6G networks are expected to deliver ultra-low latency, high reliability, and real-time intelligence for emerging services such as interactive Augmented Reality (AR), autonomous robotics, and digital twins. Achieving these requirements in practice demands tight coordination between networking, computing, and control domains, spanning RAN, transport, edge, and cloud. However, current 5G deployments lack pervasive telemetry, fine-grained observability, and automated control mechanisms capable of reacting at the time scales required by latency-sensitive applications. This paper presents a full integrated demonstration of DESIRE6G, a cloud-native 6G-ready architecture that leverages programmable data planes with P4 for flexible routing and telemetry using an implementation of novel data plane protocols, achieves distributed optimization of service deployment and runtime monitoring and reconfiguration via secure multi-agent systems (MAS), combined with intent-based orchestration layer for end-to-end service assurance. The system is validated on the federated ARNO testbed using a real distributed AR application involving a remotely-controlled drone as a User-Equipment that is equipped with a camera streaming a live video through the DESIRE6G network to a Kubernetes edge cluster that executes serverless inference functions for object detection and recognition, the video is then augmented with object information and shown on a Quest 3 AR headset. The MAS monitors the end-to-end latency in real time through P4 Telemetry and responds to changes in network conditions by reconfiguring the affected segments, while the Kubernetes monitoring provides real-time visibility and scalability across different segments. Overall, three hierarchical service assurance loops are demonstrated: (i) In-Network Control (INC) executing microsecond-scale congestion recovery in the P4 data plane, (ii) Infrastructure Management Layer (IML) performing millisecond-scale function migration and scaling, and (iii) MAS-driven cross-domain optimization operating at sub-second time scales to resolve RAN latency anomalies. Evaluation results show stable end-to-end latency in the 15–25 ms range in steady-state conditions, with fast recovery during induced congestion ≤ 1 . 5 ms data plane reroute via P4 INC.

Routers use queues to temporarily hold packets when they can’t process them immediately. Congestion occurs when the arrival rate of packets exceeds the processing capacity, leading to increased queuing delay. Active Queue Management (AQM) strategies aim to reduce congestion and queuing delay by draining packets from queues. Traditionally, AQMs are placed in the Egress pipeline of Programmable Data Plane (PDP) hardware due to the availability of queue delay information there. We argue that this approach wastes the router’s resources because the dropped packet has already consumed the entire pipeline of the device. We propose ingress Random Early Detection (iRED) as a more efficient P4-AQM approach fully implemented in PDP hardware, that introduces the concept of disaggregated AQM, effectively resolving the egress drop problem. iRED also supports the Low Latency, Low Loss, and Scalable Throughput (L4S) framework, saving device pipeline resources by dropping packets in the Ingress block. Our experiments with a Tofino2 programmable switch achieved a rate of 1Tbps and show that iRED can reduce router resource consumption by up to 10x in memory usage, 12x fewer processing cycles, and 8x less power consumption for the same traffic load. It achieves fairness in bandwidth usage for different types of traffic and significantly improves Quality of Service (QoS) in Dynamic Adaptive Streaming over HTTP (DASH) with up to a 2.34x improvement in Frames Per Second (FPS) and a 4.77x increase in video player buffer fill. We believe this is a pioneering work to highlight the egress drop problem and deeply explore its potential damage to network performance.

In software-defined networking, the domain-specific language P4 allows developers to program the behavior of networking devices at a comparatively high level of abstraction. A P4 program defines a state machine to parse incoming packets. The parsers are flexible and efficient but may be sensitive to bugs and difficult to maintain. As a possible alternative, data-dependent grammars (DDGs) can describe both packet structure and parser at a higher level of abstraction.

The emergence of the fifth generation (5G) technology has transformed mobile networks into multi-service environments, necessitating efficient network slicing to meet diverse Service Level Agreements (SLAs). SLA decomposition across multiple network domains, each potentially managed by different service providers, poses a significant challenge due to limited visibility into real-time underlying domain conditions. This paper introduces Risk-Aware Iterated Local Search (RAILS), a novel risk model-driven meta-heuristic framework designed to jointly address SLA decomposition and service provider selection in multi-domain networks. By integrating online neural network (NN)-based risk modeling with iterated local search principles, RAILS effectively navigates the complex optimization landscape, utilizing historical feedback from domain controllers. We formulate the joint problem as a Mixed-Integer Nonlinear Programming (MINLP) problem and prove its NP-hardness. Extensive simulations demonstrate that RAILS achieves near-optimal performance, offering an efficient, real-time solution for adaptive SLA management in modern multi-domain networks.

