We present a comprehensive survey on parallel I/O in the high-performance computing (HPC) context. This is an important field for HPC because of the historic gap between processing power and storage latency, which causes application performance to be impaired when accessing or generating large amounts of data. As the available processing power and amount of data increase, I/O remains a central issue for the scientific community. In this survey article, we focus on a traditional I/O stack, with a POSIX parallel file system. We present background concepts everyone could benefit from. Moreover, through the comprehensive study of publications from the most important conferences and journals in a 5-year time window, we discuss the state of the art in I/O optimization approaches, access pattern extraction techniques, and performance modeling, in addition to general aspects of parallel I/O research. With this approach, we aim at identifying the general characteristics of the field and the main current and future research topics.

The growing demand for performance observed in many organisations can still be considered the main motivator for the evolution of high performance computing and, more recently, cloud environments. Part of this demand regards the need to deal with large and complex datasets, called big data. Performance improvement in such environments begins to be limited by energy consumption. Workload characterisation is a well-known approach to reproducing systems' behaviour. However, there are several methodologies, techniques and parameters that can be considered for a workload characterisation. As a result, we present a differentiated survey on workload characterisation focusing on performance and energy efficiency improvement on HPC, cloud and big data environments. After an extensive review and classification of research works, our study indicates that around 56.4% of the papers reviewed offer contributions to performance and energy efficiency improvement, and the growing interest in this subject has a rate of 7.86% per year.

This paper presents an approach to the evaluation of Quality of Context (QoC) parameters in a ubiquitous Ambient Assisted Living (AAL) e-Health platform, supporting the care of people with special needs (elderly or with health problems) thus improving their quality of life. The proposal is initially verified with the Siafu simulator in an AAL scenario where the users health is monitored with information about blood pressure and body temperature. The research proceeded with the use of IoT technology, the e-Health Sensor Platform, a differentiated real environment. The experiment used the sensors: pulse and oxygen in blood, body temperature, blood pressure, patients position and falls. Just as in the simulation, and with the QoC evaluation, the completed real experiment confirmed instances of insufficient QoC and its possible causes, in addition to alerts to potential health problems. Results indicates the relevance of the QoC approach for AAL applications.