The emergence of cloud computing has created a shift in the service lifecycle, a shift meant to meet the requirements of cloud environments. Anticipating this, cloud governance, a step forward from SOA governance, tries to consolidate an environment where collaboration between various enterprises can be easily achieved. Cloud governance lays the groundwork for enabling easy application development and deployment by providing critical services that range from service management to security, monitoring and audit. As means of support for cloud computing, several ontologies have tried to bridge gaps and provide interoperability. None, however, have taken the time to analyze and define aspects that relate to service lifecycle and cloud governance. In this regard, our article proposes an ontology that is meant to complement existing ones.

Improving energy efficiency in order to reduce CO ₂ emissions is a permanent challenge in the European space. Smart metering could help for improving energy efficiency by offering information about the way in which the energy is used. Smart metering will be based on large volumes of sensor data, since energy monitoring will bring together sensor data from various critical areas. The main purpose of this paper was to present the selection mechanism for a scalable storage solution, based on the requirements of the DEHEMS (Digital Environment Home Energy Management System) project. With regular sensor readings coming at every 6 seconds, there is an impressive amount of data collected even for the minimal target of about 250 households, 10 ?sensors per user. With these huge data streams that are non-stationary time-series data, collected at discrete intervals, the DEHEMS project has to offer a solution for storing and retrieving sensor data in a ?responsive way. We have tested both collection speed and aggregation speed for reasonable data streams of sensor data. The tests were performed on various database models, with their associated ?representations, ?including relational databases, key-value stores, column stores, self-tuning databases, as well as time-series enabled database systems. These experiments confirmed that column stores and key-value stores perform better than relational databases, while time-series databases outperform all the others.

Recent migration towards the cloud has lead to boom of initiatives that address key issues related to it. One of these issues, integrating various available cloud services while providing automated lifecycle management, was addressed by cloud governance. However, while mostly until now computers and phones were linked to the internet, new devices like tablets, sensors, smart devices (embedded devices) are now making their way into this environment. This, in turn, raises questions related to managing, governing these devices. Similarly to how cloud governance extends SOA governance, this article will detail the way in which IoT governance extends cloud governance.

With the current prevalence of Ubiquitous Computing, more specifically Internet of Things, almost every other appliance aims to exhibit smart behavior and expose it through internet connectivity, resulting in a plethora of sensors that expose environmental information online. As a direct result, large quantities of data are pouring online waiting to be processed. Our paper addresses this problem through an architecture which handles storage, analysis and processing of large amounts of data and can scale accordingly, providing a corner stone for Internet of Things.