Smart cities aim to address urban challenges such as traffic congestion, pollution, and limited resources. Software platforms play a key role in integrating diverse technologies and services, enabling real-time data processing to support efficient public service delivery. Cloud computing meets the demands of critical applications—like autonomous vehicles and high-load systems—by offering scalable infrastructure with dynamic resource provisioning. However, the lack of structured guidelines for migrating Web applications to the cloud, especially in smart city environments, remains a significant challenge. Migration involves technical adaptation and strategic decisions regarding resource allocation and service configuration. This paper presents a goal-oriented catalogue based on the KAOS (Keep All Objectives Satisfied) model to support web application migration to the cloud in smart city contexts. The catalogue defines goals related to five essential cloud services: relational databases, virtual machines, blob storage, application deployment, and queuing services. It serves as a decision-support tool for developers and urban planners. The proposed approach will be integrated into a smart city platform and validated through case studies and simulations.

Finding information may be a complex task for end users, either due to the format in which data is stored, or difficulty in formulating a query that fits the database structure. Conversational interfaces, such as chatbots, can minimize this issue, by facilitating query formulation through natural language. Despite the several applications of chatbots for data querying, the multidimensional aspect of data is rarely addressed in the literature, making the search for information even more challenging. Chatbots can be used for allowing the user to “talk to the data” by adding metrics and dimensions to a query, without relying on technical expertise. Thus, this paper presents a chatbot approach for querying multidimensional data, which captures users intentions and links them to the multidimensional metadata. This linking process allows the bot to set query parameters, using them for accessing the data. The chatbot was implemented for querying an open database containing about 2.5 billions records and over 1700 attributes (including dimensions and metrics), and it was evaluated through an empirical user study involving a group of participants performing a set of search tasks. The evaluation results supported the usefulness of the proposed approach in querying multidimensional data and retrieving information.

Users metadata collection is a concerning issue related to Instant Messaging (IM) due to the potential of privacy violation. Even with messages' content encryption, metadata such as relationships and other communication patterns are exchanged in clear text, incurring in information leakage. In this paper, we investigate popular IM solutions to identify existing metadata, propose a broader nomenclature encompassing similar information, and assess the impact of metadata leakage on users' privacy. We also present a hierarchy of metadata based on our proposed nomenclature to allow for fair comparison among related work and easy gathering of privacy needs of users.

The principle of Network Neutrality (NN) has been debated around the world for nearly two decades. NN states that all traffic in the Internet must be treated equally, regardless of content, origin and/or destination. The main motivation for this principle is to protect fair competition, innovation, and ensure freedom of choice for consumers. The global debate revolves around whether NN should be enforced through regulations or not, as well as the potential impact of such regulations – or lack thereof – on the telecommunications market. In this context, multiple governments worldwide have already implemented NN regulations. In this work, we give an overview of NN regulations in 50 countries across five continents. We first give a brief introduction to the NN global debate. Then, we describe some of the main aspects related to the regulatory process of each country/region. Finally, we compare the different regulations according to common and divergent features identified.

The worldwide debate over Network Neutrality (NN) has been raging on for nearly two decades. According to NN principles, all traffic in the Internet must be treated with impartiality. In particular, unfair Traffic Differentiation (TD) is not allowed. Several strategies have been proposed for detecting TD, but locating the source of TD is still an under-explored topic. In this work, we present a holistic approach for unifying TD detection solutions into a single framework with the purpose of locating the source of TD. We propose an algorithm for combining measurements from multiple vantage points, and a strategy for selecting good vantage points. Our proposals leverage Internet peering properties to infer the behavior of individual Autonomous Systems (ASes), without requiring knowledge of the exact routes traversed by measurement probes. To evaluate our proposals, we first ran several experiments to confirm that indeed Internet routes do present the required properties. Then, several simulations were performed to assess the efficiency of our proposals. Results show that our approach is capable of locating TD under several different conditions. Another finding is that issuing measurements from a few end-hosts of core Internet ASes achieves similar results than from a much larger number of end-hosts at the edge.

The purpose of this demo is to visually show a testbed monitoring strategy used to select "stable" sets of nodes to run new protocols. The stability of a set of nodes is defined in terms of the ability of the nodes to communicate among themselves within given time bounds during reasonable intervals of time. We assume an unstable network, in which some nodes may not be able to communicate with some others, and this condition varies with time. In order to measure stability, the communication between pairs of nodes is continuously monitored by measuring the corresponding Round Trip Time (RTT). A stability graph is generated from the monitoring data in which vertices represent network nodes and an each edge means the corresponding nodes are considered to be stable during an observation period. Multiple different structures have been embedded on the stability graph to select a large enough number of nodes on which the new protocols are executed: based on degree, clique, and k-core. We compare the different strategies both in terms of the quality of the set of nodes returned and how they fare as time passes.

