein–Gordon equation on a periodic metric graph

We consider the nonlinear Klein-Gordon equation $$\partial_t^2 u(x,t) - \partial_x^2 u(x,t) + \alpha u(x,t) = \pm |u(x,t)|^{p-1}u(x,t)$$ on a periodic metric graph (necklace graph) for $p > 1$ with Kirchhoff conditions at the vertices. Under suitable assumptions on the frequency we prove the existence and regularity of infinitely many spatially localized time-periodic solutions (breathers) by variational methods. We compare our results with previous results obtained via spatial dynamics and center manifold techniques. Moreover, we deduce regularity properties of the solutions and show that they are weak solutions of the corresponding initial value problem. Our approach relies on the existence of critical points for indefinite functionals, the concentration compactness principle, and the proper set-up of a functional analytic framework. Compared to earlier work for breathers using variational techniques, a major improvement of embedding properties has been achieved. This allows in particular to avoid all restrictions on the exponent $p > 1$ and to achieve higher regularity

Impairment-Aware Design of Translucent DWDM Networks Based on the k-Path Connectivity Graph

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Enhancing cross layer monitoring on open optical transport networks

Continuous monitoring of key network elements is instrumental in intelligent control and predictive analysis. This demonstration illustrates implementation challenges that are encountered in cross-layer monitoring of optical transport networks in an open-source network operations platform

Traffic-Profile and Machine Learning Based Regional Data Center Design and Operation for 5G Network

Data center in the fifth generation (5G) network will serve as a facilitator to move the wireless communication industry from a proprietary hardware based approach to a more software oriented environment. Techniques such as Software defined networking (SDN) and network function virtualization (NFV) would be able to deploy network functionalities such as service and packet gateways as software. These virtual functionalities however would require computational power from data centers. Therefore, these data centers need to be properly placed and carefully designed based on the volume of traffic they are meant to serve. In this work, we first divide the city of Milan, Italy into different zones using K-means clustering algorithm. We then analyse the traffic profiles of these zones in the city using a network operator’s Open Big Data set. We identify the optimal placement of data centers as a facility location problem and propose the use of Weiszfeld’s algorithm to solve it. Furthermore, based on our analysis of traffic profiles in different zones, we heuristically determine the ideal dimension of the data center in each zone. Additionally, to aid operation and facilitate dynamic utilization of data center resources, we use the state of the art recurrent neural network models to predict the future traffic demands according to past demand profiles of each area

Experimental Demonstration and Results of Cross-layer Monitoring Using OpenNOP: an Open Source Network Observability Platform

Ensuring the smooth operation and optimal performance of communication networks requires continuous moni- toring of key network elements. Network operators can detect and prevent potential issues by monitoring various real-time network parameters. This paper proposes and presents results from the implementation of a cross- layer monitoring system for OpenROADM-compliant optical transport networks using an open source network observability platform called OpenNOP, and for the first time includes simultaneous optical layer and transport layer metrics. It leverages open source tools as a cost-effective and efficient solution for network monitoring and management. OpenNOP collects and analyzes data from various network layers, including physical, data link, network, and transport layers. OpenNOP can also ingest status and log information. This data is all stored in a common time-series database. The results show that OpenNOP can provide comprehensive network visibility and effective cross-layer monitoring of OpenROADM-based networks

Performance Characterization and Profiling of Chained CPU-bound Virtual Network Functions

The increased demand for high-quality Internet connectivity resulting from the growing number of connected devices and advanced services has put significant strain on telecommunication networks. In response, cutting-edge technologies such as Network Function Virtualization (NFV) and Software Defined Networking (SDN) have been introduced to transform network infrastructure. These innovative solutions offer dynamic, efficient, and easily manageable networks that surpass traditional approaches. To fully realize the benefits of NFV and maintain the performance level of specialized equipment, it is critical to assess the behavior of Virtual Network Functions (VNFs) and the impact of virtualization overhead. This paper delves into understanding how various factors such as resource allocation, consumption, and traffic load impact the performance of VNFs. We aim to provide a detailed analysis of these factors and develop analytical functions to accurately describe their impact. By testing VNFs on different testbeds, we identify the key parameters and trends, and develop models to generalize VNF behavior. Our results highlight the negative impact of resource saturation on performance and identify the CPU as the main bottleneck. We also propose a VNF profiling procedure as a solution to model the observed trends and test more complex VNFs deployment scenarios to evaluate the impact of interconnection, co-location, and NFV infrastructure on performance