Time-sensitive networking (TSN) is a promising technology for local area applications that require ultra-low latency, such as industrial automation, audio and video bridging, and 5G fronthaul transport. TSN guarantees deterministic packet transport with low latency, low packet delay jitter, and low packet loss. Correct provisioning of end-to-end latency is crucial for achieving optimal network performance and satisfying flow latency constraints. As TSN increasingly supports cloud and fog computing, managing virtualization overhead becomes critical to preserving latency performance. This work analyzes and compares TSN simulated, bare metal, and virtualized network deployments. The study focuses on an important TSN mechanism, namely, the IEEE 802.1Qav credit-based shaper (CBS). It examines its impact on end-to-end latency. Experiments show that the conducted TSN simulations provide latency results close to those from real-world setups, validating simulation as a reliable tool for TSN prototyping. Meanwhile, deploying TSN networks in virtualization-based scenarios presents specific trade-offs. All configurations achieved an average latency of less than 1 ms across all tested scenarios when the TSN idle slope parameter was tuned correctly to prioritize CBS-regulated traffic. Notably, despite the processing overhead introduced by virtualization, optimized configurations, such as those using kernel bypassing, outperformed bare metal deployments in terms of latency. However, this improvement came at the cost of reduced throughput, as fewer packets were forwarded compared to other scenarios. These findings underscore the importance of CBS parameter tuning and highlight the balance between performance and stability in virtualized TSN environments.

Abstract The network function virtualization (NFV) paradigm replaces hardware‐dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility. For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof‐of‐concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions.

Modern telecommunications networks are characterised by increased dynamism and heterogeneity from the cloud to the edge. To meet these requirements and remain competitive, network operators use software defined networking (SDN) and network functions virtualisation (NFV) to make networks programmable, flexible, and agile. Service function chaining (SFC) enables NFV through the ordered sequencing of VNFs and subsequent steering of flows through them to provide end-to-end services. SFCs must meet availability constraints while avoiding resource wastage due to unnecessary redundancy. This paper proposes a novel approach to evaluate the availability of SFC placement considering the associated redundancy strategies using Stochastic Petri Net (SPN) models. We propose an algorithm to generate and resolve relatively small SPN models for SFC availability in an automatic way and then compare baseline SPN models with this algorithm across five SFC placement scenarios. In all scenarios, the proposed algorithm outperformed baseline SPN models in runtime performance.

Summary A data center infrastructure is composed of heterogeneous resources divided into three main subsystems: IT (processor, memory, disk, network, etc.), power (generators, power transformers, uninterruptible power supplies, distribution units, among others), and cooling (water chillers, pipes, and cooling tower). This heterogeneity brings challenges for collecting and gathering data from several devices in the infrastructure. In addition, extracting relevant information is another challenge for data center managers. While seeking to improve the cloud availability, monitoring the entire infrastructure using a variety of (open source and/or commercial) advanced monitoring tools, such as Zabbix, Nagios, Prometheus, CloudWatch, AzureWatch, and others is required. It is often common to use many monitoring systems to collect real‐time data for data center components from different subsystems. Such an environment brings an inherent challenge stemming from the need to aggregate and organize the whole collected infrastructure data and measurements. This first step is necessary prior to obtaining any valuable insights for decision‐making. In this paper, we present the Data Center Availability (DCA) System, a software system that is able to aggregate and analyze data center measurements aimed toward the study of DCA. We also discuss the DCA implementation and illustrate its operation, monitoring a small University research laboratory data center. The DCA System is able to monitor different types of devices using the Zabbix tool, such as servers, switches, and power devices. The DCA System is able to automatically identify the failure time seasonality and trend present in the collected data from different devices of the data center.

In this paper, we propose the use of reinforcement learning to deploy a service function chain (SFC) of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenario with distributed data centers, where the virtual network functions (VNFs) are deployed in virtual machines in commodity servers. The VNF management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO2) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets.

With the continuous growth of the number of mobile devices connected to the Internet, cellular network infrastructure owners are facing several new challenges. The fifth generation of mobile technology (5G) is planned to enable a fully mobile and connected society and to empower socio-economic transformations. In 5G, different scenarios with high and strict requirements are being deployed. To deal with such heterogeneity, more advanced communication services are required. Network Function Virtualization (NFV) combined with distributed micro data centers (MDCs) can improve the management of the data flows through several 5G base stations. On the one hand, NFV provides high scalability without requiring significant architectural changes; and on the other hand, distributed MDCs decentralize architecture management, bringing the computational capabilities nearer to the base stations, hence reducing transmission delay. Nonetheless, the introduction of MDCs raises new points of failures in the architecture, impacting user experience. In this paper, we evaluate the impact of allocating MDCs to host base station functionalities; similarly, network functions run as virtual machines in the MDC, and their failures cannot be ignored. We consider a real footrace scenario as a case study. As packets are lost due to the high mobility of the runners and spectators we examine this metric. From simulations results, we discover that as the number of MDCs increases, the number of failures increases as well. However, the impact of each failure in terms of lost packets decreases considerably when considering scenarios with more MDCs. For example, by increasing the number of MDCs from one to seven, the failure impact in terms of lost packet drops by around 87.62%.

