Goal-Oriented (GO) communication is an emerging paradigm that aims at enhancing the efficiency of Internet of Things (IoT) systems by sending only the minimum data needed to achieve the application goal. In GO communications, the network can apply techniques to reduce the amount of data sent through the network to a cloud node, such as analyzing and selecting data at the source with intelligent IoT devices. GO communication and the related networking techniques have primarily been studied from a device-centric perspective, often overlooking their impact on the network. However, the network consumes significant amounts of energy, with the Radio Access Network (RAN) alone accounting for approximately 70 % of the total energy consumption in mobile communication systems. Therefore, this paper introduces an end-to-end model for energy consumption, latency, and accuracy to assess GO networking strategies. We then use the model to evaluate the performance of GO strategies in edge cloud scenarios with different hardware platforms.

Service providers can leverage shared resources to reduce the overall amount of required resources while keeping acceptable Quality of Service (QoS) levels. Kubernetes (K8s) provides a Horizontal Pod Autoscaling (HPA) mechanism that allows to automatically adjust the number of Pods to closely follow the user demand variations over time. To properly leverage shared resources with HPA, service providers need to limit the use of dedicated resources and overprovisioning. However, in the case of traffic spikes, there may not be enough resources to satisfy the demand. The HPA, which relies on resource usage to drive the scaling, is unaware of how many requests could not be served with the required QoS. This might result in an underestimation of the number of required Pods to be added, leading to additional QoS degradation. This demonstration showcases the effectiveness of a new Pod autoscaling mechanism (i.e., Service Aware Pod Autoscaling (SAPA)) that relies on user request measurements from the service load balancer to better estimate the number of required Pods. SAPA allows selecting the amount of Pod resources (dedicated and shared) in a simple way. We demonstrate the benefits of SAPA by comparing it to a K8s cluster based on the traditional HPA in terms of resource usage and service latency.

Optical aggregation network is a key subsystem to support high performance end-to-end services in 5G mobile networks. This paper presents a network design optimization methodology to implement ultra reliable low latency communications (URLLC) in a metro area. Novel optimization algorithms based on Integer Linear Programming (ILP) are defined to solve dedicated (DPP) and shared (SPP) path protection problems, in the presence of single failure with the aim to minimize the number of active nodes, by adopting the functional splitting options defined by 3GPP. The results prove the ability of the proposed algorithm to find optimal solutions which also minimize the number of high capacity node instances and the usage of bandwidth resources, especially when SPP is applied.

Base station (BS) densification is increasingly adopted by mobile operators in order to support increasing traffic demand. However, a large number of BSs requires many backhaul connections, which may be very expensive. For this reason, provisioning high speed backhaul connections to BSs in a cost-effective way is challenging, and it is important to efficiently utilize an existing fixed network infrastructure if possible. This paper proposes two optimized infrastructure-aware planning strategies for small cells and fiber backhaul. The first strategy is referred to as joint design (JD) and is based on the joint cost minimization of small cells and fiber backhaul. The JD strategy is compared to a traditional design (TD) solution based on a two-step optimization approach. In the latter a cost-optimal small cells placement is identified first, then the corresponding minimum cost fiber backhaul deployment is determined accordingly. A comparison between these two approaches in dense 5G urban scenarios shows that the relative performance of JD and TD largely depends on the expected traffic demand and on the existing infrastructure. In dense urban areas with the average traffic levels expected for the year 2020 and beyond, JD returns up to 50% lower deployment cost in a greenfield scenario, and up to 70% lower deployment cost in a brownfield scenario. Only in areas with extremely high traffic demand (e.g., open-air festivals and stadiums) JD returns deployment costs very similar to TD.

Providing high capacity to the end users is one of the main challenges for the fifth generation (5G) of mobile networks. The users' habit to consume online contents indoor makes the outdoor-to-indoor capacity provisioning impractical, especially when the high-frequency bands proposed for 5G are employed. The centralized radio architecture (CRA) is an in-building solution, which relies on the centralization of baseband processing functions, fully or partly allowing for centralized cell management while providing signals directly inside the buildings. On the other hand, the massive deployment of CRAs in urban areas may yield to unacceptably high installation costs, due to the radio network equipment to be activated. To make CRAs appealing to mobile operators, we propose different deployment strategies to minimize the CRA deployment cost. We define the remote radio unit placement (RRUP) problem and formulate it as an integer linear program, obtaining optimal deployment solutions in small urban residential scenarios. We prove the RRUP problem to be NP-hard, requiring heuristic approaches to solve large problem instances. To this end, we propose an effective and scalable heuristic for minimizing the amount of radio equipment required to deploy CRAs in large urban areas.