Network Neutrality states that all traffic in the Internet must be treated equally and thus cannot suffer unfair traffic differentiation (TD). Several solutions for detecting the presence of TD in the Internet have been proposed. However, locating where in the network TD is happening is still an open problem. In this work, we propose a strategy to locate Autonomous Systems (ASes) that are differentiating traffic. The proposed strategy takes advantage of AS-level routing properties to identify valid AS-level paths between end-hosts. It is then possible to select measurement points between which the AS-level paths traverse suspect ASes. Probes are sent from the measurement points and processed using end-to-end TD detectors based on statistical inference. The main idea is to check suspect ASes until only the AS that is actually discriminating traffic is filtered out. We first present results of experiments executed to validate the routing properties employed. Then the efficiency of the proposal for locating TD is evaluated using simulation. The results show that the proposed strategy is effective and efficient.

Summary The IoT is changing the way we interact with the world. Very soon, almost all of our daily tasks will be made through self intelligent systems embedded in devices scattered all around us. Their mission is to turn our cities, transportation systems, buildings, homes, and bodies in smart environments. These environments will bring us more comfort, improve our performance, increase our profits, and take away time‐consuming tasks. However, besides its great benefits, the IoT is also a big source of concerns, mainly because a good part of its devices will handle private and confidential information. Recently, cases of successful IoT invasions only worsen this scenario and show us that the today's adopted access control systems need to be replaced by more efficiently and secure ones. To overcome these access control problems, in this work, we present the ControlChain. The ControlChain is an access control authorization architecture that is heavily based on Blockchain technology. We also demonstrate the viability of the ControlChain through the E‐ControlChain, a proof‐of‐concept developed to run over the Ethereum network. Our proposals follows the IoT tendency requirements and are user‐transparent, user‐friendly, fully decentralized, scalable, fault tolerant, and compatible with a wide range of today's access control models already used in the IoT. Finally, we also make a cost and a performance analysis of E‐ControlChain, using a Raspberry Pi as an IoT device.

The availability of large Open Data sources creates opportunities for data analytics on different domains. But in order to be effectively used, the data needs to be correctly extracted, formatted and integrated, which is a specially challenging task on Open Data sources, since there is usually less rigour in standardizing subsequent data releases. This means Open Data evolution must be handled. A domain specific solution, taking stock of existing approaches, but with delimited kinds of operations and mappings, would be useful for providing coarse-grained management of data evolution operations throughout time. In this paper, we present an Open Data Evolution managing solution, aiming to integrate periodically released data sets. We define a set of operations acting over the instances, schema and mappings, which are executed after each new data release. These operations rely on the existence of a time dimension in the input mappings. The approach is validated on a real-world case study, which is being currently used to integrate and access a large Brazilian educational Open Data source, with billions of records and hundreds of columns evolving over many years. The proposed solution is used to process this data source, successfully integrating more than 90 data releases from 2012 to 2018.

Machine Learning as a Service (MLaaS) platforms enables access to Machine Learning (ML) processing with scalable infrastructure, from anywhere, and at any time, but requires sending large amounts of data to the cloud. ML on the edge is emerging as an option to reduce latency and bandwidth usage, maintaining data privacy. However, the existing edge approaches are not aware of the current network state for orchestrating the tasks. Network- aware orchestration services are supported by the Network Function Virtualization (NFV) architecture, making it a promising approach to manage and place ML tasks. In this paper, we propose Machine Learning Function Virtualization (MLFV), a fully network-aware framework that explores the NFV environment to virtualize ML tasks as virtual network functions. We describe a novel model for placing chains of ML tasks, considering constraints on CPU, memory, the existence of ML libraries, and the network overload, aiming to reduce the overall execution time of a chain. The results showed that MLFV outperformed existing cloud and edge approaches, particularly when network connections present instabilities. MLFV was able to identify the irregularities, allocating the ML tasks on hosts with normal connections, and thus, reducing the time for classifying single and multiple concurrent requests.