Summary Next‐generation cloud data centers are based on software‐defined data center infrastructures that promote flexibility, automation, optimization, and scalability. The Redfish standard and the Intel Rack Scale Design technology enable software‐defined infrastructure and disaggregate bare‐metal compute, storage, and networking resources into virtual pools to dynamically compose resources and create virtual performance‐optimized data centers (vPODs) tailored to workload‐specific demands. This article proposes four chassis design configurations based on Distributed Management Task Force's Redfish industry standard applied to compose vPOD systems, namely, a fully shared design, partially shared homogeneous design, partially shared heterogeneous design, and not shared design; their main difference is based on the used hardware disaggregation level. Furthermore, we propose models that combine reliability block diagram and stochastic Petri net modeling approaches to represent the complexity of the relationship between the pool of disaggregated hardware resources and their power and cooling sources in a vPOD. These four proposed design configurations were analyzed and compared in terms of availability and component's sensitivity indexes by scaling their configurations considering different data center infrastructure. From the obtained results, we can state that, in general, when one increases the hardware disaggregation, availability is improved. However, after a given point, the availability level of the fully shared, partially shared homogeneous, and partially shared heterogeneous configurations remain almost equal, while the not shared configuration is still able to improve its availability.

The next-generation data center introduces the refactoring of the traditional data center in order to create pools of disaggregated resource units, such as processors, memory, storage, network, power, and cooling sources, named composable system (CSs) with the purpose of offering flexibility, automation, optimization, and scalability. In this paper, we solve an optimization problem to allocate CSs considering next- generation data centers. The main goal is to maximize the CS availability for the application owner, having its minimum requirements (in terms of CPU, memory, network and storage), and available budget as restrictions. This problem is modeled as a bounded multidimensional knapsack problem, and we solve it using Dynamic Programming (DP), and two Soft Computing approaches: Differential Evolution (DE) and Particle Swarm optimization (PSO). We consider two different scenarios in order to analyze heterogeneity and variability aspects when allocating CSs in a data center. Moreover, we also analyze the importance of system components to give directions and priorities of actions to upgrade the system design.

Summary Emergency call services are expected to be highly available in order to minimize the loss of urgent calls and, as a consequence, minimize loss of life due to lack of timely medical response. This service availability depends heavily on the cloud data center on which it is hosted. However, availability information alone cannot provide sufficient understanding of how failures impact the service and users' perception. In this paper, we evaluate the impact of failures on an emergency call system, considering service‐level metrics such as the number of affected calls per failure and the time an emergency service takes until it recovers from a failure. We analyze a real data set from an emergency call center for a large Brazilian city. From stochastic models that represent a cloud data center, we evaluate different data center architectures to observe the impact of failures on the emergency call service. Results show that changing data center's architecture in order to improve availability from two to three nines cannot decrease the average number of affected calls per failure. On the other hand, it can decrease the probability to affect a considerable number of calls at the same time.

Summary To assess the availability of different data center configurations, understand the main root causes of data center failures and represent its low‐level details, such as subsystem's behavior and their interconnections, we have proposed, in previous works, a set of stochastic models to represent different data center architectures (considering three subsystems: power, cooling, and IT) based on the TIA‐942 standard. In this paper, we propose the Data Center Availability (DCAV), a web‐based software system to allow data center operators to evaluate the availability of their data center infrastructure through a friendly interface, without need of understanding the technical details of the stochastic models. DCAV offers an easy step‐by‐step interface to create and configure a data center model. The main goal of the DCAV system is to abstract low‐level details and modeling complexities, becoming the data center availability analysis a simple and less time‐consuming task.

The Internet of Things already comprehends billions of connected smart devices. The related issues are vast as well, ranging from the lack of standardized communication to big data management. This paper presents IoT Management and Monitoring System (IMMS), a solution focused on the management of big environments such as smart cities. The The proposed platform brings a novel database implementation to handle large volumes of data and provide quick responses to queries. Its design allows scalable and distributed deployments, while accepting diverse data types. The system was evaluated both in a real-world testbed, in a university campus, and a simulated environment. Results showed low CPU costs while consuming 1.1GB to store 21.5 million measurements. Besides, its scalability behaved as expected, maintaining its performance adequate as data volume increased.

