Júlio Cezar Estrella

Energy-Efficient Location and Activity-Aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data have become easier than ever. However, large-scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g., pay-as-you-go infrastructure). Further, not everyone is interested in such large-scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called context-aware mobile sensor data engine (C-MOSDEN) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and are used to highlight the advantages of context-aware selective sensing.

Performance and Dependability in Service Computing: Concepts, Techniques and Research Directions

Performance and Dependability in Service Computing: Concepts, Techniques and Research Directions highlights current technological trends and related research issues in dedicated chapters without restricting their scope. This book focuses on performance and dependability issues associated with service computing and these two complementary aspects, which include concerns of quality of service (QoS), real-time constraints, security, reliability and other important requirements when it comes to integrating services into real-world business processes and critical applications.

Fog Computing for Sustainable Smart Cities: A Survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

Real-time compression of SOAP messages in a SOA environment

This paper discusses how the use of compression techniques aimed at decreasing data transfer times over a communication network can influence the response time of an application that process SOAP messages in the context of a service-oriented architecture. Following an overview of the most known object models and comparing some of their features, the article presents an heuristic that can be used to decide whether a soap message either should or should not be compressed. A simulated experiment shows that the proposed heuristic can help in reducing the service response time in a variety of scenarios.

Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure

In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it being ready for using. This causes the reactive techniques, which request a new resource only when the system reaches a certain load threshold, to be not suitable for the resource allocation process. To address this problem, it is necessary to predict requests that arrive at the system in the next period of time to allocate the necessary resources, before the system becomes overloaded. There are several time series forecasting models to calculate the workload predictions based on history of monitoring data. However, it is difficult to know which is the best time series forecasting model to be used in each case. The work becomes even more complicated when the user does not have much historical data to be analyzed. Most related work considers only single methods to evaluate the results of the forecast. Other works propose an approach that selects suitable forecasting methods for a given context. But in this case, it is necessary to have a significant amount of data to train the classifier. Moreover, the best solution may not be a specific model, but rather a combination of models. In this paper we propose an adaptive prediction method using genetic algorithms to combine time series forecasting models. Our method does not require a previous phase of training, because it constantly adapts the extent to which the data are coming. To evaluate our proposal, we use three logs extracted from real Web servers. The results show that our proposal often brings the best result and is generic enough to adapt to various types of time series.

Performance Evaluation of Resource Management in Cloud Computing Environments.

Cloud computing is a computational model in which resource providers can offer on-demand services to clients in a transparent way. However, to be able to guarantee quality of service without limiting the number of accepted requests, providers must be able to dynamically manage the available resources so that they can be optimized. This dynamic resource management is not a trivial task, since it involves meeting several challenges related to workload modeling, virtualization, performance modeling, deployment and monitoring of applications on virtualized resources. This paper carries out a performance evaluation of a module for resource management in a cloud environment that includes handling available resources during execution time and ensuring the quality of service defined in the service level agreement. An analysis was conducted of different resource configurations to define which dimension of resource scaling has a real influence on client requests. The results were used to model and implement a simulated cloud system, in which the allocated resource can be changed on-the-fly, with a corresponding change in price. In this way, the proposed module seeks to satisfy both the client by ensuring quality of service, and the provider by ensuring the best use of resources at a fair price.

A Performance Evaluation Study for Web Services Attachments

Web Services are a technology based on the Service Oriented Architecture that enables communication between applications through the Internet. Using Web Services, it is possible to send any type of information in any form of encryption. In this context, different techniques have been used to attach binary files in SOAP messages. However, there is no consensus on which technique has the best performance. This paper presents a performance evaluation study with three techniques for Web Service attachments: Pure Binary, MTOM and SwA. A testing environment was configured to verify the influence of the network and the size of files. Also, we present a tool called WSATPerf that supports the execution of the performance evaluation.

Application of SOA in Safety-Critical Embedded Systems

Service-Oriented Architecture (SOA) are having a widespread use in enterprise computing applications, being Web services the most common implementation. The use of SOA has also been proposed for embedded systems, although very little could be found in the literature on the use of SOA for Safety-Critical Embedded Systems. This paper discusses the use of SOA for the development of this class of systems. Safety-critical embedded systems have specific requirements such as high reliability and real time response, making the use of SOA more challenging than for standard applications. To make concepts clear, a case study on Avionics for Unmanned Aerial Vehicles (UAVs) is presented. This is a complex application based on a reference model proposed by the authors. SOA shows to be a promising approach to implement parts of this reference model, especially in what concerns the missions played by the aircraft.

Negotiation mechanisms on application level: a new approach to improve quality of service in Web servers

This paper describes a negotiation mechanism inserted in a Web server model with differentiated services. All the results of this article have been obtained by means of simulation using for this a queueing network model representing a Web server with QoS. The results also confirm that this new approach enhances the quality of service provided to clients, mainly in overload situations, being an aid in the process of request admission and dropping

Providing IaaS resources automatically through prediction and monitoring approaches

A cloud computing infrastructure management is proposed in this paper, which consists of two approaches that facilitate the provisioning of computing resources in a self-adaptive virtualized environment. Resource allocation is employed to predict the future of workload management and to employ a self-adaptive approach by using computational agents to monitor the Virtual Machines (VMs). The paper also includes the Return on Investment (ROI) formula that deals with the relationship between the prices for the Infrastructure-as-a-Service (IaaS) contracted by the customer and the effective use of this service. The experimental results show a significant improvement when self-configuration is used with agent-based computational modeling in contrast with the self-configuration based on prediction for future workload.