Cellular Vehicle-to-Everything (C-V2X) is currently at the forefront of the digital transformation of our society. By enabling vehicles to communicate with each other and with the traffic environment using cellular networks, we redefine transportation, improving road safety and transportation services, increasing the efficiency of vehicular traffic flows, and reducing environmental impact. To effectively facilitate the provisioning of Cellular Vehicular-to-Network (C-V2N) services, we tackle the interdependent problems of service task placement and scaling of edge resources. Specifically, we formulate the joint problem and prove that it is not computationally tractable. To address its complexity we propose dhpg, a new Deep Reinforcement Learning (DRL) approach that operates in hybrid action spaces, enabling holistic decision-making and enhancing overall performance. We evaluated the performance of DHPG using simulations with a real-world C-V2N traffic dataset, comparing it to several state-of-the-art (SoA) solutions. DHPG outperforms these solutions, guaranteeing the 99th percentile of C-V2N service delay target, while simultaneously optimizing the utilization of computing resources. Finally, time complexity analysis is conducted to verify that the proposed approach can support real-time C-V2N services.

In-band network telemetry (INT), enabled by programmable data planes and the appearance of programming protocol-independent languages such as P4, emerged as a viable approach for network monitoring. INT allows the collection of fine-grained network information in real-time, increasing network visibility, at the cost of network overhead. Several lightweight INT approaches have been recently proposed that attempt to alleviate the transmission overhead of INT, while maintaining a high degree of monitoring accuracy. However, their impact on the resources of the respective hardware network devices has been hardly investigated as most of the approaches are evaluated via simulation. In this study, we provide proof of concept implementations of two lightweight INT approaches that have been proposed for path tracing on the Intel Tofino ASIC, identifying the challenges of porting the solution to the selected target. We examine their performance, providing an in-depth analysis of resource consumption.

In the context of 6G, Digital Twin technology has the potential to play a crucial role by offering a sophisticated, dynamic model that mirrors the physical world in real-time. Integrating digital twins into 6G enables precise orchestration and optimization of network resources, meeting the diverse and stringent demands of next-generation applications. Furthermore, digital twins provide a platform for continuous learning and AI-driven analysis. Research on the architecture of Network Digital Twins (NDTs) for 5G/6G is still limited, often focusing on partial implementations rather than comprehensive, full-stack approaches. This paper proposes a high-level architectural framework for 6G NDTs system, structured across three layers: the Physical Twin Layer, Digital Twin Layer, and Application and Service Layer. We discuss the challenges of deploying these systems and the importance of selecting appropriate tools, presenting an experimental case study that demonstrates the impact of different tool choices on system performance. Our findings underscore the need for flexible, scalable solutions to fully realize the benefits of NDTs in 6G networks.

6G's superpower must be simplicity, which should not be viewed as a constraint, but rather as the organic outcome of using the most advanced technologies at our disposal. Programmability in the data plane, cloud-native features like automatic scaling and failover, transparent acceleration of both network functions and applications and AI-driven optimizations are already present. We only need to integrate these different innovations into a consistent architecture and offer a simple yet powerful solution for the very different applications that would use future mobile networks. The application space is getting more and more heterogeneous, e.g., legacy Internet-based services still using the good old TCP protocol, future media services relying on multipath transport - always utilizing the best available connection, or control applications of robots or drones requiring extreme low and stable latency. The different applications will require very different Key Performance Indicators (KPIs) from the network. In this paper, we present a novel architecture called DESIRE6G (D6G) architecture that aims to fulfill these requirements by integrating the key technological innovations mentioned above. Besides supporting the diverse KPIs of future applications, the novel architecture should also simplify the mobile network itself by promoting modularity and service-based network function selection which can replace traditional control plane centric solutions, e.g., for handover.

