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.

This demonstration addresses a key open challenge in Open Radio Access Network (O-RAN) deployments: how to intelligently allocate Transport Network (TN) resources to ensure low-latency for mission-critical applications. The demo emulates a Smart Factory scenario where the time-sensitive control traffic of robotic arms competes with industrial camera broadband video streams. We propose an intelligent transport controller that combines network slicing, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Federated Learning (FL) to dynamically prioritize traffic per slice. The architecture uses $\mathbf{P 4}$ switches for local queue monitoring and real-time resource scheduling. The integration with the O-RAN disaggregated stack is based on Open Air Interface (OAI). Experimental results demonstrate valuable load balancing and buffer occupation reduction in the O-RAN midhaul.

This paper presents an in-network machine learning (ML) approach for classifying Augmented Reality (AR) and Cloud Gaming (CG) traffic using programmable hardware. Random Forest (RF) models are deployed in a P411P4: Programming Protocol-independent Packet Processors data plane capable of processing Real-time Transport Protocol (RTP) traffic features like Frame Size (FS) and Inter-Frame Interval (IFI) for efficient classification. The classifier marks AR and CG traffic with Explicit Congestion Notification (ECN) codepoints to integrate with the Low Latency, Low Loss, Scalable Throughput (L4S) features of the programmable switch. The RF model prioritizes AR/CG traffic using Differentiated Services Code-Point (DSCP) assignments and modular ECN marking. The classification performance is evaluated using accuracy, precision, recall, and F1-score, while time overhead is assessed based on nodal processing time incurred during deployment by replaying AR/CG traffic. The P4 implementations for P4Pi22https://eng.ox.ac.uk/computing/projects/programmable-hardware/p4pi.(V1Model) and Tofino Native Architecture (TNA) are all publicly available.

Cloud Gaming (CG) research faces challenges due to the unpredictability of game engines and restricted access to commercial platforms and their logs. This creates major obstacles to conducting fair experimentation and evaluation. CGReplay captures and replays player commands and the corresponding video frames in an ordered and synchronized action-reaction loop, ensuring reproducibility. It enables Quality of Experience/Service (QoE/QoS) assessment under varying network conditions and serves as a foundation for broader CG research. The code is publicly available for further development11https://github.com/dcomp-leris/CGReplay.git.

Extended reality (XR) services form the basis of various innovative applications. These new applications are expected to run on mobile devices with limited computational and energy capabilities. At the same time, XR services should fulfill some expectations regarding data rates and end-to-end latency to guarantee an uninterrupted user experience. In this context, offloading intensive computation to the edge is particularly advantageous for mobile devices, enabling access to more capable processing hardware. Current studies on computational offloading focus mainly on the compute and network continuum formed between edge and cloud and realized mostly through containers. Conversely, this demonstration showcases a computational offloading framework that allows the expansion of XR services functionality from the connected mobile device to an edge computing environment. The presented framework is based on a portable, lightweight, and secure WebAssembly runtime and uses open technology implementations. We demonstrate that the developed framework allows significant performance improvements in a Yolo object detection application and reduces heat generation on mobile devices.

In current computing processing infrastructures, optimizing performance and energy efficiency across heterogeneous hardware environments remains a critical challenge. Recently, the growth of different types of hardware architectures has raised challenges but also opportunities. In this paper, we present Decomposer, designed to decompose monolithic applications into modular components, allowing them to be executed across various combinations of hardware architectures, both locally and in distributed settings. Using static and dynamic analysis, Decomposer allows flexible allocation of functions to different targets, optimizing both execution time and energy consumption. Our approach involves the generation of Intermediate Representations (IR) using LLVM and MLIR, allowing the identification of functions for decomposition and their distributed execution on heterogeneous hardware. By expanding execution options, Decomposer provides flexibility in resource allocation without increasing execution times. Furthermore, we observed that such decomposition improves cache utilization, reducing L1 cache misses and$\mathbf{L}2$references in more than 50 %, thus enhancing memory efficiency. This work, although in its initial phase, shows that this approach allows for dynamic workload distribution across multiple targets, offering a more versatile and adaptable solution for application execution, aligning resource usage with operational objectives and environmental considerations. We envisage that the compilers area and its techniques can be very useful for optimizing a set of applications to be decomposed and run not only in core data centers but also at the edge 5G network.

