SUMMARYAutomatic garbage collection relieves programmers from the burden of managing memory themselves and several techniques have been developed that make garbage collection feasible in many situations, including real time applications or within traditional programming languages. However, optimal performance cannot always be achieved by a uniform general purpose solution. Sometimes an algorithm exhibits a predictable pattern of memory usage that could be better handled specifically, delaying as much as possible the intervention of the general purpose collector. This leads to the requirement for algorithm specific customisation of the collector strategies. We present a dynamic memory management framework which can be customised to the needs of an algorithm, while preserving the convenience of automatic collection in the normal case. The Customisable Memory Manager (CMM) organises memory in multiple heaps. Each heap is an instance of C؉؉ class which abstracts and encapsulates a particular storage discipline. The default heap for collectable objects uses the technique of mostly copying garbage collection, providing good performance and memory compaction. Customisation of the collector is achieved exploiting object orientation by defining specialised versions of the collector methods for each heap class. The object-oriented interface to the collector enables coexistence and coordination among the various collectors as well as integration with traditional code unaware of garbage collection. The CMM is implemented in C؉؉ without any special support in the language or the compiler. The techniques used in the CMM are general enough to be applicable also to other languages. The performance of the CMM is analysed and compared to other conservative collectors for C/C؉؉ in various configurations.

We report our experiences gained when integrating process analysis activities into a regional gateway of the Italian eGov platform to promote real-time process monitoring within a Service Oriented Architecture. We exploit ProM, a state-of-the-art suite providing several analysis algorithms for business processes. First, we outline our technological integration efforts, focusing on the architectural changes and implementation strategies to make ProM tools available at runtime for monitoring the gateway. Next we improve an existing performance algorithm with a new approach to deal with invisible transitions when evaluating the synchronization times of complex nets. Finally, we introduce a methodology to transform high level process notations, like BPMN, to Petri Nets in order to enable the analysis techniques and convey back their results

A uniform general purpose garbage collector may not always provide optimal performance. Sometimes an algorithm exhibits a predictable pattern of memory usage that could be exploited, delaying as much as possible the intervention of the collector. This requires a collector whose strategy can be customized to the need of an algorithm. We present a dynamic memory management framework which allows such customization, while preserving the convenience of automatic collection in the normal case. The Customizable Memory Management (CMM) organizes memory in multiple heaps, each one encapsulating a particular storage discipline. The default heap for collectable objects uses the technique of mostly copying garbage collection, providing good performance and memory compaction. Customization of the collector is achieved through object orientation by specialising the collector methods for each heap class. We describe how the CMM has been exploited in the implementation of the Buchberger algorithm, by using a special heap for temporary objects created during polynomial reduction. The solution drastically reduces the overall cost of memory allocation in the algorithm.

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