With 11 invited submissions from leading researchers and teams of researchers sharing one common characteristic ? all have worked with Dr. Judith Bishop during her long and continuing career as a leader in computer science education and research ? this book reflects on Dr Bishop's outstandingcontribution to computer science. Havingworked at three different universities she now holds a leadership position in the research division of a major software company.

The topics covered reflect some of the transitions in her career. The dominant theme is programming languages, with chapters on object oriented programming, real-time programming, component programming and design patterns. Another major and related topic is compilers, with contributions on dataflow analysis, tree rewriting and keyword recognition. Finally, there are some additional chapters on other varied but highly interesting topics including smart homes, mobile systems and teaching computer science."

With 11 invited submissions from leading researchers and teams of researchers sharing one common characteristic ? all have worked with Dr. Judith Bishop during her long and continuing career as a leader in computer science education and research ? this book reflects on Dr Bishop's outstandingcontribution to computer science. Havingworked at three different universities she now holds a leadership position in the research division of a major software company.

The topics covered reflect some of the transitions in her career. The dominant theme is programming languages, with chapters on object oriented programming, real-time programming, component programming and design patterns. Another major and related topic is compilers, with contributions on dataflow analysis, tree rewriting and keyword recognition. Finally, there are some additional chapters on other varied but highly interesting topics including smart homes, mobile systems and teaching computer science."

As computer systems evolve, the volume of data to be processed increases significantly, either as a consequence of the expanding amount of available information, or due to the possibility of performing highly complex operations that were not feasible in the past. Nevertheless, tasks that depend on the manipulation of large amounts of information are still performed at large computational cost, i.e., either the processing time will be large, or they will require intensive use of computer resources. In this scenario, the efficient use of available computational resources is paramount, and creates a demand for systems that can optimize the use of resources in relation to the amount of data to be processed. This problem becomes increasingly critical when the volume of information to be processed is variable, i.e., there is a seasonal variation of demand. Such demand variations are caused by a variety of factors, such as an unanticipated burst of client requests, a time-critical simulation, or high volumes of simultaneous video uploads, e.g. as a consequence of a public contest. In these cases, there are moments when the demand is very low (resources are almost idle) while, conversely, at other moments, the processing demand exceeds the resources capacity. Moreover, from an economical perspective, seasonal demands do not justify a massive investment in infrastructure, just to provide enough computing power for peak situations. In this light, the ability to build adaptive systems, capable of using on demand resources provided by Cloud Computing infrastructures is very attractive.