Due to the results of the experiment we
Due to the results of the experiment we decided to deploy our cloud service infrastructure with two platform-independent servers instead of one or three. This decision was based on two factors: (a) the use of more than one server is useful to avoid a bottleneck in the system, and (b) using more than two servers takes longer to choose the target server and affects performance. Fig. 19c shows that the improvement in results using three servers is the same for “max” times, but worse for “min” times.
We measured the communication response times with different numbers of components and with different coupling. For practical reasons, we did not perform the test with low architecture coupling, because there are very few connections enabling communication. In addition, we only performed the test for GUIs with 3 to 20 components, as these scenarios provided sufficient connections to evaluate the process. Fig. 19d shows the results of the experiment. The response times for the communication remain under 100ms for “medium” coupling and less than 140ms for “high” coupling. These times grow in proportion to the number of components, but on a very low gradient. We can therefore assert that communication is executed within a suitable length of time.
Related work The use of cloud-based computing, as previously described in , offers a number of advantages for both users and organizations who want to make better use of the resources they manage. Among the benefits identified, that study refers to the use of SaaS, and specifically of MaaS as a software Brefeldin A with a high level of abstraction which systems can use at any time, for example, to build software “top-down” from an approximation. The MaaS concept as an on-demand decision element that could be used by a software system was introduced in  and has had numerous applications since then. For example, in , the authors make use of this concept to provide data and decision analysis in model form from expert knowledge and an automatic modeling system. In  benefits that can be obtained from the MaaS concept and the use of models with cloud computing are also identified. In this work, the authors highlight aspects such as the availability of these models, run-time sharing, improved scalability and distribution, possible implementation, adaptation and evolution of these models or even building mashups, such as a combination of MDE services offered by different “vendors”. The MaaS concept was also used in  to identify ecological models represented and stored by a web system, for customers who would like to share this information for management and decision-making. Other authors , have demonstrated its potential uses, the challenges to be addressed and provided a case study of its use for a task which analyzed risk of oil spills. Other works  propose a definition of web service interface and a specification for data exchange to describe and offer “water resources” as web services. Other projects for enhancing interoperability of models which represent geospatial information and its access have used web services, and attempted to make these services increasingly reusable . Our proposal also uses this mechanism for accessing models with services. However, although we also use geospatial information as a domain, because our research is linked to a regional project called ENvironmental Information Agent , the models which we access through the cloud represent the structure of widget-based GUIs and it is precisely these medium or high-granularity graphical components which are reused in our system. Therefore, the domain could be any other application using a graphical interface in which components may be developed as widgets. Looking to the future, we believe that our proposal could be extended to other domains in which our COTSgets do not represent GUI widgets, but rather are used to describe software architectures based on components from other areas, such as home automation , robotics , communication network infrastructures , etc.