The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in realtime and offer a range of multimedia-based health services. However, lack of service availability, particularly in the cases of emergencies, can lead to adverse outcomes and in the worst case, death. In this paper, we propose an e-health monitoring architecture based on sensors and cloud and fog infrastructure scenarios. Further, we propose stochastic models to analyze how failures impact on the e-health system availability. We analyze four different scenarios and from results, we identify that the sensors and fog devices are the components that have the most significant impact on the availability of the entire e-health system in the scenarios analyzed.

The Internet of Things (IoT) has emerged as one of the prominent concepts in academic discourse in recent times reflecting a wider trend by industry to connect physical objects to the Internet and to each other. The IoT is already generating an unprecedented volume of data in greater varieties and higher velocities. Making sense of such data is an emerging and significant challenge. Infographics are visual representations that provide a visual space for end users to compare and analyze data, information, and knowledge in a more efficient form than traditional forms. The nature of loT requires a continuum modification in how end users see information to achieve such efficiency gains. Conceptualizing and implementing Infographics in an loT system can thus require significant planning and development for both data scientists, graphic designers and developers resulting in both costs in terms of time and effort. To address this problem, this paper presents SiMoNa, a domain-specific modeling language (DSML) to create, connect, interact, and build interactive infographic presentations for loT systems efficiently based on the model-driven develonment (MDD) naradiam.

The player should enjoy games through the game controller of their preference and keep the best possible game experience regardless their option. This article proposes a novel classification model for game interaction techniques from the player perspective and with direct control of a single humanoid avatar based on three characteristics: Effort, Duration, and Context. Combining the three characteristics, we classify the game interaction techniques into nine isomorphic categories. We conducted three experiments. The first (N = 57) and second (N = 45) were a paired quantitative comparison evaluation method, allowing the volunteer to play the same game level twice, changing only the interaction technique for one action. This aimed at validating our initial proposal with users. The third (N = 100) was also a comparative test. The volunteer played the same stage twice using the button-interaction based version as a reference against one of another four device-based versions. The goal was to extend the proposed classification model to other interaction devices to check if the interaction metaphor was kept coherent. In all three experiments, we chose the 202 participants as a convenience sample. The users’ experiments support all the research hypotheses. We conclude that, if our proposed guideline with our interaction technique classification model is applied, it may expect a positive impact on the players’ experiences.

Many enterprises rely on cloud infrastructure to host their critical applications (such as trading, banking transaction, airline reservation system, and credit card authorization). The unavailability of these applications may lead to severe consequences that go beyond the financial losses, reaching the cloud provider reputation too. However, to maintain high availability in a cloud data center is a difficult task due to its complexity. The power subsystem is crucial for the entire operation of the data center because it supplies power for all other subsystems, including IT components and cooling equipment. Some studies have already proposed models to evaluate the availability of the power subsystem, but none of them are based on standard redundancy models. Standards guide cloud providers regarding availability, points of failure, and watts per square foot based on components' redundancy. This paper proposes RBD and Petri Net models based on the TIA-942 standard to estimate the availability of the data center power subsystem and analyze how failures on power subsystem impact the availability of critical applications. These models are important to resource planning and decision making by the cloud providers, because they may identify which components they ought to invest in order to improve the availability level.

The Network Access Control (NAC) management is a critical task, especially in current networks that are composed of many heterogeneous things (Internet of Things) connected to share data, resources and Internet access. The Software-Defined Networking (SDN) simplifies the network design and operation, and offers new opportunities (programmability, flexibility, dy-namicity, and standardization) to manage the network. Despite this, the access control management remains a challenge, once managing security policies involves dealing with a large set of access control rules, detecting conflicting policies, defining priorities, delegating rights, and reacting against network state changes and events. This work presents the HACFlow, a novel, autonomic, and policy-based framework for access control management in OpenFlow networks. HACFlow aims to simplify and automate the network management allowing network operators to govern rights of network entities by defining dynamic, fine-grained, and high-level access control policies. We analyzed the performance of HACFlow and compared it against related approaches.

A data center is divided into three basic subsystems: information technology (IT), power, and cooling. Cooling plays an important role related to data center availability, and a failure in this subsystem may cause an interruption of services. Generally, a redundant cooling subsystem is implemented based on replacing the failed component by the standby one. However, it also can be based on a rotation of computer room air conditioners (CRACs). This paper proposes scalable models that represent the cooling subsystem behavior to evaluate the impact of cooling failures on the data center availability. Models are based on the TIA-942 standard and represent Tiers I and II. We validate our model by comparing our results with the literatures. Our results show that the CRACs' rotation has similar results in availability when compared to the replace strategy.

