Modern distributed applications increasingly span an edge-cloud continuum, where services may need to dynamically migrate between far-edge, near-edge, and cloud environments to meet latency, resource, or policy constraints. Ensuring seamless service continuity during such migrations remains a significant challenge, particularly due to delays in DNS resolution and inconsistent client routing. This paper presents a modular DNS-driven orchestration framework designed for Kubernetes-based infrastructures. Our approach integrates service orchestration logic with a shared DNS component to enable fast and autonomous service redirection without relying on public DNS providers or complex service meshes. By coupling orchestrator-triggered migrations with DNS record updates, the system ensures immediate service reachability after migration. An optional analytics and decision engine complements the framework by triggering orchestrator actions based on monitoring insights. We evaluate the DNS reconfiguration performance of our solution compared to traditional ExternalDNS-based architectures, demonstrating significantly lower propagation latency and higher determinism during service migrations across the edge–cloud continuum.

As industries increasingly embrace digital and intelligent transformation, enterprises face significant challenges in the containerization upgrades for their Artificial Intelligence(AI) applications and dynamic service migration and management in Cloud-Edge continuum(CEC). This paper presents CloudSkin, an innovative platform designed to realize streamlined, seamless and intelligent service migration in Cloud-Edge Continuum by integrating advanced containerization techniques and AI-driven orchestration capabilities. Our approach, leveraging intelligent algorithms for service migration, can seamlessly transit services between cloud and edge environments, ensuring optimised resource allocation and reducing service latency to assure quality of service(QoS). CloudSkin has been enabled in a Mobility Usecase, empowering Cellnex businesses undergoing digital transformation to achieve higher operational efficiency. The experimental results show that compared to traditional reactive service migration, using intelligent proactive service migration can provide better migration detection, improving F1-Score up to 23.5%, and reducing 28.9% the service running time where the service latency violates SLA.

Multi-access edge computing (MEC) is an important enabler of computationally demanding and delay sensitive intelligent applications, placing significant strain on traditional cloud computing systems. As massive multiple access and high offloading data rates are envisioned for the 6th generation (6G) networks, new paradigms that can support next generation multiple access systems have appeared, e.g., the reconfigurable intelligent surfaces (RIS). Particularly, a time-division multiple access (TDMA) MEC system can leverage the integration of multiple RISs. By regulating the interplay between multiple access control and RIS phase configuration, the inclusion of multiple RISs can improve the signal quality providing better channel conditions for uplink (UL) offloading. To that end, aiming to address the offloading sum-rate maximization problem in a multi-RIS assisted TDMA MEC network, we employ deep reinforcement learning (DRL) and propose a deep deterministic policy gradient (DDPG) based algorithm, i.e., UL-RIS-DDPG. DDPG is a DRL variant designed for high-dimensional continuous action and state spaces induced by the considered problem. The UL-RIS-DDPG algorithm jointly optimizes key parameters, i.e., the users' transmission powers, the RISs phase shift design, the user-RIS association, and the UL transmission scheduling and improves significantly the network's offloading sum-rate. The simulation results demonstrate that the algorithm adapts to the network environment offering flexible RIS selection and efficient time slot allocation, outperforming the benchmarks.

Future mobile networks will offer high data rates based on high-capacity fronthaul. Current fronthaul design has two main components that communicate via the common public radio interface and fiber links, i.e., remote units (RUs) that implement simple signal processing and centralized baseband units (CBBUs) in high power-consuming data centers that perform complex network functions. Various functional splits between CBBUs and RUs are feasible, inducing trade-offs between centralization gains and bandwidth demands. This design lacks in capacity and flexibility, motivating the use of converged fiber-wireless (Fi-Wi) fronthaul with high-bandwidth fiber and millimeter-wave links, and splits that move functionalities to RUs reducing the delay demands. Further flexibility is offered by analog radio-over-fiber fronthaul that supports dynamic functional splitting via software-defined networking (SDN). Ensuring acceptable delay for all RUs, i.e., minimizing fronthaul grade-of-service (GoS), requires selection of CBBUs, channel bandwidth and functional splits of RUs. The split type affects fronthaul power consumption determining which fronthaul components are active and their processing power. Using a simulated annealing-based dynamic fronthaul resource allocation (DFRA) scheme, we jointly optimize GoS and power consumption in a novel SDN Fi-Wi fronthaul. Our results show that DFRA minimizes GoS and power consumption for all load levels outperforming baseline approaches.

