Software-Defined Networking (SDN) offers a flexible, programmable approach to network management by decoupling the control and data planes. While SDN technical advantages, such as improved network performance and security, are well-documented, its economic implications remain underexplored, particularly in prioritizing services based on business value. This paper introduces Dhana, a novel SDNbased traffic management approach that integrates economic considerations. Dhana dynamically prioritizes high-value services by analyzing network components using metrics like downtime costs and service-level agreement (SLA) compliance. The goal is to minimize economic loss during network congestion or failures, even if technical global optimization is partially sacrificed. Tests show that Dhana can take many paths according to its needs and has not significant overhead.

The extensive adoption of containerized applications significantly raises the criticality of managing potential vulnerabilities, including privilege escalation within these environments. While the Bag of System Calls (BoSC) is a common technique to detect such attacks, tracing system calls in containerized applications is often inefficient for real-world scenarios. This paper proposes an eBPF-based solution to trace system calls in containerized applications and apply the BoSC technique to identify privilege escalation attempts within containers. We analyzed the cost of different system call hooking methods and found that raw tracepoint programs have the least overhead. Furthermore, we observed a slight increase in overhead when tracing all executed operations within a containerized application. Finally, we confirmed that our solution successfully identifies user efforts to escape containers, concluding that eBPF can be a powerful tool for containerized system security.

The Internet of Things (IoT) paradigm can improve a broad range of applications, such as medical sensors, smart cities, industrial monitoring, and so on. In healthcare specifically, these devices can aid in management tasks and improve the quality of life of patients in intensive care. However, securing medical IoT devices and its data is a crucial task. Not only do they handle Electronic Medical Records (EMR) and physiological information, but in some cases, disruption can affect a patient's treatment. Blockchain technologies applied in the healthcare context can provide privacy, immutability, decentralization, and easier access and sharing of medical data. Despite the emergence of applications aiming to solve security issues in the Healthcare IoT (HIoT) scenario using blockchain, there is still much to be addressed, mainly regarding throughput, storage of data, and efficient use of resources. This work proposes a blockchain-based storage architecture for HIoT, using a private network and distributed data storage to achieve integrity, accountability, and availability of medical data.

Network traffic engineering aims at the network quality, optimizing routes and detecting network attacks. In this context, traffic prediction is an essential tool to capture the underlying behavior of a network. Therefore, this work proposes a modularization architecture for volumetric prediction models, allowing switching between models and setups at runtime in controllers of Software Defined Networks (SDN), dealing with short time series and delivering the data already processed for the prediction. The proposed architecture compares the results from four traditional predictors based on short-range time dependency.

Depression is the most incapacitating disease worldwide, and it has an alarming comorbidity rate with anxiety. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. In spite of many solutions proposed for the automatic recognition of depression, fewer exist for anxiety and its comorbidity with depression. In this paper, we propose DAC Stacking, a solution that leverages stacking ensembles and Deep Learning (DL) to automatically identify depression, anxiety, and their comorbidity, using data extracted from Reddit. The stacking is composed of single-label binary classifiers, that either distinguish between specific disorders and control users (experts), or between pairs of target conditions (differentiating). A meta-learner explores these base classifiers as a context for reaching a multi-label decision. We assessed alternative ensemble topologies, exploring roles for base models, DL architectures, and word embeddings. All base classifiers and ensembles outperformed the baselines for depression and anxiety (f-measures near 0.79). The ensemble topology with the best performance (Hamming Loss of 0.29 and Exact Match Ratio of 0.46) combines base classifiers of three DL architectures, and includes expert and differentiating base models. The analysis of the influential classification features according to SHAP revealed the strengths of our solution and provided insights on the challenges for the automatic classification of the addressed mental conditions.

Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.

The proper execution and management of heterogeneous Virtualized Network Functions (VNFs) relies on the employment of efficient and comprehensive VNF platforms. However, current systems are developed without following any standardized reference architecture, thus leading to proprietary and monolithic solutions. Furthermore, those platforms lack support for recent NFV developements, such as VNF Components (VNFC) and the Network Service Header (NSH). In this work, we present an architecture for VNF platforms that is fully compliant with the European Telecommunications Standards Institute (ETSI) NFV architecture, while also enabling the execution of both VNFC and NSH. Through the development of a system prototype called COmprehensive VirtualizEd NF (COVEN) platform, we were able to evaluate the effectiveness of our proposed architecture and to demonstrate the benefits of supporting VNFC and NSH, such as flexibility and efficiency.