Cloud computing has gained popularity in recent years due to its pay-as-you-go business model, high availability of services, and scalability. Service unavailability does not affect just user experience but is also translated into direct costs for cloud providers and companies. Part of this costs is due to SLA breaches, once interruption time greater than those signed in the contract generate financial penalties. Thus, cloud providers have tried to identify failure points and estimate the availability of their services. This paper proposes models to assess the availability of services running in a cloud data center infrastructure. The models follow the TIA-942 standard. We propose Tier I and IV models using the Reliability Block Diagram (RBD) to allow modeling of different types of applications, and Stochastic Petri Net (SPN) to represent the failure behavior of information technology (IT) components in a data center. We perform stationary analysis to measure the service availability, and sensitivity analysis to understand which metrics have major impacts on data center availability.

The popularity of Internet video streaming services has dramatically increased, leveraged by the emerging Dynamic Adaptive Streaming over HTTP (DASH) standard. DASH also favors the growth of video content access by mobile devices. However, due to fluctuating bandwidth and high latency, mobile networks, e.g. 3G, remain a challenging environment. The user's Quality of Experience (QoE) in this scenario constantly needs to improve. One way to achieve this is through the development of a better adaptation logic that reduces rebuffering events and delivers higher and more stable video qualities. In this paper, we present DBuffer, a control system developed for improving the QoE during a streaming session through mobile networks. DBuffer estimates the ideal video quality to request, during a 3G streaming session, through client-side buffer analysis. The main component of the control system is a buffer-based state machine, which manages the adaptation logic. Our experimental results show that DBuffer outperforms some of the existing advanced adaptation techniques.

Cloud outages, no matter how long, are responsible for large financial losses. Cloud providers look for solutions that provide high availability even in failure cases. Redundancy has been often used as an alternative; however it has a cost, and depending on the redundancy model, we can have different levels of availability. Beyond that, adding more resources also means adding more complexity and more point of failures. In this paper, we propose a risk-based model for estimating the availability of a redundancy setup. In order to analyze our model, we conduct Monte Carlo simulations and results show that for all models, the number of applications we deploy in a Cloud environment impacts on its availability.

Installing flexible oil pipes in deep underwater environments is a risky operation, which is currently monitored by manually comparing the geometric conditions to the simulated studies. In order to avoid this error-prone assessment, this paper proposes an algorithm to track and reconstruct the pipe medial axis using stereo cameras. By taking advantage of the scenario illumination and the pipe texture, it is possible to devise an energy maximization approach that efficiently tracks the pipe. The reconstruction technique is an enhancement over a state-of-the-art B-spline reconstruction algorithm, specializing it for plane curves. Experiments on synthetic and laboratory videos report a very low mean tracking and reconstruction errors, while there are strong evidences of the robustness on a real scenario.

This paper presents a novel segmentation technique for flexible pipes in deep underwater environments. It consists in finding regions of the pipe denominated vertebrae and has three stages. Firstly, the input image is pre-processed to reduce noise. Secondly, the pre-processed image is binarized by the proposed Multi-level Topological Binarization technique, resulting in an image which highlights (in white) the regions that are potential vertebrae (the blobs). Lastly, the pipe is segmented by finding the best sequence of blobs that fulfills a set of restrictions inherent to a pipe. For improving the technique robustness, an alternating pattern of white (a vertebra) and black regions is marked on the pipe. Results show that the proposed technique can segment pipes even under harsh conditions such as: low contrast between pipe and background; uneven illumination of the pipe; and high level of noise due to floating particles. In particular, the proposed binarization technique achieves valuable results without any parameter, whereas state-of-the-art techniques did not achieve the same quality even after their parameters were fine-tuned for each condition. Two blurring filters are used for pre-processing input image: a bilateral filter [5] removes small particles floating around, while preserving the vertebrae’s edges; and a Gaussian filter uniformly smooths the images. The proposed binarization technique finds peaks in the pre-processed image. This is done by slicing the image into a number of slices si,b (i is its index and b is its level). A tree of slices is then constructed by hierarchically connecting nodes of consecutive levels (Figure 1a). Intuitively speaking, peaks are prominences similar to the first one exhibited in Figure 1b. More formally, a peak is a sequence of nodes denoted by P = {nk}k=1 = n1,n2, . . . ,nm, such that: nm is a leaf node of the tree; nk, such that 1≤ k < m, is the parent node of nk+1; n1 is the only element that has a brother node, or n1 is the only element that has a parent with genus greater than zero, or n1 is the root node. Nodes n1 and nm are respectively named the base and the top of a peak. Given a tree of slices, one has to traverse it from leaves to root in order to find its peaks. For example, Figure 1a contains three peaks: the first one is P′ = s1,2,s1,3; the second is P′′ = s2,2,s2,3; and the third is P′′′ = s3,2,s3,3,s1,4.