Augmented Reality (AR) real-time interaction between users and digital overlays in the real world demands low latency to ensure seamless experiences. To address computational and battery constraints, AR devices often offload processing-intensive tasks to edge servers, enhancing performance and user experience. With the increasing adoption and complexity of AR applications, especially in remote rendering, accurately classifying AR network traffic becomes essential for effective resource allocation. This paper explores two methods based on Decision Tree (DT) and Random Forest (RF) to classify network traffic among AR, Cloud Gaming (CG), and other categories. We rigorously analyze specific features to precisely identify AR and CG traffic. Our models demonstrate robust performance, achieving accuracy rates ranging from 88.40% to 94.87% against pre-existing datasets. Moreover, we contribute with a novel dataset encompassing AR and CG traffic, curated specifically for this study and made publicly available to facilitate reproducible research in AR network traffic classification.

This work delves into the vital role of TLS (Transport Layer Security) in securing web applications today. We explore kTLS (Kernel TLS) offloading as a possible solution to alleviate resource strain such as CPU time, power consumption, and network speed. By shifting cryptographic tasks closer to the CPU in software offloading or away from the main CPU in hardware offloading, kTLS can improve resource efficiency. Our experimental studies assess various offloading strategies, including software-based kTLS that bring it closer to the Kernel and cutting-edge hardware-accelerated modes such as TOE and coprocessor configurations using the Chelsio T6 SmartNIC. We highlight the immense potential of kTLS and network adapters in reshaping performance and efficiency dynamics for some network environments, considering each approach’s benefits and potential drawbacks.

A way to contribute to network management involves detecting high-impact traffic flows, known as Heavy Hitters. Heavy Hitters are flows that account for the majority of bytes transmitted over the network, consequently consuming more resources. The use of programmable hardware, such as switches and DPUs, allows for the detection of these flows in-line rate, aiming to optimize their performance. While the literature reveals an extensive analysis of detection in single-pipe switches, this study presents two approaches to identify Heavy Hitters in programmable switches with multiple pipes. One approach has an accumulator in the switch, which centralizes data from all pipes and communicates with the control plane. In the other approach, communications with the control plane are independent for each pipe. Both approaches were developed and validated through an emulator, demonstrating effectiveness and improvement in detection in multi-pipe switches compared to single-pipe switches.

This study explores the application of Time Series GAN in a Programmable Data Plane (PDP) for enhancing Reinforcement Learning within the context of computer networks, particularly in video applications. We address various challenges, including dataset augmentation, balancing, and extended RL training times in real setups. By leveraging synthetic data generated by TimeGAN, we accelerate experimentation, enhance dataset diversity, and simplify RL model training, ultimately evaluating TimeGAN’s performance against real setups in resource optimization for PDPs using an RL agent. This research contributes by directly comparing GAN usage and real setups, bridging a gap in computer network literature, and highlighting a 99% similarity in Quality of Service achieved by an RL model trained with synthetic data, affirming TimeGAN’s potential as a valuable simulator without compromising RL training efficacy.

Cloud Network Slicing (CNS) is a concept that describes a mechanism to provide computing, networking, and storage as a virtual slice entity, enabling new approaches to applications and structuring resources at the edge of the network. In this paper, the architecture defined in the NECOS Project is adopted, and the functions for creating CNS in resource-constrained edge devices were designed and implemented. The implementation was evaluated on Single Board Computers (SBCs), using lightweight virtualization solutions (microservices) and the results achieved show that it is possible to instantiate CNSs on that hardware, however, also show some limitations of multiple slice support on resource-constrained devices.

InFaRR (In-network Fast ReRouting) is an algorithm for fast rerouting in programmable data planes. Implemented in P4, InFaRR is free of additional headers and network state heartbeats. InFaRR has four essential features not jointly found in other recovery mechanisms: Loop prevention, Pushback, Recognition and Restoration and Return to the main route. Tests in standard Fat-Tree and AB Fat-Tree topologies with failures in different scenarios showed positive results when compared to state-of-the-art algorithms in the literature. In scenarios in which the other algorithms were able to recover, InFaRR presented fewer hops to bypass the failure when the Pushback, Loop Prevention and Recognition and Restoration mechanisms were used. In scenarios with multiple failures, InFaRR successfully rerouted where the others algorithms in some cases looped. The mechanism for returning to the main route allows to verify failures in remote links, making it possible to return to the main route without intervention from the control plane. Several evaluations were done comparing the results of InFaRR with the state-of-the art mechanisms, showing the capability of our fast rerouting algorithm in dealing with failures at the line-rate.