The diversity of services that operate in the Internet has increased significantly in the last years. Performance problems in these services cause important financial losses. To ensure that these problems do not occur, service levels need to be monitored. One of the main techniques for such monitoring involves the use of active measurement mechanisms. However, these mechanisms are expensive in terms of resources consumption due to the activation of measurement sessions. Measurement sessions usually cover only a fraction of what could be measured, which leads to service level problems being missed. Measurement federations can help network administrators in different tasks, such as controlling the activation of active measurement sessions. In this context, measurement correlation can be deployed in order to improve this control in such federations. The main contribution of the present work is the proposal of a data transformation service that provides measurement correlation. This service is used to enable cooperation features in measurement federations, while decreasing resource consumption. Besides that, statistical tests that can be used to compose such correlation are presented. The proposed solution is evaluated using an active measurement dataset from the Brazilian National Research and Education Network (Rede Nacional de Ensino e Pesquisa - RNP). Our results provide insights regarding measurement correlation from federated measurement points and can be used for the design of better application to control active measurement sessions.

Critical networked services established between service providers and customers are expected to operate respecting Service Level Agreements (SLAs). An interesting possibility to monitor such SLAs is using active measurement mechanisms. However, these mechanisms are expensive in terms of network devices resource consumption and also increase the network load because of the injected traffic. In addition, if the number of SLA violations in a given time is higher than the number of available measurement sessions (common place in large and complex network infrastructures), certainly some violations will be missed. The current best practice, the observation of just a subset of network destinations driven by human administrators expertise, is error prone, does not scale well, and is ineffective on dynamic network conditions. This practice can lead to SLA violations being missed, which invariably affects the performance of several applications. In the present thesis, we advocate the use of Peer-to-Peer (P2P) technology to improve the detection of SLA violations. Such use is described using principles to control active measurement mechanisms. These principles are accomplished through strategies to activate measurement sessions. In this context, the thesis contains several contributions towards SLA monitoring, conceptually as well pragmatically. The findings show properties which improve the detection of SLA violations in terms of the number of detected violations and the adaptivity to network dynamics. We expect that such findings can lead to better SLA monitoring tools and methods.

The diversity of services that operate in the Internet has increased significantly in the last years. Performance problems in these services cause important financial losses. In order to ensure that these problems do not occur, service levels need to be monitored. One of the main techniques for this monitoring involves the utilization of active measurement mechanisms. However, such mechanisms are expensive in terms of resources consumption due to the activation of measurement sessions. Thus, such sessions usually can cover only a fraction of what could be measured, which can lead to service level problems being missed. Measurement federations can help network administrators in different tasks, such as the control of the activation of active measurement sessions. In this context, measurement correlation can be deployed in order to improve this control in such federations. The main contribution of the present work is the proposal of a data transformation service which provides measurement correlation. This service can be used to enable cooperation features in measurement federations, while decreasing resource consumption. Besides that, statistical tests that can be used to compose such correlation are presented. The proposed solution provides valuable insights regarding measurement correlation from federated measurement points and can be used for the design of better application for the control of active measurement sessions.

Critical networked services are usually regulated by Service Level Agreements (SLAs). In order to ensure SLAs are being met, it is necessary to monitor Service Level Objectives (SLOs). Active measurement mechanisms are usually chosen to perform this monitoring task, which requires measurement probes to be activated in network devices. However, these probes are expensive in terms of computational resources consumption, thus, active measurement mechanisms usually can cover only a fraction of what could be measured, which can lead to SLA violations being missed. Besides that, highly dynamic networking patterns require the ongoing selection of the candidate network destinations for probing and their respective prioritization, a practice that is not well suited for human administrators because configuring the probes is labor-intensive and error-prone. A possibility to improve the detection of SLA violations is the employment of Peer-to-Peer (P2P) technology in order to steer tasks related to a distributed decision making process for probe activation. In this context, a P2P management overlay can be used to coordinate the probe activation and to share measurement results among the network devices. For a node to rely on measurement data from a peer to determine which probes to configure, it needs to know which of the peers are best correlated with itself, i.e., which nodes have the most significance in terms of being indicative of service level violations that might be observed by the node itself. We propose an autonomic P2P solution to coordinate the placement of active measurement probes in large-scale networks. The edge nodes of the network cooperate via a P2P management overlay to determine what destinations should be monitored. Each edge node determines autonomously what destinations to probe considering local measurements and measurement data from other edge nodes. The measurements considered are traffic information from passive measurement results and past service level measurement results from active measurement results. The proposed solution is evaluated using simulation and the results show its feasibility and interesting features.

