The evolution towards 6G edge-cloud ecosystems demands autonomous, intent-based network management to handle unprecedented infrastructure complexity. While Large Language Models offer promising capabilities for translating high-level user intents into network configurations, current monolithic approaches suffer from cognitive overload, hallucinations, and a profound inability to safely execute low-level data plane mutations. To bridge this gap, we introduce Edgent, a novel framework that integrates hierarchical Agentic AI with Extended Berkeley Packet Filter technologies via the Model Context Protocol. Edgent utilizes a state-driven Supervisor, enhanced by Retrieval-Augmented Generation, to decompose abstract human intents into deterministic execution graphs and dynamically delegate tasks to domain-specific worker agents. We empirically validate the framework by autonomously deploying a distributed, in-kernel DDoS mitigation pipeline across scaled containerized topologies containing up to 85 nodes. Extensive evaluations demonstrate high orchestration reliability; notably, even heavily quantized Small Language Models (e.g., 4B parameters) achieve near-perfect zero-shot execution and 100% overall task completion through autonomous error recovery. Finally, latency and resource profiling confirm that the multi-agent framework can be efficiently driven by fully localized models compatible with orchestration tasks directly within resource-constrained edge environments, therefore this work positions Edgent as a pragmatic step toward the realization of zero-touch nextgeneration networks.

Traditional SDN controllers are usually deployed as monolithic systems or tightly coupled service chains, limiting their adaptability in distributed or federated network domains. This paper presents eMSN, a microservice-based SDN framework that enables controller decomposition and decentralized multidomain orchestration, providing a foundation for scalable, and domain-aware SDN experimentation. The framework introduces lightweight, containerized microservices that interact via REST APIs and coordinate through a shared ETCD cluster. The main one, called FlowBlocker, collects topology and host information from the emitter, builds a policy-aware decision table, and shares domain-scoped state in ETCD. The latter stores only host/topology data and never flow rules; enforcement decisions remain within FlowBlocker, which installs rules locally via Ryu Core or coordinates with peer FlowBlockers across domains. The architecture supports centralized, partially decentralized, and fully decentralized deployment models. The proof-of-concept implementation uses Docker and Mininet for reproducibility. Functional evaluation demonstrates sub-millisecond Packet-In responsiveness, tens-of-milliseconds policy enforcement latency, and correct blocking of unauthorized traffic.

Software-Defined Networking traditionally relies on the separation of the control and data planes, centralizing network intelligence within a logically unified controller. However, centralizing control functionalities often introduces limitations that negatively impact the overall Quality of Service, particularly in distributed and heterogeneous network scenarios. In this paper, we explore an adaptive approach to orchestrate a microservice-based SDN controller dynamically at the Edge. Building upon our previously introduced frameworks for Microservice-based SDN Controller and for flexible service-model-aware orchestration, we investigate the benefits of adaptively deploying our microservice-based SDN controller’s functionalities at the Edge to enhance QoS. We leverage our orchestration framework to dynamically decide and execute the optimal placement of latency-critical microservices according to real-time monitoring data and evolving user demands. We evaluate the performance by comparing various deployment strategies, focusing on the tradeoff between control plane latency and placement of controller functionalities. Results demonstrate that our adaptive Edge deployment approach has the potential to reduces control plane latency, demonstrating the practical benefits of integrating SDN controller modularity with intelligent service orchestration in dynamic and heterogeneous network environments.

Traditional Cloud-based multimedia transcoding approaches struggle with issues like latency and bandwidth limitations. In this work, we experiment with strategies that exploit Edge computing capabilities to get around such constraints. We focus on an on-demand multimedia transcoding service for real-time video streaming on the uplink, leveraging a state-of-the-art Edge Computing service orchestrator to handle its provisioning. We regard this as a use case for the orchestration platform, with the objective of enhancing the experience of the user as well as keeping important metrics in check, including resource utilization and load distribution over the Edge compute nodes. We perform evaluations on a physical testbed to assess the performance of the Edge-based system using real-world video transcoding workloads.

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.