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.

Recently, several applications have been designed and implemented to run entirely in the dataplane. However, most if not all the applications assume that network traffic traverses the same pipe, from ingress to egress inside the switch. While this seems to be a natural assumption, it does not hold for current programmable hardware that supports two to four pipes and network traffic is spread among the different pipes. As a consequence, several applications may not work properly in a multi-pipe architecture and need to be redesigned to fit into such architectural constraint. In this paper, we call the attention to this challenge and elaborate on an initial solution for counting heavy hitters (HH) in a multi-pipe hardware (MPHH). Our solution keeps the HH counter only in the egress pipeline while temporarily caching the hashes at the ingress pipeline. We then carry the hashes from ingress to egress by using data packets so that the HH are counted only in the egress pipeline. We present our design around this issue, the challenges observed so far and some initial results.

The concept of cloud network slice features not only an end-to-end infrastructure with different types of resources (cloud, network and storage), but also the ability to provide services across multi-domain infrastructures. Therefore, an important challenge encountered is the ability of a slice to increase or decrease its resources according to SLAs/SLOs defined by a Tenant. This demo aims to present the implementation of a monitoring system architecture for cloud network slices, in which such main characteristics can be highlighted: (i) elasticity - as it is able to adapt as new elements are instantiated or removed in the slice; and (ii) heterogeneity - as it runs across multiple administrative and technological domains; which are intrinsic features of the cloud network slice concept.

A key feature of the cloud network slicing concept is the dynamic (de)provision of end-to-end infrastructures composed by computing, network, and storage resources, in order to meet the service needs of a variety of vertical industries. The resulting resource ensemble needs to be instantiated over a (potentially large) number of administrative and technological domains – a complex challenge for the management and orchestration of the allocated resources. Resource monitoring in this new so-called slice entity is extremely important in order to address the above operations. Therefore, in this work, we discuss the design and implementation of an elastic architecture for the monitoring of physical and virtual resources in cloud network slices that span both multiple administrative and technological domains. Since the concept of cloud network slices is quite recent in the literature, we highlight that our results present the performance evaluation of the proposed monitoring architecture when instantiating, stopping or performing elasticity operations on the slices. Additionally, we also analyze the performance of the architecture by scaling the number of metrics being monitored in overloaded scenarios.

To support future 5G computing and communication scenarios, cloud-network management tools should deploy cloud-network services adopting uncomplicated ways, reducing not only the time to market but also broadening the community capable of deploying new services. In this paper, we present the support of NECOS Platform, an EU-Brazil jointly funded project, towards slice-as-a-service creation from non-structured service description. We describe how NECOS architecture allows such functionality during the slice creation loop, and we present the initial efforts we took for structuring such a mechanism.

Over the years, network monitoring tools have evolved to keep pace with the advances in networking technologies and paradigms, such as virtualization and softwarization, and more recently, data plane programmability. However, there have been few advances regarding user interfaces and interactions in network management systems. In this paper, an investigation based on the human-computer interaction perspective is presented and improvements on the usability aspects of network monitoring tools are proposed. First, we conducted a survey to capture general information from network administrator professionals. On the basis of these initial data, we identified and specified two personas to represent the groups of users. Thereafter, a study focused on observing users' interaction and experience with a well-known network monitoring tool was conducted. Finally, our qualitative analysis revealed several findings that were used to develop recommendations to aid developers in the (re)design of these tools to enhance end-user interaction.