Artificial immune systems (AIS) are computational intelligence inspired by the human biological immune system. The AIS four main algorithms are negative selection, clonal selection, immune network, and danger theory. This paper incorporates the AIS approach to develop an agent-based detection method to analyze network traffic. The system works with an attack graph based correlation technique. This technique can improve detection performance by decreasing false alerts. This work was tested for denial of service (DoS), remote to local (R2L), user to root (U2R) and probe attack classes. Results have shown the addition of the correlation technique can aid to the detection performance of AIS detection systems.

Data analytics and scientific computing are two modern applications that in recent years have substantially changed their computation and communication needs, requiring additional processing capability and bandwidth to be able to keep pace with current demands. These applications are commonly processed within data centers, exchanging enormous volumes of data, rapidly stressing existing network infrastructures. Thus, it is crucial for data center operations and management to be able to understand and classify the communication demands of these applications. The traditional approaches for classifying application traffic are port-based and Deep Packet Inspection, both presenting issues with current network technology. Some recent works propose using machine learning plus statistical information collected from application flows to classify traffic. Applications running in data centers present communication patterns which can be recognized through their traffic matrices. So, the main contribution of this paper is a method that explores the textural information extracted from these matrices to classify the data center traffic using machine learning techniques. As a proof-of-concept, we implemented this method in a system named DCTraCS. The experimental dataset was gathered from two real data centers, collecting the traffic matrices of MapReduce and a set of scientific applications every second for a period of 30 minutes. For assessing our proposal, we compared it with other machine learning techniques for classifying application traffic found in current literature. Results show that our approach achieved the highest accuracy, classifying correctly over 99% of our data center applications.

Traffic reduction in network segments through cache implementations has become a major research topic due to the exponential increase in data requests through the network. Even with high-speed connections, the conventional model still depends on point-to-point communication between two systems. Throughout the world, more connected devices are, accessing services and obtaining information. To support this activity, servers must have massive storage to support creation, retrieval, updated and deletion of large amounts of data. Therefore, in studies of Information Centric Networks (ICN), this model has been widely discussed as the new content distribution model for the Internet. To provide improved network management many approaches are using software-defined networks (SDN) to develop flexible content-based networks. This paper proposes to use cache replication for ICN through SDN to avoid duplicated requests in the same connection. The redundant cache reduced the bandwidth consumed by duplicated requests, from a maximum of 3.20 Gbps to 2.07 Gbps, reducing the bandwidth consumption by 11.3%.

Recently we are seeing a considerable effort from both academy and industry in proposing new technologies for storage devices. Often these devices are not readily available for evaluation and methods to allow performing their tests just from their performance parameters are an important tool for system administrators. Simulators are a traditional approach for carrying out such evaluations, however, they are more suitable for evaluating the storage device as an isolate component, mostly due to time constraints. In this paper, we propose an approach based on virtual machine technology that is capable of emulate storage devices transparently for the operating system allowing evaluation of simulating devices within a real system using any synthetic or real workload. To emulate devices in real environments it is necessary to use the currently available devices as a storage medium which creates a difficulty when the device to be emulated is faster than this storage medium. To circumvent this limitation we introduce a new technique called Freezing Time, which takes advantage of virtual machine pausing mechanism to manipulate the virtual machine clock and hide the real I/O completion time. Our approach can be implemented just requiring the hypervisor to be modified, providing a high degree of compatibility and flexibility since it is not necessary to modify neither the operating system nor the application. We evaluate our tool under a real system using old magnetic disks to emulate faster storage devices. Experiments using our technique presented an average latency error of 6.08% for read operations and 6.78% for write operations when comparing a real to device.

The Brazilian government is maintaining several digital inclusion projects, providing computers and Internet connection to developing regions around the country. However, these projects can only succeed if they are constantly assessed; namely, the projects infrastructure deployment must be closely monitored and evaluated. In this paper, we introduce a system called SIMMC, which is currently monitoring and evaluating more than 4,500 computing devices from Brazilian digital inclusion projects. This system is innovative because, in addition to being used by the government for managing and expanding its projects, the collected data is also publicly available on a web page, allowing the citizens to follow the projects' deployment. We describe the SIMMC architecture, reporting some techniques used to optimize its data analysis processes, and describe how the information acquired and presented by the system has been used to enable public administration overhaul and improve efficiency on the project management, as well as its strategic use for security, theft, and defrauding.