Kubernetes (K8S) is a widely used orchestration solution that helps manage complex IT applications by providing mechanisms for autoscaling, health checking, cluster formation, and replication, which are essential to deploy and manage the multitude of connected microservices. However, they may suffer in case of unexpected faults which can severely change the underlying computing infrastructure and lead to service outages, highlighting the need for resilient solutions capable of mitigating the adverse effects of faults. To address this, the TELKA sched-uler integrates Chaos Engineering (CE), Reinforcement Learning (RL), and Digital Twin (DT) to reallocate K8S pods evicted due to unexpected faults. While TELKA showed promising results in reallocating evicted pods, its preliminary implementations suffered from scalability issues, as the RL agent could only effectively operate on scenarios with the same number of nodes seen during training. To overcome this limitation, this paper improves TELKA by incorporating a neural network architecture called Deep Sets (DS), which can generalize the operation of TELKA on different numbers of nodes. Experimental results not only demonstrate the validity of the improved TELKA but also show how it can be used to identify good operating conditions.

The Edge-Cloud Continuum represents a paradigm shift in distributed computing, seamlessly integrating resources from cloud data centers to edge devices. However, orchestrating services across this heterogeneous landscape poses significant challenges, as it requires finding a delicate balance between different (and competing) objectives, including service acceptance probability, offered Quality-of-Service, and network energy consumption. To address this challenge, we propose leveraging Multi-Objective Reinforcement Learning (MORL) to approximate the full Pareto Front of service orchestration policies. In contrast to conventional solutions based on single-objective RL, a MORL approach allows a network operator to inspect all possible “optimal” trade-offs, and then decide a posteriori on the orchestration policy that best satisfies the system’s operational requirements. Specifically, we first conduct an extensive measurement study to accurately model the energy consumption of heterogeneous edge devices and servers under various workloads, alongside the resource consumption of popular cloud services. Then, we develop a set-based MORL policy for service orchestration that can adapt to arbitrary network topologies without the need for retraining. Illustrative numerical results against selected heuristics show that our MORL policy outperforms baselines by 30% on average over a broad set of objective preferences, and generalizes to network topologies up to 5x larger than training.

Chaos engineering is the discipline of injecting computing and network faults, such as increased network latency and unavailability of computing nodes, into an IT system to help developers in identifying problems that could arise in a production environment and tackle them. Several tools have emerged to ease the application of chaos engineering to complex IT systems, leveraging microservice and container-based applications deployed on Kubernetes. However, applying of such tools requires several phases to be put into practice, from defining a steady state to establishing an effective response plan if something goes wrong. To ease the application of chaos engineering in improving the resilience of Kubernetes applications, this work presents a smart scheduler for Kubernetes called TELKA: a Twin-Enhanced Learning for Kubernetes Applications, which combines chaos engineering, Digital Twin (DT), and Reinforcement Learning (RL) methodologies to mitigate the effects of computing and network faults. Instead of interacting directly with the physical Kubernetes application, TELKA learns by interacting with a digital twin, thus reducing the learning time and the operation costs related to the application of chaos engineering. Experiment results compare TELKA with other approaches to show its effectiveness in mitigating the adverse effects of injected faults.

The last decade has witnessed the rise of Network Function Virtualization (NFV). Despite its benefits, resource allocation and traffic scheduling are still challenging. A practical issue is where to place Virtual Network Functions (VNFs) in the network to sustain long-term optimal objectives and when to reallocate resources given the dynamics of the substrate network. Most prior works either consider static settings or work in reactive fashions. This paper proposes a dual-window algorithm for proactive service redeployment and traffic routing. Specifically, our algorithm employs an entropy measure to gauge the uncertainty in the substrate network and cognitively updates the service redeployment interval to avoid unnecessary data collection overhead. Our algorithm is lightweight, intuitive, requires no offline training, and achieves the best overall effectiveness and efficiency compared with three state-of-the-art solutions.