Cloud network slicing can be defined as the process that enables isolated end-to-end and on-demand networking abstractions, which: (a) contain both cloud and network resources, and (b) are independently controlled, managed and orchestrated. This paper contributes to the vision of the NECOS project and relevant platform, that aim to address the limitations of current cloud computing infrastructures to accomplish the challenging requirements of the slicing approach. The NECOS platform implements the Slice-as-a-Service model, enabling the dynamic creation of end-to-end (E2E) slices from a set of constituent slice parts contributed from multiple domains. A challenging issue is to define the facility that implements dynamic slice resource discovery, aligned to the requirements of the slice owner or tenant, over different infrastructure providers. Here, we propose a Marketplace-based approach implementing relevant federated interactions for the resource discovery and we detail its architecture, workflows, and information model. We also present its initial implementation details and provide both quantitative and qualitative experimental results validating its main operation.

With the evolution of the processing capacity of the network devices and the appearing of novel programmable network hardware, new methods and techniques for traffic monitoring have been proposed. Among these new techniques, the use of sketches has been increased. Sketches are probabilistic data structures capable of summarizing information about the network with two main advantages over flow-based counters: low memory usage and adjustable accuracy. Considering this scenario, this paper proposes the use of probabilistic data structures implemented in P4 (Programming Protocol-Independent Packet Processors) to monitor multi-tenant networks. While it is common to see similar solutions for monitoring non-shared networks, as far as we know this is the first work that deals multitenant scenarios. We implemented sketches for each tenant so that an isolated network monitoring can be done. The solution, named BitMatrix, was created taking into account two probabilistic structures: bitmaps and counter-arrays.

One of the major issues in Data Center Networks (DCNs) is related to elephant flows. These flows typically are primarily responsible for network congestion, causing impacts on applications that are intolerant to delays. Although there are several works in the literature that analyze this type of traffic, none of them consider the impact of elephant flow size on the throughput and on the Flow Completion Time (FCT). This article presents the EFM (Elephant Flow Manager) as a complete solution for monitoring and re-routing elephant flows in DCNs, by observing the size of these flows in the network. The tests performed show that the size of the elephant flows influences the throughput and the FCT of the applications and, therefore, deserves to be observed. The evaluation of the solution was performed comparing the joint use of EFM with ECMP (EqualCost Multi-path). In all tests, our re-routing solution proved to be better when compared to pure routing solely with ECMP. Then, this paper presents three main contributions: (1) it compares our solution with the ECMP, (2) highlights the importance of the size of the elephant flows in the re-routing of these in DCNs and (3) preliminary tests indicate that due to the decrease in FCT, it is possible to reduce the energy consumption in the data center.

Improving the networking data traffic in modern data centers has attracted significant importance in recent years. In contrast to existing approaches that require changes in the network architecture, this paper proposes a traffic-aware virtual machine (VM) placement for Data Center Networks (DCN). The proposed optimization strategy is able to allocate VMs according to traffic patterns. The strategy follows a Mixed Integer Linear Programming (MILP) in conjunction with networking simulation. Our approach known as MILP Flow is used to simulate the data center traffic networking. We emphasize that our approach provides the maximum throughput without losses, and optimizes better network-aware allocations than other policies in cloud networks.

This paper focuses on evaluating the use of MPTCP to forward sub flows in Open Flow networks. MPTCP is a network protocol designed to forward sub flows through disjointed paths. Modern networks commonly use Equal-Cost Multipath Protocol (ECMP) to split flows through distinct paths. However, even with ECMP enabled, sub flows may be forwarded through the same path. MPTCP improves the multipath routing by setting sub flows to be forwarded through distinct paths. As a consequence, the amount of sub flows must be considered to evaluate the network throughput. In this paper, we design Multiflow to use MPTCP in Open Flow networks. Our proposal is to improve the throughput in shared bottlenecks by forwarding sub flows from a same MPTCP connection through multiple paths. We validate our approach in a test bed where shared bottlenecks occur in the link at the endpoints. The Multiflow improvement of the network performance is evaluated in experiments about resilience and end-to-end throughput.