Several works study the performance of Content Delivery Networks (CDNs) under various network infrastructure and demand conditions. Some go even further while considering demand elasticity by leveraging on Cloud elasticity where each Content Service Provider (CSP) is served only the amount of storage space and network throughput that it needs and pays accordingly. Many of today's CDN implementations are based on these proposals. However, the allocation of virtual servers in cloud computing is not yet very fine-grained and suffers from limitations and time constraints for reallocation. This work proposes an efficient cache slicing mechanism that frees the CDN provider from dedicating a different virtual surrogate for each CSP in each location. This is achieved by dealing internally with the allocation space for each CSP in a shared surrogate, while still providing services in terms of network storage allowing a CSP to select and pay the amount of content that can be stored. We show that the proposed approach deals better with peak surrogate usage, and reduces the total network traffic by around 15% and cross-traffic by as much as 35% under some scenarios.

Content Distribution Networks (CDN) are a well-known and accepted way to deliver content. Considering the ever growing number of users and also the growing number of small localized CDNs, there is a strong trend for collaboration among them. CDN collaboration may cause cost reduction for CDN providers and better quality of experience for the end user. However, CDN collaboration raises performance issues. This paper presents an analytical view of collaboration among CDNs. Our analytical approach provides studies of collaboration performance through metrics such as cache hit, latency, and number of caches. Then, we present a collaboration overhead representation, considering two collaboration request routing strategies. From our performance analyses, we show that CDN collaboration can reduce response time and could also help a primary CDN deal with a flash crowd event.

Considerable effort has been made by researchers in the area of network traffic classification, since the Internet is constantly changing. This characteristic makes the task of traffic identification not a straightforward process. Besides that, encrypted data is being widely used by applications and protocols. There are several methods for classifying network traffic such as known ports and Deep Packet Inspection (DPI), but they are not effective since many applications constantly randomize their ports and the payload could be encrypted. This paper proposes a hybrid model that makes use of a classifier based on computational intelligence, the Extreme Learning Machine (ELM), along with Feature Selection (FS) and Multi-objective Genetic Algorithms (MOGA) to classify computer network traffic without making use of the payload or port information. The proposed model presented good results when evaluated against the UNIBS data set, using four performance metrics: Recall, Precision, Flow Accuracy and Byte Accuracy, with most rates exceeding 90%. Besides that, presented the best features and feature selection algorithm for the given problem along with the best ELM parameters.

This paper approaches the issues of human-robot interaction in extreme human-robot interface situations, defined as those which provide a real physical intervention in a person's body by a robot. Robots that belong to this group perform tasks such as tattooing, collecting blood and doing surgery. Authors use a Lynxmotion AL5B mechanical arm controlled via Botboarduino named BOTicelli to make simple patterns with a felt-tip pen on volunteers' skin. The patterns are composed by clusters of dots, similar to real-world tattooing techniques. This paper intends to understand the major discomforts experienced by volunteers while interacting with BOTicelli and whether there are possible modifications of robot design or procedures that would render the experience less aggravating for humans.

Compressed Deterministic Finite Automata (DFA) promises same representation power as traditional DFAs while using less memory for representing Regular Expressions (RE). Experimental evaluations of DFA-based Deep Packet Inspection (DPI) systems focus mainly on memory consumption without observing other important related aspects, such as the matching speed. Proper design of DPI systems requires the assessment of several performance metrics at hardware level, in order to make sure that its implementation will not compromise the overall performance. This paper proposes a novel and systematic evaluation of DPIs and reveals the impact of DFA's data-structures and the correspondent memory layout implementation to hardware-level metrics. Experimental results show that some DFA model and memory layout combinations are almost 100 times faster than others. Results also show that choosing the incorrect model-layout pair can lead to significant performance issues. Our methodology and results will certainly help researchers and developers to design efficient DPI engines, through the selection of the best DFA model and memory layout combination to achieve the targeted overall performance.

Multi-radio multi-channel (MR-MC) ad hoc networks pose new configuration challenges, including routing. Although a large number of works exist that investigate the tuning of routing parameter settings, to the best of our knowledge, none of them investigates the impact of heterogeneity and the tuning of parameters on convergence interval and energy consumption. Heterogeneous Technologies Routing (HTR) is a soft-state framework suitable for interconnecting devices in a heterogeneous ad hoc network. In this paper, we investigate the impacts of tuning the HELLO refresh interval timer on the convergence of the HTR protocol and its subsequent energy consumption during this phase. We also compare our tuned HTR with the widely used Optimized Link State Routing (OLSR) protocol. The evaluation is validated through simulation using heterogeneous technologies such as WiMAX, 3GPP LTE and Wi-Fi. Results show that varying the HELLO refresh interval can improve the convergence time and reduce the energy consumption without major impact on network behavior.

