Todays enterprise networks wrestle with accommodating an ever-growing number of devices of different types, supporting increasingly demanding applications and ever more complex services, and protecting their users from sophisticated and disrupting cyber threats. In response, a proposed architectural approach for improving network management, referred to as Intent-Based Networking (IBN), has attracted significant attention. It is built on the premise that network operators specify network policies in natural language and the network correctly translates these spoken intents (e.g., policies) into proper device-specific configurations that are then deployed across the network to reliably act on the operators expressed intents. Unfortunately, IBN has not yet fully delivered on its promise of automated, fast, and reliable policy deployment, mainly due to the significant challenges that the reliance on methods from Natural Language Processing (NLP) or more recent techniques from Machine Learning (ML) and Artificial Intelligence (AI) poses for unambiguously and accurately translating the myriad of intents that operators can express in natural language into “trustworthy” device configurations. This paper uses LUMI, a recently designed end-to-end prototype of a system that allows operators “to manage their network by talking to the network”, as an illustrative case study. In particular, we use it to elaborate on the different functionalities such systems should have to realize IBNs vision of automating the fast deployment of policies. At the same time, we leverage LUMI to highlight the extra efforts that are required to ensure that the deployed policies can be entrusted to accurately express and execute the operators original intents.

Centralization of key Internet services, including email, can result in privacy and security concerns and increase the number of single points of failure. This paper measures and analyzes a large-scale dataset of email providers gathered from MX records of top-level domains. The findings reveal the concentration of email infrastructure providers for each TLD and identify the most significant providers in the market. The paper also demonstrates that the IPv6 adoption increased the centralization of email servers. The research contributes to the state-of-the-art by thoroughly examining email infrastructure centralization and identifying potential areas for future research.

Internet Exchange Points (IXPs) are high-performance networks that allow multiple autonomous systems to exchange traffic, with benefits ranging from cost reductions to performance improvements. In addition, performance requirements and a number of players involved in such networks bring out several issues to management tasks, such as elephant flows identification. This kind of flows, with high size and substantial duration, can severely impact the performance of smaller flows. In this paper, we present IDEAFIX, a mechanism to identify elephant flows in P4-based IXP networks. Our approach consists in analyzing flows features for each ingress packet immediately in the edge switch. These features are then stored in P4 registers, indexed by hash keys, and compared to predefined thresholds for flow classification. Experimental evaluations show that IDEAFIX is significantly more efficient than the state-of-the-art approaches implemented with sFlow and traditional Software-Defined Networking (SDN) tools (e.g., OpenFlow). While state-of-the-art mechanisms add up to 17MB of monitoring data, our solution causes an overhead of only 25KB. Also, the implemented prototype takes less than 0.40ms to identify elephant flows with a 95% accuracy in scenarios with scarce memory resources.

Network Functions Virtualization (NFV) was proposed to migrate middleboxes that compose network services, such as firewalls and Network Address Translation (NAT) servers, from hardware to software running on Virtual Machines (VMs), commonly known as Virtualized Network Functions (VNFs). In NFV-enabled networks, VNFs can be chained in Forwarding Graphs (FGs) to provide services. These FGs establish the logical order in which network packets must traverse until reaching the end-service. In this scenario, network operators establish affinity and anti-affinity rules, which determine restrictions on the placement and chaining of VNFs according to how well or poorly VNFs operate together. To address the subject of identifying affinity relations in NFV-enabled networks, we previously proposed a mathematical model to measure the affinity between pairs of VNFs. However, that affinity model falls short for identifying affinity of VNFs not yet deployed, as they have no resource usage data to take into account. In this paper, we use artificial neural networks to predict affinity estimation for newly introduced VNFs, which still do not have usage data to be analyzed. This affinity neural network is trained using past affinity measurements, containing the data from VNFs, Physical Machines (PMs), and FGs of each measurement as features. We evaluate our solution by analyzing it over real usage data from a Cloud dataset, and conclude that neural networks can be used to provide affinity values for network operators, or NFV orchestrators, to plan the deployment of new VNFs.

Virtual Network Functions (VNFs) can be chained and provisioned on demand, providing elasticity and dynamicity to the network. Due to the interdependencies between VNFs, resulting service chains may not work as expected, and because of that, it is crucial to determine which VNFs are having a negative impact on the service quality. In this paper, we introduce a model to quantify the guiltiness of a VNF on being a bottleneck in a service chain, which provides a metric that estimates the impact on processing delay. In addition, we propose an adaptive algorithm, based on linear regression and neural networks, to adjust the model parameters according to the environment particularities, such as the type and number of VNFs. We show through an experimental evaluation that the guiltiness metric faithfully characterizes end-service performance, by identifying up to 94% of the bottleneck VNFs in the analyzed scenarios. Also, we provide artifacts for researchers to reproduce our results in other scenarios.