The Edge Computing paradigm is increasingly gaining traction in modern telecommunication scenarios, as it enables the offloading of computational tasks from end devices to a variety of nodes located in close proximity to them. This approach is essential for meeting the ever-stricter Quality of Service requirements imposed by modern applications. Concurrently, the advent of Data Plane Programmability allows for unmatched flexibility on the networking plane, supporting processing of multiple protocols in a logically centralized fashion with simple in-line computation, and offering the possibility to offload additional services to networking equipment. Reaping those benefits necessitates heedful management of resources and infrastructure. This, in turn, calls for the introduction of a service orchestration entity, capable of taking advantage of device heterogeneity to enable efficient and swift service provisioning. This work delves into the potential of introducing an orchestration system able to cope with the challenges of offloading security tasks at the Edge. This effort involves developing and implementing novel architectural components that capitalize on the heterogeneous nature of the Edge infrastructure as well as of the Programmable Data Plane as a potential tool for service offloading. To establish the feasibility and performance of this approach, an industrial scenario is considered, where the integrity of data from legacy devices must be ensured. Following an evaluation of the hashing performance of the Programmable Data Plane in comparison to general-purpose devices, a simulation study is conducted on the overall orchestration system, demonstrating the viability of the proposed approach.

Kubernetes is a well-known orchestration and management solution for complex and large-scale service architectures in the Cloud Continuum. While it provides very valuable functions from the operation perspective, the high number of control loops it implements significantly enlarges the already wide space of configuration parameters and policies to consider for management purposes. We argue that optimizing complex Kubernetes deployments considering a multi-cloud and edge computing environment would significantly benefit from a Digital Twin approach, enabling an accurate virtual representation of a Kubernetes application to optimize its deployment and management policies. Towards that goal, this work illustrates the design of KubeTwin, a framework to implement Digital Twins of Kubernetes deployments. Furthermore, we present a validation of KubeTwin in a Multi-access Edge Computing (MEC) scenario, which shows its soundness in reenacting realistic Digital Twins of complex and highly distributed Kubernetes deployments. We believe that KubeTwin can provide useful guidance to the research community working in this field.

The use of microservice-based applications is becoming more prominent also in the telecommunication field. The current 5G core network, for instance, is already built around the concept of a “Service Based Architecture”, and it is foreseeable that 6G will push even further this concept to enable more flexible and pervasive deployments. However, the increasing complexity of future networks calls for sophisticated platforms that could help network providers with their deployments design. In this framework, a central research trend is the development of digital twins of the physical infrastructures. These digital representations should closely mimic the behavior of the managed system, allowing the operators to test new configurations, analyze what-if scenarios, or train their reinforcement learning algorithms in safe environments. Considering that Kubernetes is becoming the de-facto standard platform for container orchestration and microservice-based application lifecycle management, the implementation of a Kubernetes digital twin requires an accurate characterization of the microservice response time, possibly leveraging suitable Machine Learning techniques trained with measurement data collected in the field. In this paper we introduce a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications. We show the improvement in performance with respect to simulation-based inference procedures proposed in literature.

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation.

We consider the problem of designing and training a neural network-based orchestrator for fog computing service deployment. Our goal is to train an orchestrator able to optimize diversified and competing QoS requirements, such as blocking probability and service delay, while potentially supporting thousands of fog nodes. To cope with said challenges, we implement our neural orchestrator as a Deep Set (DS) network operating on sets of fog nodes, and we leverage Deep Reinforcement Learning (DRL) with invalid action masking to find an optimal trade-off between competing objectives. Illustrative numerical results show that our Deep Set-based policy generalizes well to problem sizes (i.e., in terms of numbers of fog nodes) up to two orders of magnitude larger than the ones seen during the training phase, outperforming both greedy heuristics and traditional Multi-Layer Perceptron (MLP)-based DRL. In addition, inference times of our DS-based policy are up to an order of magnitude faster than an MLP, allowing for excellent scalability and near real-time online decision-making.

Fog computing is a distributed paradigm that extends cloud computing closer to the edge of the network, and even beyond that. By employing local resources, it enables quicker and more effective data processing and analysis. The optimization and automation of resource allocation, data processing, and job scheduling in the fog environment are made possible by the application of machine learning to Fog Computing Orchestration. It is also important, when working with the network computing models, to consider the XaaS paradigm, as it promotes the flexibility and scalability of fog services, bringing the concept of “service” into the foreground. Therefore, the need for a fog orchestrator enabling such characteristics arises, leveraging AI and the “service-centric” approach to enhance users’ service fruition. The design and development of such an orchestrator will be the objective of the early-stage PhD project presented in this paper.