The last two decades have witnessed major advances in the wireless communication area, which have been supported by the increasing capabilities of portable devices, such as notebooks, tablets, cellular phones, smart phones and personal digital assistants (PDAs). Today, they provide a rich computing environment and incorporate different radio technologies. Despite of network diversity, a great deal of research in the field of mobile ad hoc networks focuses on a single and isolated wireless technology at a time, with homogeneous elements identified by IP addresses. This work proposes a Heterogeneous Technology Routing Framework (HTR), targeted towards scenarios where the heterogeneity of devices and networking technologies is present. Our contribution is twofold. It consists of a framework, which encompasses a process for bootstrapping networks, and a routing protocol capable of dealing with multiple network interfaces. We then evaluate the performance of this protocol by taking measurements in a real test-bed.

Current network traffic analysis systems heavily rely on Deep Packet Inspection (DPI) techniques, such as Finite Automata (FA), to detect patterns carried by regular expression (regex). However, traditional Finite Automata cannot keep up with the ever-growing speed of the Internet links. Although there are a number of efficient FA compressing mechanisms for DPIs, there is no standardized or common way to evaluate and compare them. In this scenario, this paper proposes a methodology to evaluate and compare automaton models and the data-structures that materialize them. We also adapt state-of-the-art memory layouts to better fit in today's computer architectures. Finally, we apply our methodology to most important automaton models, memory layouts, and well-known signature sets. The results show us that some memory layouts are not efficient for regexes that represent small automata and other ones which fit only with uncompressed automata. Further, we also found out that theoretical studies about memory usage from memory encodings are not as accurate as they should be.

Deep Packet Inspection (DPI) systems have been becoming an important element in traffic measurement ever since port-based classification was deemed no longer appropriate, due to protocol tunneling and misuses of well-defined ports. Current DPI systems express application signatures using regular expressions and it is usual to perform pattern matching through the use of Finite Automaton (FA). Although DPI systems are essentially more accurate, they are also resource-intensive and do not scale well with link speeds. Looking to this area of interest, this paper proposes a novel Deterministic Finite Automaton, called Ranged Compressed Deterministic Finite Automaton (RCDFA), that compresses transitions without additional memory lookups. Experimental results show that RCDFA yields space savings of 97% over the original DFA and up to 93% better compression when compared to the DFA's state-of-the-art compression techniques.

In this paper we present implementation guidelines of the Distributed Cloud Resource Allocation System (D-CRAS). D-CRAS ensures an automatic monitoring and control of resources to guarantee the optimal functioning of the Cloud while meeting developers' requirements. Additionally, this work establishes the necessary technologies that meet the construction of the proposed system.

Autonomic Clouds emerge as a result of applying four self-management properties of Autonomic Computing (self configuration, self-healing, self-optimization, and self-protection) in Cloud environment. In this way, Autonomic Cloud Computing is seen as a Cloud with autonomy to take important decisions about resource management, such as the resource allocation for incoming requests, and the optimization decisions of resource utilization. The main goal of this paper is to make a brief discuss about issues of creating self-adaptive systems for Autonomic Clouds, focusing on infrastructure management level.

Distributed Clouds, or just D-Clouds, can be seen as a paradigm that is able to exploit the potential of sharing resources across geographic boundaries and provide latency-bound allocation of resources to third-party developers. The representation of D-Cloud resources is a challenge that involves the careful choice of characteristics that drive the mapping of requests on the substrate resources. Regarding these problems, this paper introduces the Cloud Modeling Language (CloudML), a vendor-neutral XML-based language intended to integrate the description of different cloud related aspects such as computational and network resources, services profiles, and developers' requests in an integrated way. Furthermore, the CloudML provides a way to describe geographical location aware services, seen particularly indispensable in D-Cloud scenarios.

Augmented Reality applications overlap virtual objects over a real scene, taking into account the context, in order to add information to the end user. Nowadays, more advanced applications also make use of Diminished Reality that removes real objects from a scene. This paper describes an approach that combines Augmented Reality and Diminished Reality techniques to modify real objects present in applications. The proposed approach removes an object and replaces it with its purposely-modified replica. The solution uses dynamic texture techniques and Inpaint to enhance the visual response of the modification performed. The results are promising considering both realism of the modified real object and performance of the application.

Augmented Reality applications overlap virtual objects over a real scene, taking into account context, in order to add information to the end user. Nowadays, more advanced applications also make use of Diminished Reality that removes real objects from a scene. This paper describes an approach that combines Augmented Reality and Diminished Reality techniques to modify real objects present in applications. The proposed approach removes an object and replaces it with its purposely-modified replica. The solution uses dynamic texture techniques and Inpaint to enhance the visual response of the modification performed. The results are promising considering both realism of the modified real object and performance of the application.

A 3D underlay routing protocol, for scenarios with device and networking technology heterogeneity is presented. It provides mechanisms for the seamless interoperation of dynamic, heterogeneous ad hoc networks. This underlay protocol aggregates and exchanges device context information, such as hardware resources, channel state information and network interfaces to build its routing decisions. 3D Routing considers the following fundamental emergency networking aspects: the bootstrapping process of the network(s) and nodes, the building and spreading of context information between the nodes, and the assignment of roles to particular nodes. To this end, we discuss how these ideas are designed and implemented in order to provide seamless interoperability for this type of emerging networking scenarios.

