Deaf people face serious difficulties to access information. The fact is that they communicate naturally through sign languages, whereas, to most of them, the spoken languages are considered only a second language. When designed, Information and Communication Technologies (ICTs) rarely take into account the barriers that deaf people face. It is common that application developers do not hire sign languages interpreters to provide an accessible version of their app/site to deaf people. Currently, there are tools for automatic translation from sign languages to spoken languages, but, unfortunately, they are not available to third parties. To reduce these problems, it would be interesting if any automatic translation tool/service could be publicly available. This is the main goal of this work: use a preconceived machine translation from Portuguese Language to Brazilian Sign Language (LIBRAS) (named VLIBRAS) and provide Deaf Accessibility as a Service (DAaaS) publicly. The idea is to abstract inherent problems in the translation process between the Portuguese Language and LIBRAS by providing a service that performs the automatic translation of multimedia content to LIBRAS. VLIBRAS was primarily deployed as a centralized system, and this conventional architecture has some disadvantages when compared to distributed architectures. In this paper we propose two distributed architectures in order to provide a scalable service and achieve fault tolerance. For conception and availability of this service, it will be used the cloud computing paradigm to incorporate the following additional benefits: transparency, high availability, and efficient use of resources.

BACKGROUND: There is a large body of literature on research about fault management in grid computing. Despite being a well-established research area, there are no systematic studies focusing on characterizing the sorts of research that have been conducted, identifying well-explored topics as well as opportunities for further research. OBJECTIVE: This study aims at surveying the existing research on fault management in grid computing in order to identify useful approaches and opportunities for future research. METHOD: We conducted a systematic mapping study to collect, classify and analyze the research literature on fault management in grid computing indexed by the main search engines in the field. RESULTS: Our study selected and classified 257 scientific papers and was able to answer five research questions regarding the distribution of the scientific production over the time and space. CONCLUSIONS: The majority of the selected studies focus on fault tolerance, with very few efforts towards fault prevention, prediction and removal.

Background: The large computational infrastructures required to provide the on-demand services that most users are now used to are more prone to failures than any single computational device. Thus, fault management is a essential activity in the realization of the cloud computing model. Aims: This work aims at identifying well-explored topics on fault management in cloud computing as well as pin-pointing gaps in the scientific literature that may represent opportunities for further research and development in this area. Method: We conducted a systematic mapping study to collect, filter and classify scientific works in this area. The 4535 scientific papers found on major search engines were filtered and the remaining 166 papers were classified according to a taxonomy described in this work. Results: We found that IaaS is most explored in the selected studies. The main dependability functions explored were Tolerance and Removal, and the attributes were Reliability and Availability. Most papers had been classified by research type as Solution Proposal. Conclusion: This work summarizes and classifies the research effort conducted on fault management in cloud computing, providing a good starting point for further research in this area.

This work presents the framework Cloud Testing, a solution to parallelize the execution of a test suite over a distributed cloud infrastructure. The use of a cloud as runtime environment for automated software testing provides a more efficient and effective solution when compared to traditional methods regarding the exploration of diversity and heterogeneity for testing coverage. The objective of this work is evaluate our solution regarding the performance gains achieved with the use of the framework showing that it is possible to improve the software testing process with very little configuration overhead and low costs.

Grids have the potential to revolutionize computing by providing ubiquitous, on demand access to computational services and resources. However, grid systems are extremely large, complex and prone to failures. A survey we have conducted reveals that fault diagnosis is still a major problem for grid users. When a failure appears at the user screen, it becomes very difficult for the user to identify whether the problem is in his application, somewhere in the grid middleware, or even lower in the fabric that comprises the grid. To overcome this problem, we argue that current grid platforms must be augmented with a collaborative diagnosis mechanism. We propose for such mechanism to use automated tests to identify the root cause of a failure and propose the appropriate fix. We also present a Java-based implementation of the proposed mechanism, which provides a simple and flexible framework that eases the development and maintenance of the automated tests.

Software testing is a fundamental part of system development. As software grows, its test suite becomes larger and its execution time may become a problem to software developers. This is especially the case for agile methodologies, which preach a short develop/test cycle. Moreover, due to the increasing complexity of systems, there is the need to test software in a variety of environments. In this paper, we introduce GridUnit, an extension of the widely adopted JUnit testing framework, able to automatically distribute the execution of software tests on a computational grid with minimum user intervention. Experiments conducted with this solution have showed a speed-up of almost 70x, reducing the duration of the test phase of a synthetic application from 24 hours to less than 30 minutes. The solution does not require any source-code modification, hides the grid complexity from the user and provides a cost-effectiveness improvement to the software testing experience.