Mission Critical (MC) communications take a pivotal role to achieve effective Public Protection and Disaster Relief (PPDR) actions. Even though 3GPP standards define MC applications and services in an architectural framework compatible with current 5G mobile networks, real-life experiments and applications of these concepts are still at the very beginning. In this paper, we present an architectural study and related experimental activity on network slicing for MC communications. We implemented these services in a fully virtualized environment, and deployed and tested them in a multi-domain network slicing scenario compliant with the ETSI NFV-MANO specifications. Our work aligns with the 5G approach separating control and data planes. The level of automation in service deployment and the slice isolation features are demonstrated, showing the benefits in terms of application performance, management flexibility, scalability, and quality of service differentiation capabilities.

With the establishment of the Everything-as-a-Service (XaaS) paradigm for service provisioning, coupled with the increasingly-demanding requirements imposed by modern network services, the need for a XaaS-aware orchestration system able to cope with a heterogeneous infrastructure, such as the one of Fog Computing environments, is evident. In this work, we describe the working principles and implementation aspects that allow the orchestration of services offered according to the Function-as-a-Service (FaaS) model. The live demonstration will showcase the ability of the system to deploy this kind of services on a suitable test bed, with comments on the procedure and the performance.

The possibility of managing network infrastructures through software-based programmable interfaces is becoming a cornerstone in the evolution of communication networks. The Intent-Based Networking (IBN) paradigm is a novel declarative approach towards network management proposed by a few Standards Developing Organizations. This paradigm offers a high-level interface for network management that abstracts the underlying network infrastructure and allows the specification of network directives using natural language. Since the IBN concept is based on a declarative approach to network management and programmability, we argue that the use of declarative programming to achieve IBN could uncover valuable insights for this new network paradigm. This paper proposes a formalization of this declarative paradigm obtained with concepts from category theory. Taking this approach to Intent, an initial implementation of this formalization is presented using Haskell, a well-known functional programming language.

The maturity of heterogeneous and hybrid public Cloud environments enables service providers to deploy there their complex IT services trusting these large and complex infrastructures. At the same time, evaluating the impact of changes at service configuration before and at the runtime is still a very challenging and difficult task. Moreover, a comprehensive performance evaluation of IT service configurations should not be limited just to costs for IT resource acquisition, but also include risk related elements such as Service Level Agreement (SLA) violation penalties and other intangibles. To support IT service providers in this difficult task, we developed Business-Driven Management as a Service Plus (BDMaaS+), a novel decision support tool that can evaluate IT service configuration through simulation with realistic service and network models. By allowing service providers to define expanded operational parameters, BDMaaS+ also enables what-if scenario analysis, thereby opening interesting possibilities at the planning level. Experimental results, collected from our thorough evaluations, demonstrate how a service provider can leverage BDMaaS+ to explore the potential of high-level business SLA changes and data center additions.

Fog Computing can facilitate the adoption of the Everything-as-a-Service paradigm in infrastructure segments that are located closer to the end user, or to the data source, compared to typical Cloud solutions. This enables combining the advantages of flexible service deployment models with the need to cope with the strict requirements–especially in terms of latency–of emerging applications in softwarized networks. Along comes the need to consider aspects of service orchestration specific to the Fog environment and its intrinsically dynamic nature. In this paper we propose an architecture for flexible Fog Computing service orchestration, with a particular focus on the awareness of service deployment models. We discuss the design choices and describe the components and operations of the proposed orchestration system. We then present a complete working implementation of such architecture, including insights on its ability to handle critical orchestration functions such as service discovery and resource monitoring. We also report on the experimental validation of the system and the performance evaluation on real-world equipment, proving the feasibility and the effectiveness of the approach on a dynamic Fog infrastructure. We complement the work by presenting the results of a combinatorial analysis, validated by simulation, of the service model-aware resource selection process. As a result of our investigation, we show that Fog services can be effectively deployed in a matter of a few seconds, or even in less than one second when suitable Fog nodes are available, taking advantage of the awareness of the available service models.