Software Defined Networking is one of the most promising approaches to the deployment of future network infrastructures. The most of the Internet service providers have to deal with a number of configurations to a crescent amount of network devices. SDN is a paradigm that proposes the separation of data forwarding plane from the data control plane. OpenFlow is an standard protocol used in SDN for establishing communication among switches and controllers. However, computational modelling for SDN is still few researched in the literature. Computational modelling is the key to describe, evaluate and analyse the most diverse computational problems before its prototyping. In this paper, we propose the integration between computational modelling and the deployment of data paths for SDN. We develop the toolset called MILPFlow to generate computational models of data centers to solve routing problems and to establish data paths between servers according to the solutions of these models. Additionally, data paths are set before data flows are sent through the network. As a consequence, MILPFlow contributes to reduce the overhead to discover network routes among hosts of data centers. We evaluate the effectiveness of our methodology by using Mininet through a set of experiments.

The growing demand for ubiquitous computing lead manufactures to develop multi-band devices supporting different network access technologies, such as GSM, Wi-fi, and UMTS. Although these devices support different access technologies, they lack of native vertical handover support, breaking any ongoing connections whenever users switch between access technologies. In this paper we present a next-generation Internet architecture with native vertical handover support, providing mechanisms to support mobility between different access technologies without disruptions. The architecture introduces an identification layer that decouples the host identification from its location, enabling native mobility support for legacy applications. The identification layer employs cryptographic identifiers to identify end-hosts over the Internet and to provide security mechanisms for identity management during vertical handovers. As a proof of concept, a prototype was implemented and evaluated in different vertical handover scenarios in GSM, Wi-fi and wired domains.

Current security protocols such as IPSec and TLS/SSL provide security mechanisms to authenticate and protect the communication between end-hosts over the Internet. Nonetheless, new communication scenarios like mobility and heterogeneous networks have exposed some technical limitations of these protocols. Both protocols employ the IP address as end-host identifier to establish security associations between the parties, entangling the end-host identification with its topological location. In order to overcome these limitations, we propose the Identification Layer Security (IDSec), a security model to provide authentication based on public key cryptography with denial of service resistance capabilities and data integrity and confidentiality. IDSec introduces a new communication substrate based on cryptographic identifiers which natively support new services such as mobility and heterogeneous networks with security embedded in the identification layer. In order to validate the proposal, a prototype was implemented and evaluated, analyzing the performance of legacy applications in static and mobility scenarios.

A frequent subject in forums, academia and industry is the evolution of the Internet in terms of routing. Basically, the scalability in the Default Free Zone related to (1) the growing rate of the routing tables and (2) the convergence of the routing system, are pointed by routing experts as the main concerns of the current mechanism. Several approaches have emerged, but they normally require a mapping system to translate from identifiers to locators (IP). We contribute with this discussion by introducing our XOR-based Flat Routing mechanism for Internet-like topologies. Essentially, the proposed mechanism routes directly on top of flat ASes identifiers, eliminating the need for mapping systems. In this work we propose a mechanism for building the routing tables over the XOR-based scenario in conjunction with a reachability service developed using the concepts of Landmark and Bloom filters. The proposal is evaluated using our developed emulation tool under five different Internet-like topologies ranging from 512 to 8192 nodes.

Flat routing is subject of many proposals found in the literature. One challenging task in this scenario is the flat identity space management since aggregation is not possible. The Landmark-based Flat Routing (LFR) proposal is an alternative to address the routing tables' growth in a scenario which (1) organizes the network in regions represented by landmarks and (2) adopts an XOR-based routing mechanism inside regions. In this work, Bloom filters are introduced to LFR as an effective mechanism to deal with two main aspects of flat routing. The first one is to advertise aggregated reachability information between landmark regions and the second combines the intra-region XOR-based mechanism with Bloom filters in order to reduce unnecessary walk at the flat binary space, opening up many possibilities for the Flat Routing scenario, especially for stretch reduction and scalability for disseminating ``reachability'' information. The evaluations were done in a regular mesh network topology with 256 nodes and the results show the advantages of having Bloom filters in both scenarios, highlighting the effectiveness of such mechanism for flat identifiers aggregation.

In this paper we make the domain entity a first class citizen. The concept of Domain Identifiers (DIDs) is introduced to effectively bring the domains to the next generation Internet scenario. The paper presents an architecture to address challenging next generation Internet requirements such as node and domain mobility, multi-homing, security, network composition and inter-domain routing. Although our architecture supports all the mentioned requirements, the focus of this paper is specifically on node and domain mobility in order to evaluate and compare the advantages of having DIDs for facilitating the mobility. First, we present the generic Next Generation Internet architecture proposal and its envisioned scenario. Then, we show how to instantiate it to work together with the current Internet. We have developed a prototype to evaluate our proposal and we depict results related to node and domain mobility under this gradual deployment scenario using the Internet as the core.

