Joubert de Castro Lima

Dynamic clustering in WiFi direct technology

WiFi Direct is a new technology supported by WiFi Alliance. Devices can establish connections using an Access Point (network leader), chosen automatically by the system. Unfortunately, there are no measurements for discovering the best device to be a network leader. In this paper, we propose a dynamic election of leaders for Wifi Direct Technology. In our approach, devices are exposing clustering service information used in the election of leaders. This way, leaders can be replaced based on any clustering strategy, such as battery, speed, direction and many others. We implement classical clustering algorithms to prove the usability of our proposal. The results demonstrate that our proposal is extensible to support cluster election algorithms.

Multidimensional cyclic graph approach: Representing a data cube without common sub-graphs

We present a new full cube computation technique and a cube storage representation approach, called the multidimensional cyclic graph (MCG) approach. The data cube relational operator has exponential complexity and therefore its materialization involves both a huge amount of memory and a substantial amount of time. Reducing the size of data cubes, without a loss of generality, thus becomes a fundamental problem. Previous approaches, such as Dwarf, Star and MDAG, have substantially reduced the cube size using graph representations. In general, they eliminate prefix redundancy and some suffix redundancy from a data cube. The MCG differs significantly from previous approaches as it completely eliminates prefix and suffix redundancies from a data cube. A data cube can be viewed as a set of sub-graphs. In general, redundant sub-graphs are quite common in a data cube, but eliminating them is a hard problem. Dwarf, Star and MDAG approaches only eliminate some specific common sub-graphs. The MCG approach efficiently eliminates all common sub-graphs from the entire cube, based on an exact sub-graph matching solution. We propose a matching function to guarantee one-to-one mapping between sub-graphs. The function is computed incrementally, in a top-down fashion, and its computation uses a minimal amount of information to generate unique results. In addition, it is computed for any measurement type: distributive, algebraic or holistic. MCG performance analysis demonstrates that MCG is 20-40% faster than Dwarf, Star and MDAG approaches when computing sparse data cubes. Dense data cubes have a small number of aggregations, so there is not enough room for runtime and memory consumption optimization, therefore the MCG approach is not useful in computing such dense cubes. The compact representation of sparse data cubes enables the MCG approach to reduce memory consumption by 70-90% when compared to the original Star approach, proposed in [33]. In the same scenarios, the improved Star approach, proposed in [34], reduces memory consumption by only 10-30%, Dwarf by 30-50% and MDAG by 40-60%, when compared to the original Star approach. The MCG is the first approach that uses an exact sub-graph matching function to reduce cube size, avoiding unnecessary aggregation, i.e. improving cube computation runtime.

JSensor: A Parallel Simulator for Wireless Sensor Networks and Distributed Systems

In this paper, we present a sensor network simulator called JSensor which executes parallel simulations for shared memory or multi-core architectures. JSensor allows synchronous and asynchronous large sensor networks simulations. In our test scenarios we consider flooding data propagation. In experiments, the application code level of concurrence on shared memory hardware is close to

Computing data cubes using exact sub-graph matching: the sequential MCG approach

90%

A middleware solution for integrating and exploring IoT and HPC capabilities: A middleware for integrating IoT and HPC

, the speed-up is nearly linear when the number of computer cores is increased, and the runtime function is nearly linear when the number of nodes is increased until five hundred thousand nodes.

A general-purpose distributed computing Java middleware: A general-purpose distributed computing Java middleware

In this paper, we present a novel full cube computation and representation approach, named MCG. In a data cube, each cuboid can be viewed as a set of sub-graphs. In general, redundant sub-graphs are quite common in a data cube, but their elimination is a hard problem as some previous cube approaches demonstrate. The MCG approach differentiates significantly from previous approaches since it efficiently eliminates all common sub-graphs from the entire cube, based on an exact sub-graph matching solution. We propose a matching function to guarantee one-to-one mapping between sub-graphs. The function is computed incrementally, in a top-down fashion, and its computation uses a minimal amount of information to generate unique results, regardless of whether we are using distributive, algebraic or holistic measures. MCG performance analysis demonstrates a similar runtime when compared to Star approach and very low memory consumption (94--98% reduction) when compared to a full cube representation.

TabPath: interactive tables for metabolic pathway analysis

Even with the considerable advances in the development of middleware solutions, there is still a substantial gap in Internet of Things (IoT) and high‐performance computing (HPC) integration. It is not possible to expose services such as processing, storage, sensing, security, context awareness, and actuating in a unified manner with the existing middleware solutions. The consequence is the utilization of several solutions with their particularities, thus requiring different skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce the gap between IoT and HPC technologies, we present the JavaCá&Lá (JCL), a middleware used to help the implementation of distributed user‐applications classified as IoT‐HPC. This ubiquity is possible because JCL incorporates (1) a single application programming interface to program different device categories; (2) the support for different programming models; (3) the interoperability of sensing, processing, storage, and actuating services; (4) the integration with MQTT technology; and (5) security, context awareness, and actions services introduced through JCL application programming interface. Experimental evaluations demonstrated that JCL scales when doing the IoT‐HPC services. Additionally, we identify that customized JCL deployments become an alternative when Java‐Android and vice‐versa code conversion is necessary. The MQTT brokers usually are faster than JCL HashMap sensing storage, but they do not perform distributed, so they cannot handle a huge amount of sensing data. Finally, a short example for monitoring moving objects exemplifies JCL facilities for IoT‐HPC development.

Custom Process to Small Business.

The middleware solutions for General‐Purpose Distributed Computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi‐cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Cá&Lá (JCL), a distributed‐shared‐memory and task‐oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single‐board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.

IoT and HPC Integration: Revision and Perspectives

Motivation: Information about metabolic pathways in a comparative context is one of the most powerful tool to help the understanding of genome‐based differences in phenotypes among organisms. Although several platforms exist that provide a wealth of information on metabolic pathways of diverse organisms, the comparison among organisms using metabolic pathways is still a difficult task. Results: We present TabPath (Tables for Metabolic Pathway), a web‐based tool to facilitate comparison of metabolic pathways in genomes based on KEGG. From a selection of pathways and genomes of interest on the menu, TabPath generates user‐friendly tables that facilitate analysis of variations in metabolism among the selected organisms. Availability and implementation: TabPath is available at http://200.239.132.160:8686. Contact: lmmorei@gmail.com

A Hybrid Memory Data Cube Approach for High Dimension Relations

In this work we customized the RUP process with Scrum practices, and proposed a differentiate traceability matrix, applying in a small company. The experimental results show that our customization can be adopted as an alternative to a systematic and less-intrusive process. Keywords— Software Development Process, Scrum, RUP