Low-power wide-area network technologies are used to interconnect a number of devices in a simple and efficient way. One of these technologies, LoRaWAN, is deemed as one of the most promising due to its capability to allow long-range communications with very small energy consumption. LoRaWAN networks are managed by a network server implementing an adaptive data rate (ADR) algorithm to allocate proper data rates to end devices (EDs). However, the standard ADR solution focuses only on the link-level performance and assigns transmission parameters to EDs one-by-one in an independent way. In this article, we propose a novel and more efficient ADR algorithm, denoted as collision-aware ADR (CA-ADR), which tries to minimize the collision probability when assigning data rates by considering the entire set of EDs in the network and keeping the link-level performance under control. The performance of CA-ADR is characterized and benchmarked against the standard solution as well as another proposal presented in the literature. An integrated simulation-experimental approach is used to assess results for large-scale networks and to compare two architectures based on cloud and fog computing. Results show that CA-ADR outperforms standard solutions when connectivity is good, whereas it behaves similarly in large areas. It is also shown that the improvement with respect to the benchmark solutions does not depend on the channel model considered (no shadowing, uncorrelated, and correlated shadowing). Finally, a fog-based architecture is proved to be feasible, with the advantage of reducing the end-to-end latency.

The service function chaining paradigm links ordered service functions via network virtualization, in support of applications with severe network constraints. To provide wide-area (federated) virtual network services, a distributed architecture should orchestrate cooperating or competing processes to generate and maintain virtual paths hosting service function chains while, guaranteeing performance and fast asynchronous consensus even in the presence of failures. To this end, we propose a prototype of an architecture for robust service function chain instantiation with convergence and performance guarantees. To instantiate a service chain, our system uses a fully distributed asynchronous consensus mechanism that has bounds on convergence time and leads to a (1 - 1/e)-approximation ratio with respect to the Pareto optimal chain instantiation, even in the presence of (non-byzantine) failures. Moreover, we show that a better optimal chain approximation cannot exist. To establish the practicality of our approach, we evaluate the system performance, policy tradeoffs, and overhead via simulations and through a prototype implementation. We then describe our extensible management object model and compare our asynchronous consensus's overhead against Raft, a recent decentralized consensus protocol, showing superior performance. We furthermore discuss a new management object model for distributed service function chain instantiation.

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms.

Mission Critical (MC) communications are key to effective Public Protection and Risk Reduction (PPRR) actions. The 3GPP standards include the definition of MC applications and services in an architectural framework compatible with current (LTE) and future (5G) mobile networks. In this paper we report an experimental activity where MC communication services are implemented in a fully virtualized environment, being deployed and tested in a multi-domain network slicing architecture compliant with the ETSI NFV MANO specifications. The level of automation in service deployment and the slice isolation features are demonstrated, in line with the 5G approach of separation between control and data plane, showing the benefits in terms of application performance and management flexibility.

In this manuscript we describe an experimental work that integrates the NFV-MANO framework with segment routing to support 5G network slicing. The aim is to implement Service Function Chains spanning several cloud domains and the related interconnection transport network in a coordinated way. The manuscript shows the feasibility and the performance effectiveness of this approach, reporting numerical results from practical experiments.

The increasing number of devices and applications requesting external processing and storage facilities with reduced access latency has led to the introduction of edge computing solutions. Among others, Fog Computing can be considered as an edge computing solution enabling the edge devices to offer general-purpose processing and storage capabilities. Despite a huge research effort for proposing efficient Fog Computing solutions, their implementability is still under study. By resorting to the Cloud Computing service models, we propose here an Android-based proof-of-concept solution allowing to implement different Fog services in an edge scenario by using off-the-shelf end-user devices.