The current internetworking architecture presents some limitations to naturally support mobility, security and multi- homing. Among the limitations, the IP semantic overload seems to be a primary issue to be considered. In this paper we present a next generation internetworking architecture to overcome the IP semantic overload by introducing an identity layer located between the network and transport layers. This new layer provides a stable cryptographic identifier for end-hosts and seamlessly allows the deployment of new services, such as mobility, multi-homing and security. A prototype was implemented and evaluated considering some mobility scenarios, including intra-domain, inter-domain and simultaneous node mobility.

In response to the limitations of the Internet architecture when used for applications for which it was not originally designed, a series of clean slate efforts have emerged to shape the so-called future Internet. Recently, visionary voices have advised a shift in the networking problem under research, moving from seamless host-reachability to internetworking of information. We contribute to the healthy debate on future Internet design and discuss ongoing information oriented efforts. Inspired by recent works in Bloom-filterlike data structures, we propose the SPSwitch as a novel switching engine to make wire speed forwarding decisions on flat information labels. We address part of the scalability issues in a data-oriented forwarding layer by trading overdeliveries for state reduction and line speed operations.

The publish/subscribe communication paradigm is an appealing mechanism for efficient content retrieval due to the decoupling of data sources and consumers. However, the location decoupling nature of the paradigm opens security issues related to the content authentication and integrity since there is no binding between published content and its providers. In this paper we propose a new control plane called security plane, which is responsible for providing all essential security functionalities, such as efficient content authentication, data integrity and publication control. The security plane creates a binding between information providers and their contents, allowing secure content authentication by subscribers, mitigating security flaws such as fake content publication and data corruption.

The current internetworking architecture presents some limitations to naturally support mobility and multi-homing. Among the limitations, the IP semantic overload seems to be a primary issue to be considered. In this paper we present a next generation internetworking architecture to overcome the IP semantic overload by introducing an identification layer located between the network and transport layers. This new layer provides a stable identifier for end-hosts, enabling the natural deployment of new services, such as mobility, multi-homing and security embedded. We implemented a prototype and evaluated it considering the legacy application support in mobility scenarios.

A distributed control plane architecture enhances transport networks with dynamic and flexible connection control. As a result, it allows the provisioning of advanced connectivity services, like Virtual Private Networks (VPNs), on layer 1 switching networks. Such Layer 1 VPN (LI VPN) services enable multiple customer networks to share a single transport network. In this work, we propose an architecture for LI VPN management. Our approach has been to use Policy-Based Management (PBM) to provide customers with some level of control and management over their LI VPNs. We also present a prototype implemented to validate the proposed architecture and discuss implications of policies for LI VPN configuration management.

In this paper we present a Web Services-based approach to provide connections between optical domains. The architecture we describe here consists of a virtual topology service, an end-to-end connection service and an end-to-end interdomain negotiation service. All of them were implemented using the Web services technology. In this work we shortly introduce the architecture and focus mainly on the performance analysis. In order to validate our approach, a prototype that implements the services mentioned above has been developed and evaluated in terms of time, scalability and bandwidth consumption to establish end-to-end interdomain optical connections.

Provisioning of interdomain connections has been a constant challenge for providers. Clients (e.g. access networks) are asking for services that span across multiple different domains and each domain has its local constraints (policies) that must be analyzed before accepting an end-to-end (e2e) connection. In this paper we present a service oriented architecture-based approach by which basic services are modeled and composed to create more complex services. Such approach considers the establishment of e2e interdomain connections in optical networks. The architecture we present here consists of a virtual topology service, an end-to-end connection service and an end-to-end negotiation service. All of them were implemented using the Web services technology

In this paper we present a policy-based admission control architecture responsible for managing the installation and aggregation of packet-based LSPs within lightpaths (optical LSPs). We have defined some policies in order to reduce the optical network overhead to remove and reroute lower priority LSPs. The architecture is composed of an admission control and a policy manager and the performed simulations indicated that the number of LSP removals is smaller when policies are applied.