The IoT is pervading our daily activities and lives with devices scattered all over our cities, transport systems, buildings, homes and bodies. This invasion of devices with sensors and communication capabilities brings big concerns, mainly about the privacy and confidentiality of the collected information. These concerns hinder the wide adoption of the IoT. To overcome them, in this work, we present an Blockchain-based architecture for IoT access authorizations. Following the IoT tendency requirements, our architecture is user transparent, user friendly, fully decentralized, scalable, fault tolerant and compatible with a wide range of today's access control models used in the IoT. Finally, our architecture also has a secure way to establish relationships between users, devices and group of both, allowing the assignment of attributes for these relationships and their use in the access control authorization.

Network Neutrality is essential for ensuring a level playing field for the development of new applications and services on the Internet. Laws and rules alone might not be enough to protect innovation, fair competition and consumer's freedom of choice online. The research community has the responsibility to propose solutions that reveal discriminatory traffic management mechanisms on the Internet. We present the potential risks of a non-neutral Internet, identify several open challenges for designing solutions that detect traffic differentiation, and propose a model that addresses such challenges by taking advantage of distributed systems technologies.

Scientific applications demand huge computational power connected through fast networks. They are developed using parallel kernel methods, usually implemented with the Message Passing Interface (MPI), presenting well-behaved communication patterns across computing nodes. The current network technologies do not allow defining traffic forwarding policies considering the different application traffic, resulting in an unbalanced load on the network links. Moreover, the devices are not concerned if the traffic is latency-sensitive or bandwidth-intensive. To handle this, we present NetSA, a framework exploiting the communication patterns of scientific applications, considering latency and bandwidth constraints, as the key logic for evenly placing the application flows on the network available paths. Through NetSA, the scientific application developer can easily modify the network behavior to best fit the application communication requirements. We have performed experiments for optimizing the MPI communication primitives and applied our solution to speed up scientific applications, obtaining an execution time reduction up to 27%.

Scientific applications (SciApps) are broadly used in all science domains. For more accurate results, they have been increasingly demanding computational power and extremely agile networks. These applications are usually implemented using numerical methods presenting well-behaved patterns to exchange data across its computing nodes. This paper presents SpateN, a tool that exploits the spatial communication patterns of SciApps as the fundamental logic to drive the network programming. SpateN classifies the SciApps nodes communications and balances the elephant flows across the available network paths. As a proof of concept, we carried out a set of experiments in real testbeds, demonstrating that network programming may affect the performance of SciApps significantly. Also, a balanced flow allocation can speed up SciApps to near-optimal execution times.

This paper describes the architecture and design of Cubrick, a distributed multidimensional in-memory DBMS suited for interactive analytics over highly dynamic datasets. Cubrick has a strictly multidimensional data model composed of cubes, dimensions and metrics, supporting sub-second OLAP operations such as slice and dice, roll-up and drill-down over terabytes of data. All data stored in Cubrick is range partitioned by every dimension and stored within containers called bricks in an unordered and sparse fashion, providing high data ingestion rates and indexed access through any combination of dimensions. In this paper, we describe details about Cubrick's internal data structures, distributed model, query execution engine and a few details about the current implementation. Finally, we present results from a thorough experimental evaluation that leveraged datasets and queries collected from a few internal Cubrick deployments at Facebook.

Elasticity is undoubtedly one of the most striking characteristics of cloud computing. Especially in the area of high performance computing (HPC), elasticity can be used to execute irregular and CPU-intensive applications. However, the on- the-fly increase/decrease in resources is more widespread in Web systems, which have their own IaaS-level load balancer. Considering the HPC area, current approaches usually focus on batch jobs or assumptions such as previous knowledge of application phases, source code rewriting or the stop-reconfigure-and-go approach for elasticity. In this context, this article presents AutoElastic, a PaaS-level elasticity model for HPC in the cloud. Its differential approach consists of providing elasticity for high performance applications without user intervention or source code modification. The scientific contributions of AutoElastic are twofold: (i) an Aging-based approach to resource allocation and deallocation actions to avoid unnecessary virtual machine (VM) reconfigurations (thrashing) and (ii) asynchronism in creating and terminating VMs in such a way that the application does not need to wait for completing these procedures. The prototype evaluation using OpenNebula middleware showed performance gains of up to 26 percent in the execution time of an application with the AutoElastic manager. Moreover, we obtained low intrusiveness for AutoElastic when reconfigurations do not occur.