Network Functions Virtualization (NFV) is driving a paradigm shift in telecommunications networks, fostering new business models and creating innovation opportunities. In NFV-enabled networks, Service Providers (SPs) have the opportunity to build a business model where tenants can purchase Virtual Network Functions (VNFs) that provide distinct network services and functions. However, the chance to negotiate VNFs requires a change in traditional network planning strategies to accommodate tenants demands. In this context, the planning tasks perform a critical role in the introducing of business strategies that encompass both profit and health of services, which requires operators to have a broad understanding of the environment. In this paper, we propose the usage of two interactive visualization techniques to help NFV network operators in planning and strategic decisions. We advocate that our visualizations can aid in NFV planning tasks, such as infrastructure investment, resources allocation, and service pricing. We present three case studies to provide evidence of the feasibility and effectiveness of our visualizations.

Programmable Virtual Networks (PVNs) make the network more flexible and allow the fast introduction of new services. However, several shortcomings hamper their wider adoption, including: (i) the extensive knowledge required to configure and manage the NetApps; (ii) the lack of descriptors to detail all nuances of the NetApps; and (iii) there is no solution that enables to distribute and configure NetApps over distinct technologies. Therefore, we propose iMPROVE to simplify the introduction of services in PVNs, enhancing the distribution of NetApps. We also extend the ETSI network service descriptor to support distinct technologies as well as to represent conflict issues. We demonstrate evidence of iMPROVE's feasibility in a case study and compare it with the main solutions for distributing and deploying applications over multiple platforms.

Network Functions Virtualization (NFV) offers several benefits for Service Providers (SPs), such as mitigating equipment cost and increasing business agility. In NFV-enabled networks, inadequate placement of Virtualized Network Functions (VNFs) creates bottlenecks, impacting negatively on performance. Therefore, network operators must establish affinity and anti-affinity rules to avoid network and processing bottlenecks, and thus comply with Service Level Agreement (SLA) requirements of tenants. Affinity and anti-affinity rules in NFV must be broad and carefully elaborated to maintain service performance. Network operators must consider further than simply resource allocation when identifying affinity among VNFs. The criteria for VNFs affinity varies for different forwarding graphs. Geolocation, latency, packet loss, and bandwidth usage are some examples of criteria that can be considered as indicators of bottlenecks in high traffic networks. In this paper, we propose a solution to measure affinity between pairs of VNFs, based on a weighted set of affinity criteria considered relevant by a network operator. To evaluate the feasibility of our affinity model, we analyze three case studies over an experimental NFV scenario. We conclude that our affinity model can help network operators identify the cause of issues in NFV-enabled networks, as well as it may be used by NFV orchestrators to aid on VNFs migration and embedding.

AMNESiA is an affinity measurement platform for NFV-enabled networks, designed to consolidate and interpret existing monitoring data into an affinity metric, aiding operators to identify affinity and anti-affinity relations in the network. AMNESiA uses the latest snapshot of usage data, collected through a generic monitoring solution, from the database to measure affinity between VNFs.

Many aspects of the management of computer networks, such as quality of service and security, must be taken into consideration to ensure that the network meets the users and clients demands. Fortunately, management solutions were developed to address these aspects, such as Intent-Based Networking (IBN). IBN is a novel networking paradigm that abstracts network configurations by allowing administrators to specify how the network should behave and not what it should do. In this paper, we introduce an IBN solution called INSpIRE (Integrated NFV-based Intent Refinement Environment). INSpIRE implements a refinement technique to translate intents into a set of configurations to perform a desired service chain in both homogeneous environments (VNFs only) and heterogeneous environments (VNFs and physical middleboxes). Our solution is capable of (i) determining the specific VNFs required to fulfill an intent, (ii) chaining these VNFs according to their dependencies, and (iii) presenting enough low-level information to network devices for posterior traffic steering. Finally, to assess the feasibility of our solution we detail a case study that reflects real-world management situations and evaluate the scalability of the refinement process.

Network Functions Virtualization (NFV) is a key technology to reduce management costs as well as to improve scalability and elasticity of computer networks. Still, recent research efforts have been exposing additional management challenges. Concerning monitoring in particular, new types of entities and requirements are underexploited. To address these issues, we propose DReAM, a resource management architecture based on management by delegation and distributed monitoring, where each agent runs a diagnostic model to compute the network service state. In this paper, we describe DReAM's proposed architecture and its major components. We also discuss the feasibility of DReAM through experimental and analytical evaluations, where we observed application throughput, CPU utilization, communication overhead, scalability, and diagnosis complexity. We provide a trade-off analysis on the monitoring strategies in NFV scenarios. Our results indicate that a result-aware strategy is a better option when the monitored environment has more than 256 agents or when the diagnosis module induces at least 10% of CPU utilization.