A key aspect that Telco operators must carefully consider when deploying Network Function Virtualization (NFV) solutions is the level of performance that cloud computing software platforms can guarantee in support of the offered network services. OpenStack is widely considered as one of the most relevant open-source frameworks that could accelerate the NFV adoption, also because the evolution of its networking components sees a progressive integration of SDN-based solutions that could significantly improve the performance of cloud-based connectivity services. In this paper, we discuss some of the most recent OpenStack innovations that enable a native SDN-like approach to firewalling functions in the data plane, as well as a native SDN-oriented control of the virtual network infrastructure. Then we present a detailed performance analysis of the aforementioned innovations at both the data and control/management plane, showing the potentials of native SDN adoption within OpenStack toward an integrated solution for production-level NFV deployments.

The Cloud computing paradigm is enabling innovative and disruptive services by allowing enterprises to lease computing, storage and network resources from physical infrastructure owners. This shift in infrastructure management responsibility has brought new revenue models and new challenges to Cloud providers. One of those challenges is to efficiently migrate multiple virtual machines (VMs) within the hosting infrastructure with minimum service interruptions. In this paper we first present a live-migration performance testing, captured on a production-level Linux-based virtualization platform, that motivates the need for a better multi-VM migration strategy. We then propose a geometric programming model whose goal is to optimize the bit rate allocation for the live-migration of multiple VMs and minimize the total migration time, defined as a tradeoff cost function between user-perceived downtime and resource utilization time. By solving our geometric program we gained qualitative and quantitative insights on the design of more efficient solutions for multi-VM live migrations. We found that merely few transferring rounds of dirty memory pages are enough to significantly lower the total migration time. We also demonstrated that, under realistic settings, the proposed method converges sharply to an optimal bit rate assignment, making our approach a viable solution for improving current live-migration implementations.

This paper presents the definition and a proof-of-concept implementation of an intent-based northbound interface (NBI) used to orchestrate dynamic service chaining of Virtualized Network Functions (VNFs) by actively controlling the underlying network infrastructure through vendor-independent, technology-agnostic policies. The proof of concept considers a general scenario where VNFs are hosted in different SDN domains, possibly interconnected by non-SDN domains. Being implemented as part of a Virtualized Infrastructure Manager, the proposed NBI is compliant with the ETSI NFV management and orchestration specifications, as well as with the recent ONF definition of intent-based NBI. The case of a network operator that provides adaptive quality of service enforcement in a multi-tenant scenario is considered. Responsiveness of the intent-based NBI is experimentally evaluated under increasing load, proving the correct functionality and the scalability potentials of the proposed approach.

Content Distribution Networks (CDNs) could benefit from peer-to-peer (P2P) network techniques to improve content delivery time by leveraging the upload capacity of the entire network. In most available solutions, peers first select a set of partners, and later select the pieces of content to exchange. It is fundamental instead that both peer and piece selection algorithms are performed in a consistent and integrated way. To this aim, we propose a content distribution protocol that unifies piece and peer selection in a single algorithm which optimizes the swarming effects. Our approach leverages Flajolet-Martin sketches, a technique based on Bloom filters, to estimate the number of distinct pieces in the CDN, and then adopts a fractional knapsack problem approach to effectively utilize the entire upload capacity of each peer. We tested our solution with both simulations and Planetlab experiments, showing how the piece estimation at every peer is a good approximation of the global rarest piece across the network, and not just across the first hop neighborhood. Moreover, we show how our solution improves the average downloading time by up to 20%. If we consider only the fastest 50% of peers, the downloading time is improved by 100%. Furthermore, our solution decreases the average first content uploading time by 80% with respect to standard P2P protocols which use a local rarest first piece selection, and tit-for-tat as peer selection algorithm.

This manuscript investigates the issue of implementing chains of network functions in a “softwarized” environment where edge network middle-boxes are replaced by software appliances running in virtual machines within a data center. The primary goal is to show that this approach allows space and time diversity in service chaining, with a higher degree of dynamism and flexibility with respect to conventional hardware-based architectures. The manuscript describes implementation alternatives of the virtual function chaining in a SDN scenario, showing that both layer 2 and layer 3 approaches are functionally viable. A proof-of-concept implementation with the Mininet emulation platform is then presented to provide a practical example of the feasibility and degree of complexity of such approaches.