This paper presents a solution for the photorealistic rendering of synthetic objects into dynamic real scenes, in Augmented Reality applications. In order to achieve this goal, an Image Based Lighting approach is used, where environment maps with different levels of glossiness are generated for each virtual object in the scene at every frame. Due to this, illumination effects, such as color bleeding and specular reflections, can be simulated for virtual objects in a consistent way. A unifying sampling method for the spherical harmonics transformation pass is also used. It is independent of map format and does not need to apply different weights for each sample. The developed technique is combined with an extended version of Lafortune Spatial BRDF, featuring Fresnel effect and an innovative tangent rotation parameterization. The solution is evaluated in various Augmented Reality case studies, where other features like shadowing and lens effects are also exploited.

In this paper we share our experience in building an ADSL video delivery test-bed. As a result, we are able to measure the impact of important parameters such as the loop length, the presence of background traffic and line protection against transmission errors on video quality. We show that although these access technologies promise a broadband pipe and low delay communication, a careful configuration of line parameters is paramount to achieve optimal access and is needed for the success of multimedia triple play (3P) services. We particularly analyze the delay, jitter, packet loss and bitrate consumption obtained from real ADSL measurements. These results are an important step towards understanding the adequate deployment of such services. As a special case, we study the effect of repetitive noise on SDTV.

We introduce a parallel algorithm to solve approximate and exact nearest neighbor queries on the GPU, exploiting its massively parallel processing power. Both data structure construction and nearest neighbor queries are performed on the GPU, avoiding memory copies from system memory to device memory. This algorithm achieves real-time performance, enabling its usage in dynamic scenarios, by minimizing the sorting comparisons needed for a large K value. The underlying data structure for spatial subdivision handles 3D points and is based on grid spatial hashing. Users can specify the grid size interactively. Comparisons were done with other nearest neighbor algorithms implemented on both CPU and GPU. Our approach clearly surpasses CPU implementations regarding processing time, while it presents a competitive solution to GPU ones. Real-time results were obtained with ANN searches (K = 10) for data sets up to 163K points and the potential of our algorithm is demonstrated through a point-based rendering application.

Current GPU computational power enables the execution of complex and parallel algorithms, such as Ray Tracing techniques supported by kD-trees for 3D scene rendering in real time. This work describes in detail the study and implementation of five different kD-Tree traversal algorithms using the parallel framework NVIDIA Compute Unified Device Architecture (CUDA), in order to point their pros and cons regarding adaptation capability to the chosen architecture. In addition, a new algorithm is proposed by the authors based on this analysis, aiming performance improvement. A performance analysis of the implemented techniques demonstrates that two of these algorithms, once adequately adapted to CUDA architecture, are capable of reaching speedup gains up to 15x when compared to former CPU implementations and up to 4x in comparison to existing and optimized parallel ones. As a consequence, interactive frame rates are possible for scenes with 1376x768 pixels of resolution and 1 million primitives.

This paper presents a solution for the photorealistic rendering of synthetic objects into dynamic real scenes, in Augmented Reality applications. In order to achieve this goal, an Image Based Lighting approach is proposed, where environment maps with different levels of glossiness are generated for each virtual object in the scene at every frame. Due to this, illumination effects, such as color bleeding and specular reflections, can be simulated for virtual objects in a consistent way, even under the presence of scene changes. A unifying sampling method for the spherical harmonics transformation pass is also used. It is independent of map format and does not need to apply different weights for each sample. The developed technique is combined with an extended version of Lafortune Spatial BRDF, featuring Fresnel effect and an innovative tangent rotation parameterization. The solution is evaluated in various Augmented Reality case studies, where other features like shadowing and lens effects are also exploited.

This paper presents a markerless tracking technique targeted to the Windows Mobile Pocket PC platform. The primary aim of this work is to allow the development of standalone augmented reality applications for handheld devices based on natural feature tracking of fully 3-Dimensional objects. In order to achieve this goal, a model-based tracking approach that relies on edge information was adopted. Since it does not require high processing power, it is suitable for constrained devices such as handhelds. The OpenGL ES graphics library was used to detect the visible edges in a given frame, taking advantage of graphics hardware acceleration when available. In addition, a subset of two computer vision libraries was ported to the Pocket PC platform in order to provide some required algorithms to the markerless mobile solution. They were also adapted to use fixed-point math, with the purpose of improving the overall performance of the routines. The port of these libraries opens up the possibility of having other computer-vision tasks being executed on mobile platforms. An augmented reality application was created using the implemented technique and evaluations were done regarding tracking performance, accuracy and robustness. In most of the tests, the frame rates obtained are suitable for handheld augmented reality and a reasonable estimation of the object pose was provided.

