Summary form only given. Collects the abstracts for the constituents of the 2012 IEEE International Power Engineering and Optimization Conference (PEOCO2012) proceedings.

Cloud computing [23] is a promising paradigm for the provisioning of IT services. Cloud computing infrastructures, such as those offered by the RESERVOIR project, aim to facilitate the deployment, management and execution of services across multiple physical locations in a seamless manner. In order for service providers to meet their quality of service objectives, it is important to examine how software architectures can be described to take full advantage of the capabilities introduced by such platforms. When dealing with software systems involving numerous loosely coupled components, architectural constraints need to be made explicit to ensure continuous operation when allocating and migrating services from one host in the Cloud to another. In addition, the need for optimising resources and minimising over-provisioning requires service providers to control the dynamic adjustment of capacity throughout the entire service lifecycle. We discuss the implications for software architecture definitions of distributed applications that are to be deployed on Clouds. In particular, we identify novel primitives to support service elasticity, co-location and other requirements, propose language abstractions for these primitives and define their behavioural semantics precisely by establishing constraints on the relationship between architecture definitions and Cloud management infrastructures using a model denotational approach in order to derive appropriate service management cycles.

The Java-UML Lightweight Enumerator (JULE) tool implements a vitally important aspect of the framework for software tool certification- test suite generation. The framework uses UML models as the test inputs for the bounded exhaustive-testing approach. Within a size bound for the metamodel types, JULE enumerates only the set of non-isomorphic models in the form of relational structures. These models are classified into two sets- demonstration and counterexample- using Binary Decision Diagrams (BDDs). The power of JULE lies in its model enumeration and its use of a high-performance grid infrastructure. Hence, JULE efficiently generates a very small test suite while increasing the bound on the input size to the extent that is practical for certification purpose.

The integration of clusters of computers into computational grids has recently gained the attention of many computational scientists. While considerable progress has been made in building middleware and workflow tools that facilitate the sharing of compute resources, little attention has been paid to grid scheduling and load balancing techniques to reduce job waiting time. Based on a detailed analysis of usage characteristics of an existing grid that involves a large CPU cluster, we observe that grid scheduling decisions can be significantly improved if the characteristics of current usage patterns are understood and extrapolated into the future. The paper describes an architecture and an implementation for a predictive grid scheduling framework which relies on Kalman filter theory to predict future CPU resource utilisation. By way of replicated experiments we demonstrate that the prediction achieves a precision within 15-20% of the utilisation later observed and can significantly improve scheduling quality, compared to approaches that only take into account current load indicators.

Grid computing has the potential to revolutionise how small groups of simulation scientists work together to tackle new science problems. In this paper we report how the eMinerals project has developed a small scale integrated compute and data grid infrastructure - the eMinerals minigrid - and developed generic job submission tools that exploit this infrastructure and which enable the science users to also access other grid systems.