Summary 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.

Summary 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.

This paper presents JSensor, a parallel general purpose simulator which enables huge simulations of Wireless Sensor Networks applications. Its main advantages are: i) to have a simple API with few classes to be extended, allowing easy prototyping and validation of WSNs applications and protocols; ii) to enable transparent and reproducible simulations, regardless of the number of threads of the parallel kernel; and iii) to scale over multi-core computer architectures, allowing simulations of more realistic applications. JSensor is a parallel event-driven simulator which executes according to event timers. The simulation elements, nodes, application, and events, can send messages, process task or move around the simulated environment. The mentioned environment follows a grid structure of extensible spatial cells. The results demonstrated that JSensor scales well, precisely it achieved a speedup of 7.45 with 16 threads in a machine with 16 cores (eight physical and eight virtual cores), and comparative evaluations versus OMNeT++ showed that the presented solution could be 43 times faster.

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: [email protected].

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.

Construction of prognoses about environmental changes demands simulations of massive multi-agent models. This work evaluates the hypothesis that the combined use of techniques such as annotation and bag of tasks can result in flexible and scalable platforms for multi-agent simulation. Although these are well known techniques, most environmental modeling platforms use other approaches to provide high performance computing. In general, the approach used is dependent of the modeling paradigm theses platforms implement. We are looking for approaches that can cope with multiple modeling paradigms. To evaluate our hypothesis, the TerraME modeling platform was extended to run over SMPs (Symmetric Multiprocessors) architectures and used in real case studies. While annotation allows modelers to implement different parallelization strategies without prevent models to run over sequential architectures, the bag of tasks provides load balancing over multiprocessors. The results demonstrated that 35% of linear speedup can be obtained for models with high dependence among tasks, when 8 processors are used. Moreover, for models that have low data or control dependencies, around 90% of linear speedup can be obtained.