The fifth generation (5G) of wireless networks is set out to meet the stringent requirements of vehicular use cases. Edge computing resources can aid in this direction by moving processing closer to end-users, reducing latency. However, given the stochastic nature of traffic loads and availability of physical resources, appropriate auto-scaling mechanisms need to be employed to support cost-efficient and performant services. To this end, we employ Deep Reinforcement Learning (DRL) for vertical scaling in Edge computing to support vehicular-to-network communications. We address the problem using Deep Deterministic Policy Gradient (DDPG). As DDPG is a model-free off-policy algorithm for learning continuous actions, we introduce a discretization approach to support discrete scaling actions. Thus we address scalability problems inherent to high-dimensional discrete action spaces. Employing a real-world vehicular trace data set, we show that DDPG outperforms existing solutions, reducing (at minimum) the average number of active CPUs by 23% while increasing the long-term reward by 24%.

In-band network telemetry (INT), empowered by programmable dataplanes such as P4, comprises a viable approach to network monitoring and telemetry analysis. However, P4-INT as well as other existing frameworks for INT yield a substantial transmission overhead, which grows linearly with the number of hops and the number of telemetry values. To address this issue, we present a deterministic and a probabilistic technique for lightweight INT, termed as DLINT and PLINT, respectively. In particular, DLINT exercises per-flow aggregation by spreading the telemetry values across the packets of a flow. DLINT relies on switch coordination through the use of per-flow telemetry states, maintained within P4 switches. Furthermore, DLINT utilizes Bloom Filters (BF) in order to compress the state lookup tables within P4 switches. On the other hand, PLINT employs a probabilistic approach based on reservoir sampling. PLINT essentially empowers every INT node to insert telemetry values with equal probability within each packet. Our evaluation results corroborate that both proposed techniques alleviate the transmission overhead of P4-INT, while maintaining a high degree of monitoring accuracy. In addition, we perform a comparative evaluation between DLINT and PLINT. DLINT is more effective in conveying path traces to the telemetry server, whereas PLINT detects more promptly path updates exploiting its more efficient INT header space utilization.

Video services account for the largest share of all Internet traffic, demanding a network capable of supporting the requirements of delay-sensitive traffic. Fluctuations in network load can cause high delays in the queues of network routers, which tend to degrade the Quality of Service (QoS) for adaptive video streaming, such as Dynamic Adaptive Streaming over HTTP (DASH). This work is positioned in the scope of active management queues (AQM) to improve the QoS of a DASH service by means of dropping packets. One traditional AQM that adopts a packet drop policy is Random Early Detection (RED), developed to drain the flow in times of congestion and thus reduce queueing delay. We revisited and implemented a P4-based implementation of RED, named iRED (ingress RED), an algorithm capable of dropping packets at the ingress pipeline, an innovation compared to other AQM strategies based on dropping at the egress. iRED was evaluated in two scenarios. First, we compare iRED against state-of-art AQM algorithms employing egress packet dropping in terms of Round-Trip Time (RTT), throughput and their impact on resources usage. Our findings indicate that iRED outperforms existing P4-based approaches by approximately up to 2.5x in RTT and 0.75x in throughput for the given buffer sizes. Next, we compare iRED versus Tail Drop (TD) approach in an emulated programmable Content Delivery Network (CDN) employing DASH. Experiments indicate that the iRED improve the QoS by approximately 0.85x in terms of cached video available in the client’s buffer and 0.9x in Frames Per Second (FPS) played.

The excess buffering of packets in network elements, also referred to as bufferbloat, results in high latency. Considering the requirements of traffic generated by video conferencing systems like Zoom, cloud rendered gaming platforms like Google Stadia, or even video streaming services such as Netflix, Amazon Prime and YouTube, timeliness of such traffic is important. Ensuring low latency to IP flows with a high throughput calls for the application of Active Queue Management (AQM) schemes. This introduces yet another problem as the co-existence of scalable and classic congestion controls leads to the starvation of classic TCP flows. Technologies such as Low Latency Low Loss Scalable Throughput (L4S) and the corresponding dual queue coupled AQM, DualPI2, provide a robust solution to these problems. However, their deployment on hardware targets such as programmable switches is quite challenging due to the complexity of algorithms and architectural constraints of switching ASICs. In this study, we provide proof of concept implementations of two AQMs that enable the co-existence of scalable and traditional TCP traffic, namely DualPI2 and the preceding single-queue PI2 AQM, on an Intel Tofino switching ASIC. Given the fixed operation of the switch’s traffic manager, we investigate to what extent it is possible to implement a fully RFC-compliant version of the two AQMs on the Tofino ASIC. The study shows that an appropriate split between control and data plane operations is required while we also exploit fixed functionality of the traffic manager to support such solutions.