Summary Network overlays support the execution of distributed applications, hiding lower level protocols and the physical topology. This work presents DiVHA: a distributed virtual hypercube algorithm that allows the construction and maintenance of a self‐healing overlay network based on a virtual hypercube. DiVHA keeps logarithmic properties even when the number of nodes is not a power of two, presenting a scalable alternative to connect distributed resources. DiVHA assumes a dynamic fault situation, in which nodes fail and recover continuously, leaving and joining the system. The algorithm is formally specified, and the latency for detecting changes and the subsequent reconstruction of the topology is proved to be bounded. An actual overlay network based on DiVHA called HyperBone was implemented and deployed in the PlanetLab. HyperBone offers services such as monitoring and routing, allowing the execution Grid applications across the Internet. HyperBone also includes a procedure for detecting groups of stable nodes, which allowed the execution of parallel applications on a virtual hypercube built on top of PlanetLab. Copyright © 2014 John Wiley & Sons, Ltd.

The Barnes-Hut algorithm is a widely used approximation method for the N-Body simulation problem. The irregular nature of this tree walking code presents interesting challenges for its computation on parallel systems. Additional problems arise in effectively exploiting the processing capacity of GPU architectures. We propose and investigate the applicability of software Simulated Wide-Warps (SWW) in this context. To this extent, we explicitly deal with dynamic irregular patterns in data accesses with data remapping and data transformation, by controlling execution flow divergence of threads. We present a new compact data-structure for the tree layout, GPU parallel algorithms for tree transformation and parallel walking using SWW. Benefits of our techniques are in transposing the tree algorithm to execute regular patterns to match the GPU model. Our experiments show significant performance improvement over the best known GPU solutions to this algorithm.

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. In order to explore this feature, several works addressed the development of frameworks and platforms focusing the construction of elastic parallel and distributed applications for IaaS clouds. However, none of these works addressed the exploration of elasticity in multithreaded applications. In this paper, we propose a mechanism to provide elasticity support for OpenMP applications, making possible the dynamic provisioning of cloud resources taking into account the program structure and runtime requirements. In our proposal, the OpenMP directives were modified to support the dynamic adjustment of resources and a set of routines were included to the user-level library in order to enable the configuration of the the elastic execution. Dynamic memory allocation support was also included in elastic OpenMP library. We also present the architecture and implementation of the proposed mechanism. The experiments validate our approach and show some possibilities to use the elastic OpenMP.

Disk schedulers should provide QoS guarantees to applications, thus sharing proportionally the storage resource and enforcing performance isolation. Disk schedulers must execute requests in an efficient order though, preventing poor disk usage. Non-work-conserving disk schedulers help to increase disk throughput by predicting future requests' arrival and therefore exploiting disk spatial locality. Previous work are limited to either provide QoS guarantees or exploit disk spatial locality. In this paper, we propose a new non-work-conserving disk scheduler called High-throughput Token Bucket Scheduler (HTBS), which can provide both QoS guarantees and high throughput by (a) assigning tags to requests in a fair queuing-like fashion and (b) predicting future requests' arrival. We show through experiments with our Linux Kernel implementation that HTBS outperforms previous QoS aware work-conserving disk schedulers throughput as well as provides tight QoS guarantees, unlike other non-work-conserving algorithms.

Users of large scale network testbeds often execute experiments that require a set of nodes that behave and communicate among themselves in a reasonably stable pattern. In this work we call such a set of nodes a stable clique, and introduce a monitoring strategy that allows their detection in PlanetLab, a non-trivial task for such a large scale dynamic network. Nodes monitor each other by sampling the RTT (Round-Trip-Time) and computing its variation. Based on this data and a threshold, pairs of nodes are classified as stable or unstable. A set of graphs is generated, on which maximum sized cliques are computed. Three experiments were conducted in which hundreds of nodes were monitored for several days. Results show the unexpected behavior of some nodes, and the size of the maximum stable clique for different time windows and different thresholds.

In the world of intelligent tutoring systems research, the connection between learner models and interaction models has been largely ignored. Similarly, previous works have not accounted for the dynamic representation of interface contents based on the underlying pedagogic directives. The paper describes an approach to the authoring of training contents aimed at intermediate-level learners of medical Radiology. It is argued that, in certain dialogues designed for supporting visual diagnosis, it is worthwhile employing domain-general teaching mechanisms and reuse them in various domain-specific situations. An empirical study has been carried out with a corpus of human-to-human tutorial dialogues to identify the component features of expertise in medical Radiology. The results of the study form the basis for implementing interface and learner models that guide long-term tutorial interactions through an intelligent shell called RUI. Conclusions and future research directions are also described briefly.

