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This paper presents a project of the LG Sangnam Library that made real the concept of a ubiquitous library. The project intends to provide a service so that the print-disabled can take advantage of the library system and information services without complicated procedures in connections and user certifications by using NFC (Near Field Communication) technology-applied mobile phones. This model is composed of three scenarios. The first scenario offers a Digital Talking Book (DAISY) over the Internet. A user with a NFC reader-equipped mobile phone simply touches a dongle that is connected to a computer and enables the computer to conduct NFC and Bluetooth communications. The second scenario uses a wireless connection to the Internet via a mobile phone and the final scenario uses voice services via a telephone to access the library services. In the library services, users can listen to Digital Talking Books in real time. As shown in the scenarios, the purpose of the project is to offer a comfortable information environment for the print-disabled, including the blind and physically handicapped, by using ubiquitous technology.

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process` execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn`t consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

This paper proposes the TMO model-based monitoring structure (TMS) for monitoring TMO model-based real-time systems. The monitoring infrastructure configured through TMS is managed by the middleware layer, allowing for automatic monitoring and ease of deployment. In addition, since TMS is designed to utilize proven distributed capabilities enabled by the TMO model, it allows for stable, distributed monitoring on the TMO systems. As the results of experiments indicate, TMS instrumentation overhead on the execution of middleware threads and TMO methods does not exceed 1ms. This means TMS has little or no effect on the operation of the middleware and TMO methods. As such, TMS is a suitable structure for monitoring TMO systems in a stable manner.

This paper proposes MMS (TMO model-based Middleware Monitoring Structure), a structure for monitoring the middleware. MMS can be an infrastructure to check the performance and stability of the middleware, and to guarantee the reliable operation of the TMO applications by managing the operational status of the middleware threads. Moreover, this paper implements a middleware monitor as a supporting tool for developers to monitor the MMS-instrumented middleware. Thus, developer can manage the real-time system more stably, using the MMS-instrumented middleware and the middleware monitor proposed in this paper.