Network slicing plays a key role in the 5G ecosystem for vertical industries to introduce new services. However, one widely-recognized challenge of network slicing is to provide traffic isolation and concurrently satisfy diverse performance requirements, e.g., bandwidth and latency. In this work, we showcase the capability to retain these two goals at the same time, via extending the 5Growth baseline architecture and designing a new data-plane pipeline, i.e., virtual queue, over the P4 switch. To demonstrate the effectiveness of our approach, a proof-of-concept is presented serving different service requests over a mixed data path, including P4 switches and Open vSwitches (OvSs).

The advent of programmable network switch ASICs and recent developments on other programmable data planes (NPUs, FPGAs) drive the renewed interest in network data plane programmability. The P4 language has emerged as a strong candidate to describe a protocol independent datapath pipeline. With its supported architectures, the P4 language provides an excellent way to define the packet processing and forwarding behavior, while leaving other networking components such as the traffic management engine, to non-programmable fixed function elements, based on the capabilities of most programmable devices. However, network flexibility is essential to meet the Quality of Service (QoS) requirements of traffic flows. Thus, enabling programmable control for fixed-function elements like traffic management is crucial. Towards that end we propose the use of virtual queues in the P4 pipeline, investigate the application of virtual queue-based traffic management, and portability of the approach using different P4 programmable targets. Specifically, we focus on virtual queue based Active Queue Management (AQM) for congestion policing and meeting the latency targets of distinct network slices. The solution is compared to P4 built-in functionality for bandwidth management using meters, proving also that the additional dimensions of control are achieved without compromising the processing complexity of the solution.

With the increasing attention to the integrated satellite-terrestrial networks (ISTNs), the satellite gateway placement problem becomes of paramount importance. The resulting network performance may vary depending on the different design strategies. In this paper a joint satellite gateway placement and routing strategy for the terrestrial network is proposed to minimize the overall cost of gateway deployment and traffic routing, while adhering to the average delay requirement for traffic demands. Although traffic routing and gateway placement can be solved independently, the dependence between the routing decisions for different demands makes it more realistic to solve an aggregated model instead. We develop a mixed integer linear program (MILP) formulation for the problem. We relax the integrality constraints to achieve a linear program (LP) which reduces time-complexity at the expense of a sub-optimal solution. We further propose a variant of the proposed model to balance the load between the selected gateways.

Cloud computing has emerged as the computing paradigm that enables the delivery of utility-based IT services to users. The hyper-growth of Cloud computing has led to increased power consumption with significant consequences both in terms of environmental and operational costs. Hence, over the last years, attention has been drawn to optimizing energy consumption at the data center, aimed at the reduction of carbon footprints. However the world of Cloud Computing is constantly developing, with new concepts introduced while additional challenges arise. In this paper, a method for energy efficient resource allocation is proposed, in the context of a networked cloud environment. The method employs dynamic server consolidation by periodic VM migration. The approach is validated conducting performance evaluation via simulation, while it is compared against energy aware / non energy aware methods, using a set of both power indication and resource allocation metrics.

Cloud computing builds upon advances on virtualization and distributed computing to support cost-efficient usage of computing resources, emphasizing on resource scalability and on demand services. Moving away from traditional data-center oriented models, distributed clouds extend over a loosely coupled federated substrate, offering enhanced communication and computational services to target end-users with quality of service (QoS) requirements, as dictated by the future Internet vision. Toward facilitating the efficient realization of such networked computing environments, computing and networking resources need to be jointly treated and optimized. This requires delivery of user-driven sets of virtual resources, dynamically allocated to actual substrate resources within networked clouds, creating the need to revisit resource mapping algorithms and tailor them to a composite virtual resource mapping problem. In this paper, toward providing a unified resource allocation framework for networked clouds, we first formulate the optimal networked cloud mapping problem as a mixed integer programming (MIP) problem, indicating objectives related to cost efficiency of the resource mapping procedure, while abiding by user requests for QoS-aware virtual resources. We subsequently propose a method for the efficient mapping of resource requests onto a shared substrate interconnecting various islands of computing resources, and adopt a heuristic methodology to address the problem. The efficiency of the proposed approach is illustrated in a simulation/emulation environment, that allows for a flexible, structured, and comparative performance evaluation. We conclude by outlining a proof-of-concept realization of our proposed schema, mounted over the European future Internet test-bed FEDERICA, a resource virtualization platform augmented with network and computing facilities.

