Interoperable and transparent dynamic deployment of web services for service oriented grids

Dynamic deployment of Web services is a term used frequently when describing the selection and deployment of a service to a grid host. Although current grid systems (such as Globus) provide dynamic deployment, the requirements of the service being deployed are not considered. Therefore truly dynamic deployment cannot be achieved as the services deployed are restricted to the grid system used. We present a dynamic deployment mechanism as part of self configuration in a service oriented grid environment. The dynamic deployment mechanism takes the requirements of the service into consideration, including parameters such as the operating system required to execute the service, the required software libraries, any additional required software packages, price and Quality of Service (QoS) parameters.

On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances.

Subsuming methods: finding new optimisation opportunities in object-oriented software

The majority of existing application profiling techniques ag- gregate and report performance costs by method or call- ing context. Modern large-scale object-oriented applications consist of thousands of methods with complex calling pat- terns. Consequently, when profiled, their performance costs tend to be thinly distributed across many thousands of loca- tions with few easily identifiable optimisation opportunities. However experienced performance engineers know that there are repeated patterns of method calls in the execution of an application that are induced by the libraries, design patterns and coding idioms used in the software. Automati- cally identifying and aggregating costs over these patterns of method calls allows us to identify opportunities to improve performance based on optimising these patterns. We have developed an analysis technique that is able to identify the entry point methods, which we call subsuming methods, of such patterns. Our ofiine analysis runs over previously collected runtime performance data structured in a calling context tree, such as produced by a large number of existing commercial and open source profilers. We have evaluated our approach on the DaCapo bench- mark suite, showing that our analysis significantly reduces the size and complexity of the runtime performance data set, facilitating its comprehension and interpretation. We also demonstrate, with a collection of case studies, that our analysis identifies new optimisation opportunities that can lead to significant performance improvements (from 20% to over 50% improvement in our case studies).