Single processor technology has been evolving across last decades, but due to physical limitations of chip manufacturing process, the industry is pursuing alternatives to sustain computational power growth,including the creation of multi-core systems. Parallel computing targets problems that are scalable and possibly distributed, dividing the problem into smaller pieces. This approach may be explored to satisfy real-time constraints required by real time physics simulation. This work introduces an optimal solution to implement accurate simulation of deformable bodies in real time. This is accomplished through the use of a meshless simulation technique, known as Point Based Animation, implemented using a parallel framework:the NVIDIA Compute Unified Device Architecture(CUDA). Our implementation showed significative improvements on performance in comparison to the former CPU one: impressive speedups around 20times could be achieved in the simulation of deformable bodies with 575 physical elements (phyxels) and53,504 surface elements (surfels).

The problem of access selection (AS) for multi-access networks has for long been addressed by both the standardization and research communities. As a result, a number of papers have proposed efficient AS algorithms that can take into account radio resource efficiency, overall capacity and quality of service (QoS) requirements in a multi-service environment. However, only a few works have developed on-line AS algorithms that do not require a priori knowledge of the traffic mix when delay sensitive (e.g. voice) and best effort (data) applications are supported. In this work, we present an online AS algorithm that performs well in a multiaccess network supporting two service classes and specifically takes into account the elastic nature of data applications. We model AS as a binpacking problem and realize that the problem is NP-complete. Therefore, we develop a heuristic algorithm called LessDamage that calculates a damage parameter and uses it as a metric for the allocation strategy. Simulation results show that LessDamage performs better in terms of blocking probability and elastic data throughput than available online binpacking heuristics, independently of the number of the available access technologies.

Recent years have promoted important evolution in remote sensing, specially taking into consideration quality improvements on satellites that are now capable of obtaining more detailed and accurate observation and producing stereoscopic views. Following market laws, once the level of detail increases the associated costs also rise. In order to take advantage of modest dataset acquisition costs the OpenStereo library (a free, open source, multiplatform, flexible and robust library for stereoscopy) is applied with the purpose of converting satellite images and similar types of geodata acquirers into stereoscopic views.

Digital elevation models may contain several errors, what causes uncertainty about the reliability of the data. Reliable use of elevation data requires that uncertainty associated with the data be accounted for and that the errors responsible for this uncertainty are identified, quantified and removed. Several studies have proposed assorted methods to detect and quantify, and also to remove different kinds of errors. However, these automatic procedures apply algorithms that are specialized in detecting errors with particular characteristics, producing good results only when the model contains predominantly these specific types of errors. In this context, this paper presents a methodology and a tool for enhancing digital elevation models, named DEMEditor. Visual interpretation plays an important role in this work, which exploits user's knowledge about the data in the decision-making process about areas to be enhanced in the digital elevation model. The background of the user allows the identification of any type of error, relieving the need for automatic detection algorithms that specialize in detecting errors with particular characteristics.

This paper presents a simulation environment for mobile cellular networks called CELSA, which implements a mobility model based on users, regions and time period characteristics. This environment was used to obtain quality of service (QoS) parameters of a mobile cellular network and to evaluate the performance of a non-uniform channel allocation scheme called CA-STV compared to fixed and dynamic channel allocation.

The conventional approach to reliable multicast in computer networks relies on the retransmission on demand of lost packets. Existing multicast protocols adopt a static packet retransmission scheme (unicast or multicast) to retransmit these packets often leading to performance loss due to wasting bandwidth resources. This is worse if we consider the limited bandwidth resources wireless interfaces currently offer. Therefore, careful continuous monitoring and control is fundamental in these wireless mobile environments. This paper looks at solutions to the retransmission problem while taking into consideration network topology and the number of fixed and mobile users. Precise conditions and rules for packet retransmission are set taking into account the dynamics of the network state. Finally, this algorithm has been incorporated into a reliable multicast protocol with support to mobile users, known as the reliable mobile multicast protocol (RM2), for performance analysis.

Current distributed computing is based on rigid paradigms such as client server computing, that lack run-time adaptive behaviour. The designer of such systems has to consider all possibilities during the design stage and the services that are offered remain constant until a new re-design is done. Although there have been attempts to provide different programming languages that consider programming both distributed and non distributed applications the same, the two remain however different. New paradigms are needed that consider factors such as dynamic adaptability, interaction, ability to monitor and react to the environment, mobility, network latency, partial failure and concurrency. We show that the agent based programming paradigm, agent-ware, is closer to fulfilling some of these requirements and describe the proposed Agentware Virtual Machine (AVM), a generic distributed agent-based computing environment. With the emergence of intelligent agent-based technology, a number of distributed applications are being re-engineered taking advantage of this new design methodology.