Cloud-oriented content delivery networks (CCDNs) constitute a promising alternative to traditional content delivery networks. Exploiting the advantages and principles of the cloud, such as the pay as you go business model and geographical dispersion of resources, CCDN can provide a viable and cost-effective solution for realizing content delivery networks and services. In this paper, a hierarchical framework is proposed and evaluated toward an efficient and scalable solution of content distribution over a multiprovider networked cloud environment, where inter and intra cloud communication resources are simultaneously considered along with traditional cloud computing resources. To efficiently deal with the CCDN deployment problem in this emerging and challenging computing paradigm, the problem is decomposed to graph partitioning and replica placement problems while appropriate cost models are introduced/adapted. Novel approaches on the replica placement problem within the cloud are proposed while the limitations of the physical substrate are taken into consideration. The performance of the proposed hierarchical CCDN framework is assessed via modeling and simulation, while appropriate metrics are defined/adopted associated with and reflecting the interests of the different identified involved key players.

The cloud represents a computing paradigm where shared configurable resources are provided as a service over the Internet. Adding intra- or intercloud communication resources to the resource mix leads to a networked cloud computing environment. Following the cloud infrastructure as a Service paradigm and in order to create a flexible management framework, it is of paramount importance to address efficiently the resource mapping problem within this context. To deal with the inherent complexity and scalability issue of the resource mapping problem across different administrative domains, in this paper a hierarchical framework is described. First, a novel request partitioning approach based on Iterated Local Search is introduced that facilitates the cost-efficient and online splitting of user requests among eligible cloud service providers (CPs) within a networked cloud environment. Following and capitalizing on the outcome of the request partitioning phase, the embedding phase-where the actual mapping of requested virtual to physical resources is performed can be realized through the use of a distributed intracloud resource mapping approach that allows for efficient and balanced allocation of cloud resources. Finally, a thorough evaluation of the proposed overall framework on a simulated networked cloud environment is provided and critically compared against an exact request partitioning solution as well as another common intradomain virtual resource embedding solution.

This paper presents our work toward organizing and managing various forms of federations of virtualized infrastructures. We adopt the Ponder2 policy framework and the SMC architecture as a powerful engineering approach, which we apply to semantic-aware management of federations of Future Internet (FI) virtualized infrastructures. To cater for context-awareness, we plan for a common information model, based on the Network Description Language (NDL), capturing a common set of abstractions of virtualized resources and services, nodes, routers and switches, custom network topologies with specific bandwidth demands, etc. To handle management of generic complex federated environments, we employ structural patterns to model federations as graphs, whose vertices represent SMCs and edges denote the type of relationship between them. We give an illustration of such structures corresponding to existing FI experimental platforms in the US and Europe and we provide examples containing inter-domain management responsibilities as missions. Finally, we propose to augment the Ponder2 framework with single & multi-domain resource provisioning capabilities, enabling efficient sharing of virtualized networked facilities among federation users.

Mobile and wireless technology has revolutionized people’s lives by enabling data delivery anytime, anywhere, liberating the users from the restrictions of wired networks. In addition broadband communications permit high speed data connections. Since multimedia content delivered on-line requires reasonably high data rates and today’s wireless networks (3G, Wi-Fi, WiMAX etc) can support the quality requirements of media streaming delivery, while mobile devices handheld or not (Pocket PC, smart phones, laptop pc etc) are able to accommodate a variety of content formats, content delivery has become an indisputable trend. This paper presents such a content delivery network, the ENAMORADO architecture, which is the outcome of the work carried out in the IST project ENAMORADO (Enabling Nomadic Agents in a Multimedia Oriented Architecture of Distributed Objects). The ENAMORADO architecture covers the full content delivery chain from the media producers to the end users incorporating the content production, annotation, aggregation, adaptation and delivery. The paper describes the reference architecture of the infrastructure as well as the testbed used in the components integration and validation process. In addition further system evaluation results are presented.