Optical networks are evolving toward self-adaptation exploiting self-observation of network conditions (e.g., through monitoring at the physical layer). Thus, according to monitoring information, reconfiguration of network devices including switches and transponders (e.g., transmission parameters) is acted. Typically, the network is thought as centralized, where a central software defined networking (SDN) controller holds the overall view of network topology and resources and, upon connection request, it decides for routing and spectrum assignment, and, then, it configures accordingly the data plane. Similarly, the central controller receives notifications when failures occur and (re)acts by reconfiguring the data plane. However, centralized solutions can suffer from scalability and responsiveness issues, translating in delays during provisioning and recovery. In this paper, we report the demonstration in a field trial of an automatic and self-reconfigurable network. We leverage a novel control paradigm based on programmable finite state machine (FSM) where each state is associated to a specific network condition and a state transition implies network reconfiguration. Thanks to this approach the central controller can instruct (pre- program) network devices making them aware of the proper reconfiguration settings associated to the network conditions. Then, the reconfiguration is triggered locally, thus increasing scalability and response speed. Two successful experiments will be shown: 1) local transmission parameter adaption according to physical layer conditions; 2) connection central frequency optimization mitigating filtering effects and distortions.

Distributed storage systems spanning across different cloud data centers have substantially improved availability and flexibility for data storage and retrieval operations. However, stringent latency requirements of emerging applications necessitate optimized selection of storage resources that exhibit smaller delay. Introducing edge resources into distributed storage systems enables data placement closer to its source, but simultaneously increases the complexity of decision-making and orchestration processes for optimal data placement. In this work, we develop mechanisms for storing data across an infrastructure that includes both edge and cloud resources. Our approach focuses on optimizing data integrity, longevity, security, and cost, while leveraging erasure coding when performing the resource allocation. We first present a comprehensive mixed integer linear programming formulation of the storage resource orchestration problem. As the search space for the optimal solution can be vast and the execution time prohibitively large for real size problems, we also propose an innovative multi-agent heuristic approach that uses the rollout, a reinforcement based policy, to balance performance and execution time efficiently. Through various simulation experiments, we evaluate the developed mechanisms and trade-offs involved in our approach. By incorporating data from a multi-cloud provider, we further enhance the validity of the simulations and the conclusions drawn.

Background: Reducing energy consumption and CO2 emissions in order to address climate change requires behavioral changes by the citizens, who will have to adopt more environmentally friendly and energy-saving practices. Methods: We present a system and corresponding practices for gathering energy consumption data from public school buildings over the Internet, processing them to identify hidden correlations and produce actionable advice and presenting the results in real-time to its occupants over the Web. We describe the metering infrastructure installed at schools for energy consumption monitoring and the related actions carried out in order to motivate local school communities towards an environmentally friendly behavior. The introduction of IPv6 was found to be a key enabling technology for setting up such a system in a simple, secure, and efficient way. Results: The proposed system and processes are put under test in a pilot installation composed of about 50 IPv6-enabled schools of the Greek School Network (GSN). These are also combined with appropriate educational and social engagement tools. In this way, energy consumption, operating costs, and greenhouse effects can be reduced in the area of educational and public administration buildings in general. Thus, people become almost instantly aware of the energy and environmental implications of their actions, which motivates them towards behavioral changes and the adoption of environmentally friendly practices. Conclusions: The pilot provides a good example of collaboration between the ICT sector, the smart building and automation vendors, and the public authorities.

White boxes, originally introduced in data centers, are expected to soon also penetrate the market of wide area networks, since operators see them as a way to drive down capital expenditure. Indeed, white boxes can be composed of modules from different vendors, removing the traditional vendor lock-in and creating more competition in the market, thus reducing hardware costs. In this paper, we describe the experimental demonstration of a fully disaggregated white box, composed of two different types of transponders, monitors (including filtering effect parameter monitors), add-drop multiplexers, and switches. Toward this end, we developed an appropriate control and management plane for the white box based on NETCONF and YANG. Using that, we demonstrated the automatic reconfiguration of the white box so as to maintain the service in the presence of multiple unexpected degradations: signal-to-noise ratio decrease and filtering distortion. We also demonstrated and presented here a vendor-neutral procedure to compute the thresholds used for raising alarms in response to physical layer degradations.

The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.