Using SHAP Values to Validate Model’s Uncertain Decision for ML-based Lightpath Quality-of-Transmission Estimation

We apply Quantile Regression (QR) for lightpath quality-of-transmission (QoT) estimation with the aim of identifying uncertain decisions and then exploit Shapley Additive Explanations (SHAP) to quantify lightpath features’ importance by means of SHAP values and validate the model’s decisions in a post-processing phase. Numerical results show that our approach can eliminate more than 98% of false predictions and that SHAP values can validate up to 90% of the model's uncertain decisions

Privacy-Preserving Multi-Operator Contact Tracing for Early Detection of Covid19 Contagions

The outbreak of coronavirus disease 2019 (covid-19) is imposing a severe worldwide lock-down. Contact tracing based on smartphones' applications (apps) has emerged as a possible solution to trace contagions and enforce a more sustainable selective quarantine. However, a massive adoption of these apps is required to reach the critical mass needed for effective contact tracing. As an alternative, geo-location technologies in next generation networks (e.g., 5G) can enable Mobile Operators (MOs) to perform passive tracing of users' mobility and contacts with a promised accuracy of down to one meter. To effectively detect contagions, the identities of positive individuals, which are known only by a Governmental Authority (GA), are also required. Note that, besides being extremely sensitive, these data might also be critical from a business perspective. Hence, MOs and the GA need to exchange and process users' geo-locations and infection status data in a privacy-preserving manner. In this work, we propose a privacy-preserving protocol that enables multiple MOs and the GA to share and process users' data to make only the final users discover the number of their contacts with positive individuals. The protocol is based on existing privacy-enhancing strategies that guarantee that users' mobility and infection status are only known to their MOs and to the GA, respectively. From extensive simulations, we observe that the cost to guarantee total privacy (evaluated in terms of data overhead introduced by the protocol) is acceptable, and can also be significantly reduced if we accept a negligible compromise in users' privacy

ML-based Network Pruning for Routing Data Overhead Reduction in Wireless Sensor Networks

Routing in Wireless Sensor Networks (WSNs) is one of the tasks that heavily impact network lifetime: current routing protocols, such as Ad-hoc On-demand Distance Vector (AODV), generate excessive and rather unnecessary overhead for route discovery, which in turn contributes to deplete the limited power resources of sensors. In this work, we propose a novel machine learning-based approach to perform network pruning during route discovery aiming at reducing data overhead. Our approach assumes that sensor nodes are aware of their locations and have processing capabilities to run lightweight machine learning algorithms. We perform extensive simulations considering WSNs consisting of different amounts of nodes. Results show that our proposed approach can reduce data overhead by 50% to 65%, depending on the amount of nodes and pruning aggressiveness

Online virtual machine evacuation for disaster resilience in inter-data center networks

With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi-objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.publishe

On the Application of Explainable Artificial Intelligence to Lightpath QoT Estimation

We demonstrate the potentialities of explainable AI when applied to distill knowledge from a trained supervised machine learning model for lightpath quality of transmission estimation in optical networks, with synthetic datasets

A Privacy-Preserving Protocol for Network-Neutral Caching in ISP Networks

By performing in-network caching, Internet Service Providers (ISPs) allow Content Providers (CPs) to serve contents from locations closer to users. In this way, the pressure of content delivery on ISPs’ network is alleviated, and the users’ Quality-of-Experience (QoE) improved. Due to its impact on QoE, caching has been recently considered as a form of traffic prioritization in the debate on Network Neutrality (NN). A possible approach to perform NN-compliant caching consists in assigning the same portion of cache storage to all the CPs. However, this static subdivision does not consider the different popularities of the CPs’ contents and is therefore inefficient. Alternatively, the cache can be subdivided among the CPs proportionally to the popularity of their contents. However, CPs consider this information private and are reluctant to disclose it. In this work, we propose a protocol to perform a popularity-driven subdivision of the caches’ storage in a privacy-preserving and network-neutral fashion. The protocol is based on the Shamir Secret Sharing (SSS) scheme and is designed to ensure a NN-compliant subdivision of the caches while preserving the privacy of both CPs and ISP (i.e., contents’ popularity and caches’ size are not disclosed). Through dynamic simulation, we show that the popularity-driven cache subdivision (enforced by using our protocol) outperforms several baseline approaches in terms of overall network Resource Occupation (RO) and caching Hit-Ratios. Thanks to our numerical results, we observe that the frequency of execution of the protocol has a significant impact on the RO, and that the ISP can tune this frequency to minimize its RO while introducing an acceptable data overhead. Because of this tuning, several CPs may experience a loss with respect to the hit-ratio that they would obtain by independently choosing the frequency of execution. This loss is very limited, and the employment of the protocol is therefore beneficial to all the involved parties, especially since, by using it, CPs are guaranteed that the ISP behaves in a network-neutral manner

Energy-efficient caching for Video-on-Demand in Fixed-Mobile Convergent networks

The success of novel bandwidth-consuming multimedia services such as Video-on-Demand (VoD) is leading to a tremendous growth of the Internet traffic. Content caching can help to mitigate such uncontrolled growth by storing video content closer to the users in core, metro and access network nodes. So far, metro and especially access networks supporting mobile and fixed users have evolved independently, leveraging logically (and often also physically) separate infrastructures; this means that mobile users cannot access caches placed in the fixed access network (and vice-versa), even if they are geographically close to them, and energy consumption implications of such undesired effect must be investigated. We define an optimization problem modeling an energy-efficient placement of caches in core, metro and fixed/mobile access nodes of the network. Then, we show how the evolution towards a Fixed-Mobile Converged metro/access network, where fixed and mobile users can share caches, can reduce the energy consumed for VoD content delivery