The importance of digital information is constantly increasing in the last years. Such information often needs to be preserved for a long-term and this is the responsibility of digital archiving systems. This paper proposes a reliable replication model of immutable digital content to be used in long-term archiving systems. The archiving system is modeled as a set of storage repositories where each repository has an independent fail probability assigned to it. Items are inserted with a reliability that is satisfied by replicating them in subsets of repositories. Through simulation, we evaluated three different proposed strategies to create replicas. It is also proposed a completely distributed archiving system using this model over a structured peer-to-peer (P2P) network. The communication between the nodes (repositories) of the network is organized in a distributed hash table and multiple hash functions are used to select repositories that will keep the replicas of each stored item. The system is evaluated through experiments in a real environment. The proposed model and the algorithms, combined with the structured P2P scalability made possible the construction of a reliable and totally distributed digital archiving system.

This paper describes how cognitive and computational concepts can be applied to build interface and learner models for long-term tutorial interactions in medical radiology. Key human-to-human tutorial dialogue factors that typically occur in the different stages of skill acquisition are captured through an empirical study. The results of the study are detailed and linked to the design of RUI, an Intelligent Tutoring System for multiple domains of radiological expertise. A brief discussion and future research directions offer a comparative view of the method and tools.

This paper presents HyperBone, an overlay network based on a virtual hypercube that offers services such as monitoring and routing, allowing the execution of distributed applications across the Internet hypercubes are scalable by definition, presenting several properties such as symmetry and logarithmic diameter, that are advantageous for distributed and parallel applications. HyperBone nodes run the distributed virtual hypercube algorithm (DiVHA) in order to maintain the topology. DiVHA keeps the hypercube properties even when the number of nodes is not a power of two, or under a dynamic fault situation, in which nodes fail and recover continuously, leaving and joining the system. HyperBone was implemented and experimental results are presented, obtained from the execution of a set of MPI parallel applications on a virtual hypercube spread across the world built with PlanetLab nodes.

This paper presents an autonomic monitoring strategy for highly-dynamic systems based on DiVHA - the distributed virtual hypercube algorithm. Hypercubes are scalable by definition, presenting several advantageous properties such as symmetry and logarithmic diameter. A system based on DiVHA keeps the hypercube properties even when the number of nodes is not a power of two, or under a dynamic fault situation, in which nodes fail and recover continuously, leaving and joining the system. In particular, the paper describes a strategy for dealing with unstable nodes, which also allows the discovery of nodes that present a more predictable behaviour. The system was implemented in PlanetLab, a highly-dynamic large scale environment that spans the globe, and and experimental results are presented.

This paper presents HyperBone, an overlay network based on a virtual hypercube that offers services such as monitoring and routing, allowing the execution of distributed applications across the Internet. Hypercubes are scalable by definition, presenting several properties such as symmetry and logarithmic diameter, that are advantageous for distributed and parallel applications. HyperBone nodes run the Distributed Virtual Hypercube Algorithm (DiVHA) in order to maintain the topology. DiVHA keeps the hypercube properties even when the number of nodes is not a power of two, or under a dynamic fault situation, in which nodes fail and recover continuously, leaving and joining the system. HyperBone was implemented and experimental results are presented, obtained from the execution of a set of MPI parallel applications on a virtual hypercube spread across the world built with PlanetLab nodes.

This paper describes an interface and interaction environment for a public online Chess server, on the web, as an educational tool. The main purpose of the environment is to improve Chess teaching in Brazilian public schools. The vast majority of such Chess online servers consider and take for granted that users are specialists rather than learners. The solution described in this paper is inserted in an education environment, aiming at providing users with direct access to the contextually significant actions by means of strategic and operational help.

This work presents a dependable fully distributed network management tool based on the Internet standard network management protocol, SNMP (Simple Network Management Protocol). Multiple SNMP agents running the Hi-ADSD with Timestamps, a Hierarchical Distributed System-Level Diagnosis algorithm with Timestamps, monitor themselves and a configurable set of network services and devices, issuing controlling commands depending on the results. The system is dependable in the sense that it continues working even if only one agent is fault-free. A MIB (Management Information Base) allows the definition of test procedures specific for each managed entity. The system presents a configurable Web interface that allows the human manager to monitor the network from any agent. Practical results are presented, including the construction of a resilient Web server built on top of the tool.