Emerging network paradigms, such as Software Defined Networking and Network Function Virtualization, represent the key enablers for efficient and cost-effective deployment and management of cloud-based edge networks, where a number of cooperating virtual machines can implement the tasks traditionally performed by expensive and disrupting network middleboxes. A key feature in such a scenario is the capability of live migrating a group of correlated virtual machines as a single entity representing a customer's profile. Multiple VM live migration is a topic that has not been thoroughly studied in the literature. This manuscript presents a relatively simple model that can be used to derive some performance indicators, such as the whole service downtime and the total migration time, and allows to properly design the cloud-based edge network. The model is used to compare some simple scheduling strategies for the VM migration and to provide guidelines to such implementation.

The joint management of IT and network resources is a key aspect for dynamic inter-data center interconnection networks designed to work according to the cloud computing paradigm. Such a scenario calls for the anycast routing of service requests to find a data channel that best suits both connectivity and IT resource requirements. This paper presents different network resource allocation and release policies across an inter-data center interconnection network aimed at a balanced accommodation of network resources to improve performance. Such policies are evaluated and compared in terms of service set-up blocking probability considering also configuration latency of real network devices that play a significant role along with network bandwidth availability across the network. Results show that the best choice is to release network resources as soon as possible, even though this means increasing the service blocking due to network reconfiguration latency.

This work presents a signaling framework for the Future Internet capable of enabling unified network and IT resource allocation. The proposed signaling scheme is based on existing technologies and carried out through extensions to protocols already operated in current Internet, i.e., SIP, and by resorting to source-initiated GMPLS signaling without introducing network technology- dependent mechanisms. The experimental validation presented in this work has been carried out on a real test-bed. Performance of the service set-up procedure are reported taking into account the response time of real life devices such as commercial routers and protocols. The results provided show that the architecture proposed is compliant with conventional performance levels such as those set by the ITU- T. Finally, scalability performance have been verified by stressing the system with increasing rate of simultaneous service requests.

In this paper end-to-end performance is evaluated when multiple high-speed access networks, based on the Ethernet Passive Optical Network (EPON) technology, are interconnected through an Optical Burst Switching (OBS) core network. The inter-working unit, or edge node, between these two different networks is properly studied and discussed. A time-based burst assembly algorithm is assumed, which is analyzed and dimensioned to cope with multiple EPONs. The role of a key parameter, such as the EPON cycle time used in the multiple access scheme, is assessed. Transport layer performance of the multi-EPON/OBS network is evaluated through simulations with ns2. Results show that the relationship between the EPON cycle time and the burst assembly time has a fundamental role and requires proper tuning to optimize, e.g., the TCP throughput.

In this paper the performance of a hybrid optical network composed of multiple Ethernet Passive Optical Networks (EPONs) at the access connected to an Optical Burst Switching (OBS) core is evaluated. The EPONs make use of the IPACT algorithm to manage the upstream transmissions, whereas the OBS edge node employs a time-based assembly scheme for burst aggregation. This leads to interoperability issues between access and core which are investigated in this paper. The hybrid network scalability is proved by assessing the transport layer performance with different numbers of EPONs served by the same burst assembler and different numbers of Optical Network Units (ONUs) per EPON. The impact of the main design parameters affecting the interaction between EPONs and OBS is estimated. The transport layer performance is evaluated considering heterogeneous traffic flows by simulating both TCP and UDP sources.

Dynamic, flexible and programmable network nodes will enable the key infrastructure for the Future Internet. Reengineering of the traditional router architectures is required in order to allow hardware- and vendor-independent routing functions and to design open and cost-effective service platforms. This paper presents a modular programmable router architecture to support multi-granular routing based on optical switching matrices, according to recent IETF standards. A possible implementation of the proposed concepts is demonstrated using a software-based optical router emulator, which integrates optical packet and circuit switching paradigms. The emulator is designed as a test platform to prove the feasibility of different subsystems and their interoperability.

Programmable optical networks are a key solution to high performance dynamic network services in Future Internet scenario. Modular router architecture which meets this concept is here defined and represented in a flexible software context, using Click! tools. Evaluation of logical performance and testing of physical characteristics of information forwarding within this framework is shown as feasible. The approach allows also to easily test functions and interactions of control and data planes to support enhanced future Internet services. Sample implementations of programmable router subsystems are given and discussed.

