Charting the NF-kB pathway interactome map

One of the phenomena observed in human aging is the progressive increase of a systemic inflammatory state, a condition referred to as “inflammaging”, negatively correlated with longevity. The five components of the Nuclear Factor kB (NF-kB) family are prominent mediators of inflammation. Several different signaling pathways activated by very diverse stimuli converge on NF-kB, resulting in a regulatory system characterized by high complexity. It is increasingly recognized that the number of components that impinges upon phenotypic outcomes of signal transduction pathways may be higher than those taken into consideration from canonical pathway representations. Scope of this analysis is to provide a wider, systemic picture of such intricate signaling system

Cooperative vehicular traffic monitoring in realistic low penetration scenarios: The COLOMBO experience

The relevance of effective and efficient solutions for vehicle traffic surveillance is widely recognized in order to enable advanced strategies for traffic management, e.g., based on dynamically adaptive and decentralized traffic light management. However, most related solutions in the literature, based on the powerful enabler of cooperative vehicular communications, assume the complete penetration rate of connectivity/communication technologies (and willingness to participate in the collaborative surveillance service) over the targeted vehicle population, thus making them not applicable nowadays. The paper originally proposes an innovative solution for cooperative traffic surveillance based on vehicular communications capable of: (i) working with low penetration rates of the proposed technology and (ii) of collecting a large set of monitoring data about vehicle mobility in targeted areas of interest. The paper presents insights and lessons learnt from the design and implementation work of the proposed solution. Moreover, it reports extensive performance evaluation results collected on realistic simulation scenarios based on the usage of iTETRIS with real traces of vehicular traffic of the city of Bologna. The reported results show the capability of our proposal to consistently estimate the real vehicular traffic even with low penetration rates of our solution (only 10%)

Knowledge Distillation for Federated Learning: a Practical Guide

Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees when building predictive models. The most used algorithms for FL are parameter-averaging based schemes (e.g., Federated Averaging) that, however, have well known limits: (i) Clients must implement the same model architecture; (ii) Transmitting model weights and model updates implies high communication cost, which scales up with the number of model parameters; (iii) In presence of non-IID data distributions, parameter-averaging aggregation schemes perform poorly due to client model drifts. Federated adaptations of regular Knowledge Distillation (KD) can solve and/or mitigate the weaknesses of parameter-averaging FL algorithms while possibly introducing other trade-offs. In this article, we provide a review of KD-based algorithms tailored for specific FL issues.Comment: 9 pages, 1 figur

Improved Adaptation and Survivability via Dynamic Service Composition of Ubiquitous Computing Middleware

These days, ubiquitous computing has radically changed the way users access and interact with services and content on the Internet: novel smart mobile devices and broadband wireless communication channels allow users to seamlessly access them anytime and anywhere. Middleware infrastructures to support ubiquitous computing need to support an extremely dynamic and ever-changing scenario, where novel contents/services, devices, formats, and media channels become available. Service-oriented architectures and service composition techniques have proven to be the key in designing flexible and extensible platforms that are able to reliably support ubiquitous computing. However, current trends in service composition for ubiquitous computing tend to be either too formal and, therefore, poorly used by average final users, or too vertical and poorly flexible and extensible. This paper proposes novel service composition middleware for ubiquitous computing that relies on a translucent composition model to achieve a flexible, extensible, highly-available, but also easily understandable and usable platform. The proposed system has been widely tested, benchmarked, and deployed on a number of different and heterogeneous ubiquitous scenarios

The Trap Coverage Area Protocol for Scalable Vehicular Target Tracking

Vehicle target tracking is a sub-field of increasing and increasing interest in the vehicular networking research area, in particular for its potential application in dense urban areas with low associated costs, e.g., by exploiting existing monitoring infrastructures and cooperative collaboration of regular vehicles. Inspired by the concept of trap coverage area, we have originally designed and implemented an original protocol for vehicle tracking in wide-scale urban scenarios, called TCAP. TCAP is capable of achieving the needed performance while exploiting a limited number of inexpensive sensors (e.g., public-authority cameras already installed at intersections for traffic monitoring), and opportunistic vehicle collaboration, with high scalability and low overhead if compared with state-of-the-art literature. In particular, the wide set of reported results show i) the suitability of our TCAP tracking in the challenging urban conditions of high density of vehicles, ii) the very weak dependency of TCAP performance from topology changes/constraints (e.g., street lengths and speed limits), iii) the TCAP capability of self-adapting to differentiated runtime conditions

Quality management of surveillance multimedia streams via federated SDN controllers in Fiwi-iot integrated deployment environments