5G New Radio's (NR) spectrum expansion towards higher bands, although critical towards achieving the envisioned 5G capacity requirements, creates the need for installing a very large number of Access Points (APs), which asserts tremendous capital burden on the Mobile Network Operators. Current centralization solutions such as the Cloud Radio Access Network (C-RAN) alleviate partially the costs of densification by moving the majority of radio processing functionalities from the Remote Radio Heads (RRHs) to the central Base Band Unit (BBU), but still require very high-speed Point-to-Point links between the BBU and each RRH mainly due to the digitized Common Public Radio Interface (CPRI) that is excessively inefficient for hauling broadband signals. In this article, we present a novel architecture that employs an analog converged Fiber-Wireless scheme in order to create a very spectrally efficient Point-to-Multipoint network capable of interconnecting a large number of APs, while allowing compatibility with mature Ethernet-based low-cost equipment. Preliminary simulation results show very low end-to-end Ethernet packet delay, well below eCPRI's 100 μs mark, even for fiber lengths up to 10 km, indicating the suitability of our solution for employment in 5G NR large-scale fronthaul networks.

The upcoming generation of mobile networks is expected to serve numerous mobile users with high quality-of-service (QoS) demands, requiring high-capacity fronthaul. As the provision of fiber connections directly to the end users is not cost-efficient, the integrated fiber wireless (FiWi) fronthaul design based on wireless networking and passive optical networks (PONs) has been proposed. The FiWi design involves modern networking technologies that can accommodate the need for data rates in the Gb/s scale and low delay, such as the wavelength division multiplexing (WDM) in the optical domain and the multiple input multiple output (MIMO) communication over millimeter wave (mmWave) spectrum in the wireless domain. The co-existence of two network types requires resource management in a medium transparent manner, i.e., the sharing of the bandwidth in the wireless domain should allow the organization of the data packets in optical frames. As the traffic circulating in the FiWi fronthaul involves packets of different priorities, i.e., different QoS classes, the resource management scheme should support QoS differentiation. To this end, we propose a resource management scheme for FiWi fronthaul and we extensively study its performance in terms of experienced delay and throughput. Our simulation results demonstrate that the proposed scheme significantly reduces the delay of the high priority class.

Nowadays, over-the-top (OTT) applications can be accessed via Internet connections over cellular networks. Mobile network operators (MNOs) strive to accommodate mobile traffic through energy efficient shared networks. The OTT service providers (OSPs) need to interact with MNOs and require resources for offering desired quality- of-service (QoS) levels to users of different categories, with different QoS requirements, which imply prioritization of certain OTT flows. Resource scheduling should respect network neutrality, which forbids OSP prioritization. Furthermore, OSPs request resources periodically, according to their performance goals, causing delay in flows' accommodation due to i) the time required for information exchange between OSPs and MNOs, affected by network congestion, and ii) the time required for flows to receive resources, affected by the number of active flows. The intervention of OSPs may induce additional energy cost for the MNOs and affect the mobile network energy efficiency. Acknowledging the lack of OSP- oriented resource management approaches, we introduce a matching theoretic flow prioritization (MTFP) algorithm and investigate delay and energy efficiency through extensive simulations. Our study shows that MTFP improves both metrics comparing to the best effort approach, whereas its performance is affected by the OTT traffic level and the frequency of resource allocation process.

Modern over-the-top (OTT) applications can be accessed via Internet connections over cellular networks, possibly shared and managed by multiple mobile network operators (MNOs). The OTT service providers (OSPs) need to interact with MNOs, requesting resources for serving users of different categories and with different quality-of-service requirements. For this purpose, OSPs need OTT application flow prioritization in resource allocation, while the network resource scheduling should respect network neutrality that forbids OSP prioritization. OSPs also need to request resources periodically, according to their performance goals, i.e., grade-of-service (GoS) level (blocking probability), causing delay in flows' accommodation due to: 1) the time required for information exchange between OSPs and MNOs, affected by network congestion, and 2) the time required for flows to receive resources, affected by the number of concurrently active flows. Acknowledging the lack of OSP-oriented resource management approaches, we: 1) introduce a novel matching theoretic flow prioritization (MTFP) algorithm that respects network neutrality and 2) design analytical models that enable the thorough investigation of the GoS and delay performance in various scenarios. Our results (analytical and simulation) show that MTFP improves both metrics compared to the best effort approach, whereas its performance is affected by the number of flows and the resource allocation frequency.