In this paper we propose a service-oriented networking architecture that, by exploiting the Session Initiation Protocol (SIP), is able to support a population of users moving within a network of trusted domains over wireless and/or wired connections. The proposed architecture combines the flexibility of a peer-to-peer connectivity network with the efficient signaling organization of a hierarchy of domains.

Optical networking paradigms based on statistical multiplexing, such as Optical Burst Switching or Optical Packet Switching, require the adoption of suitable contention resolution mechanisms at the optical nodes due to packets/bursts attempting to get access to the shared output channel at the same time. In the most general case of multi-fiber output links, contentions are tried to be solved by exploiting different domains — namely space, wavelength and time — and by applying an optimal scheduling policy. This paper focuses on the key parameters that can be used to design and optimize an optical packet/burst contention resolution scheme and shows how such parameters should be correlated for a correct performance assessment, taking into account feasibility trade-offs due to limited optical buffering and wavelength conversion capability.

This paper presents a novel application-aware network architecture for evolving and emerging IT services and applications. It proposes and analyses network architectures that integrate Session Initiation Protocol (SIP) with Optical Burst Switched (OBS) protocols on a unified manner. We suggest various SIP-OBS layering architectures for possible deployment as well as a number of end-to-end resource discovery protocols (both for network and non-network resources). Finally the paper reports of a SIP-enpowered OBS Testbed where this approach was experimentally validated.

Optical burst and packet switching are being considered as the most promising paradigms to increase bandwidth efficiency in IP over DWDM networks. In both cases, due to statistical multiplexing, a scheduling policy is needed to solve contentions at the node level caused by more than one burst/packet directed to the same output channel. The scheduling may be performed in the wavelength, time and space domains, turning this problem into a choice of the best available resource in the different domains. Because of the very large bandwidth on the wavelength channels, the burst/packet arrival rate at the network nodes is very high and therefore the time available to take the scheduling decision is very limited. As a consequence, whatever scheduling policy is adopted, it must be implemented in an extremely effective way in terms of computational complexity. This paper is focused on a cost-effective solution to the scheduling problem, which is not based on typical search algorithms and whose complexity can be made almost independent of the dimension of the resource set to be searched.

This paper proposes a methodology to compare scheduling algorithms for congestion resolution in optical burst switching or optical packet switching networks, focusing on the issue of wavelength conversion capabilities. The performance of such algorithms when full wavelength conversion is available at the switching nodes are compared with the case of conversion capabilities over a limited range of wavelengths. The results presented show that limited range wavelength conversion is not necessarily performing worse than full wavelength conversion, as long as the correct terms of comparison are considered.

Next generation optical network technologies such as OPS and OBS have a non-negligible impact on the performance of the transport layer. This is related to how a packet stream traversing the optical network is affected by random behaviors such as latency variability, out-of-sequence delivery and loss. This paper tries to better understand how the use of contention resolution schemes at the optical layer affects the TCP performance, focusing in particular on the impact of unordered delivery and loss of packets.

In this paper, promising technologies and a network architecture are presented for future optical packet switched networks. The overall network concept is presented and the major choices are highlighted and compared with alternative solutions. Both long and shorter term approaches are considered, as well as both the wide-area network and multiple-area networks parts of the network. The results presented in this paper were developed in the frame of the research project DAVID (Data And Voice Integration over DWDM) project, funded by the European Commission through the IST-framework.

This paper addresses the problem of buffering performance optimization for asynchronous, variable length packets in a DWDM optical network. In the case of a multiplexing scheme that shares the wavelength resource among all packets regardless the connection they belong to, the problem of defining a wavelength allocation algorithm arises. Wavelength allocation has to be merged with delay allocation in buffers realized by means of fiber delay lines (FDLs). This paper proposes several allocation algorithms and compares their performance. It shows that the buffering performance strongly depends on the choice of the delay unit of the FDL's and that the algorithm aiming at reducing the artificial increase in the load due to the discrete time scale of the buffer gives best performance.