Disruption-Tolerant Networks (DTNs) are characterized by long delays and constant disconnections among nodes. These networks have management needs analogous to those of traditional computer networks, but, their intrinsic characteristics (e.g., intermittent connections) hinder the execution of network management tasks. The employment of P2P technology is an alternative for managing DTNs since this technology promotes the autonomy of management entities (i.e., management peers). This autonomy enables the use of local logic and data in order to perform opportunistic management tasks. We propose a solution to estimate the future contacts of nodes through distributed statistical analysis of past contacts. Besides that, nodes can share information about their contacts to improve contact estimation using a P2P management overlay. The proposed solution was implemented using an open source P2P-Based Network Management (P2PBNM) system, ManP2P-ng. Furthermore, the solution was evaluated through experiments performed using publicly available DTN traces. The results show that the proposed solution can improve the execution of management tasks in DTNs.

Critical networked services enable significant revenue for network operators and, in turn, are regulated by Service Level Agreements (SLAs). In order to ensure SLAs are being met, service levels need to be monitored. One technique for this involves active measurement mechanisms which employ measurement probes along the network to inject synthetic traffic and compute the network performance. However, these mechanisms are expensive in terms of resources consumption. Thus, active measurement mechanisms usually can cover only a fraction of what could be measured, which can lead to SLA violations being missed. Besides that, the definition of this fraction is a practice done by human administrators, which does not scale well and does not adapt to highly dynamic networking patterns. The contribution of the present work is the proposal of a solution to increase the potential number of detected SLA violations in which network devices autonomously and dynamically share service level measurement results. The sharing of these measurement results is based on the utilization of a Peer-to-Peer (P2P) management overlay built using past service level measurement results and a coordination strategy characterized by a high degree of decentralized decision making. The solution is evaluated using simulation and the results show its feasibility and interesting features.

The introduction of self-* properties has been proven to be a feasible approach for the management demands of Disruption-Tolerant Networks (DTNs). Among the properties of the self-* management vision, self-healing figures as a key property to improve the dependability of the managed infrastructures. An interesting possibility to materialize self-* support in disruption-tolerant management is through the employment of Peer-to-Peer (P2P) technology. In this paper, we introduce a P2P self-healing service tailored for disruption-tolerant management. We implemented the proposed service using ManP2P-ng, an open source P2P-based network management system. In addition, an experimental evaluation is performed considering as case study the disruption-tolerant management of a Host-based Intrusion Detection System (HIDS) deployed on a specific kind of DTN: Internet access networks for remote villages. The results show the feasibility of our proposal and its interesting features.

The introduction of self-* properties over distributed network management infrastructures has been proving to be a feasible approach for the new demands of modern network management. Among the properties of the self-* management vision, self-healing figures as key property in improving the dependability of the managed infrastructures. An interesting possibility to materialize self-* support - and self-healing support as well - in network management is through the employment of peer-to-peer (P2P) management overlays. Considering this scenario, we introduce in this paper a self-healing service provided by a prototype P2P-Based Network Management (P2PBNM) system. Such a service is expected to be contracted by human administrators interested in monitoring and recovering their IT infrastructures. In addition, an experimental evaluation of the self-healing service is performed considering a case study where a Host-based Intrusion Detection System (HIDS) needs to be constantly observed and eventually healed to keep the underlying communication network protected.

Autonomic network management is a vision that brings autonomic computing principles to network management. In this vision, the use of policies is a key aspect. Besides, it is necessary to add some level of decentralization to enable broad autonomic capabilities. Thus, the elements that build decentralized autonomic network management systems (known as autonomic management elements) must be policy-enabled. However, the consistency of policy states among autonomic management elements is an important challenge. Traditional mechanisms to maintain consistency of these states are supported by some centralization which wastes some desirable properties of decentralization. In contrast to these mechanisms, we propose a distributed, scalable and robust mechanism to maintain the consistency of policy states. In this paper we introduce multi-agent truth maintenance features in decentralized autonomic network management as a mechanism to bring consistency maintenance of policy states. We developed a model of a decentralized autonomic network management system on Peersim to perform simulation experiments. Besides, the utilization of policies in P2P-based autonomic network management systems is presented as case study.

Network visualization is an emerging technology for cost-effective sharing of network resources. The key strategy in network virtualization is of slicing physical resources (links, CPU, memory, and storage) to create virtual networks that are assigned to different operators. One important challenge on network virtualization is the efficient use of the physical resources. To accomplish such efficient use the management of the physical resources should be transparent to the applications running within the virtual networks, and should be executed at runtime in order to deal with the variation on the load requests of different virtual networks. Traditional resource allocation schemes use offline, centralized, and global view strategies to manage the use of physical resources. In contrast to these strategies, we propose a runtime, distributed, local view approach to manage physical resources. In this paper we introduce a virtual network architecture and an associated self-organizing algorithm to reallocate virtual network resources along different physical nodes in order to equalize the bandwidth, and storage consumption on the physical nodes. We developed a virtual network model based on Omnet++ to simulate the designed self- organizing algorithm. An IPTV testbed scenario is presented and initial experiments, about the interruption time of the application inside the IPTV virtual network, are described.