In long term evolution advanced (LTE-A) networks, the mobile devices can concurrently participate in cooperative outband device-to-device (D2D) data exchange by virtue of user- or network-related parameters (e.g., interest in the same content and cooperative transmissions, respectively). In these scenarios, two major problems arise: 1) the coexistence of multiple devices creates channel access issues, demanding effective medium access control (MAC) schemes, and 2) cellular network factors (i.e., scheduling policy and channel conditions) affect the D2D communication, as the circulating information in D2D links is mainly of cellular network origination, stressing the need for cross network approaches. In this context, the contribution of this paper is threefold. First, exploiting idle devices as relays and the benefits of network coding (NC) in bidirectional communications, we propose an adaptive cooperative NC-based MAC (ACNC-MAC) protocol for the D2D data exchange. Then, we devise a cross-network model that captures the impact of cellular network characteristics on D2D communication. Finally, we evaluate the performance of the ACNC-MAC in terms of throughput, energy efficiency, and battery consumption. Our results show that the LTE-A parameters and the relays' participation significantly affect the D2D throughput, while the D2D performance deteriorates with the increase of cell congestion.

Wireless network virtualization enables efficient network sharing in Long Term Evolution Advanced (LTE-A) networks. For the accommodation of ever-increasing user demands, Mobile Virtual Network Operators (MVNOs) can lease and operate virtual resources in network infrastructure that belongs to Mobile Network Operators (MNOs). The coexistence of multiple MVNOs that serve users with different Quality of Service (QoS) requirements and spatial distribution in an LTE-A cell further complicates the arising resource allocation problem. Moreover, the MVNOs aim to satisfy the QoS demands for the maximum possible number of users with the minimum possible cost. This multifaceted context renders centralized optimization approaches for virtual resource allocation in shared LTE-A networks unsuitable. As an alternative, the framework of matching theory, thanks to its distributed nature, can achieve a proper allocation through a stable matching between resources and MVNOs. In this context, we introduce a matching-theoretic formulation for the virtual resource allocation problem and propose a distributed algorithm that properly matches the MVNOs with the available resources. Our simulation results show that the proposed algorithm reaches a stable matching that satisfies the QoS demands for more users in comparison with other approaches.

Device-to-Device (D2D) communication allows the direct connection of mobile devices in a cellular network. In D2D networking, cooperative communication is inherent due to the proximity of the devices that are able to overhear and forward information. Particularly, adjacent devices can act as relays and assist the communication of other devices. Network Coding (NC) can further increase the cooperation gains, since a number of packets can be encoded and transmitted together. However, the contention among multiple relays causes channel access issues that must be regulated by effective Medium Access Control (MAC) protocols. In this context, we propose an Adaptive Cooperative Network Coding-based MAC (ACNC-MAC) protocol that utilizes cooperative relaying and exploits NC opportunities in a D2D topology. Both analytical and simulation results show that the proposed protocol is advantageous in terms of energy efficiency without sacrificing the Quality of Service (QoS).

We propose a novel priority-based approach that enables the optimal control of the transmission power and the use of the available network resources of a multihop Direct Sequence Code Division Multiple Access (DS-CDMA) Wireless Visual Sensor Network (WVSN). TheWVSN nodes can either monitor different scenes (source nodes) or retransmit videos of other nodes (relay nodes). Moreover, in real environments the source nodes monitor different scenes that may be of dissimilar importance. Hence a higher end-to-end quality is demanded for those nodes that are assigned a higher priority. Overall, each node has different power and resource requirements, and therefore a global optimization approach is required. For the purpose of enhancing the delivered video quality of the source nodes with respect to their priorities, we define and suggest the use of priority-based optimization criteria. Experimental results that assess the proposed approach are provided and conclusions are drawn.