Traditionally, hybrid optical-wireless networks (Fiber-Wireless - FiWi domain) and last-mile Internet of Things edge networks (Edge IoT domain) have been considered independently, with no synergic management solutions. On the one hand, FiWi has primarily focused on high-bandwidth and low-latency access to cellular-equipped nodes. On the other hand, Edge IoT has mainly aimed at effective dispatching of sensor/actuator data among (possibly opportunistic) nodes, by using direct peer-to-peer and base station (BS)-assisted Internet communications. The paper originally proposes a model and an architecture that loosely federate FiWi and Edge IoT domains based on the interaction of FiWi and Edge IoT software defined networking controllers: The primary idea is that our federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of end-to-end quality-aware packet management. To show the applicability and the effectiveness of the approach, our original proposal is applied to the notable example of multimedia stream provisioning from surveillance cameras deployed in the Edge IoT domain to both an infrastructure-side server and spontaneously interconnected mobile smartphones; our solution is able to tune the BS behavior of the FiWi domain and to reroute/prioritize traffic in the Edge IoT domain, with the final goal to reduce latency. In addition, the reported application case shows the capability of our solution of joint and coordinated exploitation of resources in FiWi and Edge IoT domains, with performance results that highlight its benefits in terms of efficiency and responsiveness

A Novel Design for Advanced 5G Deployment Environments with Virtualized Resources at Vehicular and MEC Nodes

IoT and edge computing are profoundly changing the information era, bringing a hyper-connected and context-aware computing environment to reality. Connected vehicles are a critical outcome of this synergy, allowing for the seamless interconnection of autonomous mobile/fixed objects, giving rise to a decentralized vehicle-to-everything (V2X) paradigm. On this front, the European Telecommunications Standards Institute (ETSI) proposed the Multi-Access Edge Computing (MEC) standard, addressing the execution of cloud-like services at the very edge of the infrastructure, thus facilitating the support of low-latency services at the far-edge. In this article, we go a step further and propose a novel ETSI MEC-compliant architecture that fully exploits the synergies between the edge and far-edge, extending the pool of virtualized resources available at MEC nodes with vehicular ones found in the vicinity. In particular, our approach allows vehicle entities to access and partake in a negotiation process embodying a rewarding scheme, while addressing resource volatility as vehicles join and leave the resource pool. To demonstrate the viability and flexibility of our proposed approach, we have built an ETSI MEC-compliant simulation model, which could be tailored to distribute application requests based on the availability of both local and remote resources, managing their transparent migration and execution. In addition, the paper reports on the experimental validation of our proposal in a 5G network setting, contrasting different service delivery modes, by highlighting the potential of the dynamic exploitation of far-edge vehicular resources

RLQ: Workload Allocation With Reinforcement Learning in Distributed Queues

Distributed workload queues are nowadays widely used due to their significant advantages in terms of decoupling, resilience, and scaling. Task allocation to worker nodes in distributed queue systems is typically simplistic (e.g., Least Recently Used) or uses hand-crafted heuristics that require task-specific information (e.g., task resource demands or expected time of execution). When such task information is not available and worker node capabilities are not homogeneous, the existing placement strategies may lead to unnecessarily large execution timings and usage costs. In this work, we formulate the task allocation problem in the Markov Decision Process framework, in which an agent assigns tasks to an available resource, and receives a numerical reward signal upon task completion. Our adaptive and learning-based task allocation solution, Reinforcement Learning based Queues (RLQ), is implemented and integrated with the popular Celery task queuing system for Python. We compare RLQ against traditional solutions using both synthetic and real workload traces. On average, using synthetic workloads, RLQ reduces the execution cost by approximately 70%, the execution time by a factor of at least 3×, and the waiting time by almost 7×. Using real traces, we observe an improvement of about 20% for execution cost, around 70% improvement for execution time, and a reduction of approximately 20× in waiting time. We also compare RLQ with a strategy inspired by E-PVM, a state-of-the-art solution used in Google's Borg cluster manager, showing we are able to outperform it in five out of six scenarios

RLQ: Workload Allocation With Reinforcement Learning in Distributed Queues

Distributed workload queues are nowadays widely used due to their significant advantages in terms of decoupling, resilience, and scaling. Task allocation to worker nodes in distributed queue systems is typically simplistic (e.g., Least Recently Used) or uses hand-crafted heuristics that require task-specific information (e.g., task resource demands or expected time of execution). When such task information is not available and worker node capabilities are not homogeneous, the existing placement strategies may lead to unnecessarily large execution timings and usage costs. In this work, we formulate the task allocation problem in the Markov Decision Process framework, in which an agent assigns tasks to an available resource, and receives a numerical reward signal upon task completion. Our adaptive and learning-based task allocation solution, Reinforcement Learning based Queues ( RLQ ), is implemented and integrated with the popular Celery task queuing system for Python. We compare RLQ against traditional solutions using both synthetic and real workload traces. On average, using synthetic workloads, RLQ reduces the execution cost by approximately 70%, the execution time by a factor of at least 3×, and the waiting time by almost 7×. Using real traces, we observe an improvement of about 20% for execution cost, around 70% improvement for execution time, and a reduction of approximately 20× in waiting time. We also compare RLQ with a strategy inspired by E-PVM, a state-of-the-art solution used in Google's Borg cluster manager, showing we are able to outperform it in five out of six scenarios