Danilo Ardagna – författare

The complexity of Information Technology (IT) systems has been steadily incre- ing in the past decades. In October 2001, IBM released the “Autonomic Computing Manifesto” observing that current applications have reached the size of millions of lines of code, while physical infrastructures include thousands of heterogeneous servers requiring skilled IT professionals to install, con?gure, tune, and maintain. System complexity has been recognized as the main obstacle to the further advan- ment of IT technology. The basic idea of Autonomic Computing is to develop IT systems that are able to manage themselves, as the human autonomic nervous system governs basic body functions such as heart rate or body temperature, thus freeing the conscious brain— IT administrators—from the burden of dealing with low-level vital functions. Autonomic Computing systems can be implemented by introducing autonomic controllers which continuously monitor, analyze, plan, and execute (the famous MAPE cycle) recon?guration actions on the system components. Monitoring acti- ties are deployed to measure the workload and performance metrics of each running component so as to identify system faults. The goal of the analysis activities is to determine the status of components from the monitoring data, and to forecast - ture conditions based on historical observations. Finally, plan and execute activities aim at deciding and actuating the next system con?guration, for example, deciding whether to accept or reject new requests, determining the best application to servers assignment, in order to the achieve the self-optimization goals.

The complexity of Information Technology (IT) systems has been steadily incre- ing in the past decades. In October 2001, IBM released the “Autonomic Computing Manifesto” observing that current applications have reached the size of millions of lines of code, while physical infrastructures include thousands of heterogeneous servers requiring skilled IT professionals to install, con?gure, tune, and maintain. System complexity has been recognized as the main obstacle to the further advan- ment of IT technology. The basic idea of Autonomic Computing is to develop IT systems that are able to manage themselves, as the human autonomic nervous system governs basic body functions such as heart rate or body temperature, thus freeing the conscious brain— IT administrators—from the burden of dealing with low-level vital functions. Autonomic Computing systems can be implemented by introducing autonomic controllers which continuously monitor, analyze, plan, and execute (the famous MAPE cycle) recon?guration actions on the system components. Monitoring acti- ties are deployed to measure the workload and performance metrics of each running component so as to identify system faults. The goal of the analysis activities is to determine the status of components from the monitoring data, and to forecast - ture conditions based on historical observations. Finally, plan and execute activities aim at deciding and actuating the next system con?guration, for example, deciding whether to accept or reject new requests, determining the best application to servers assignment, in order to the achieve the self-optimization goals.

Theseproceedingscontainthe?nalversionsofpapersacceptedfortheworkshops that were held in conjunction with the 6th International Conference on Bu- ness Process Management (BPM 2008) that took place in Milan, Italy. Thirteen workshop proposals were submitted for this conference, nine of which were - lected, and ultimately they ran concurrentlyon September 1,2008.This wasthe fourth year running for BPM workshops, a testament to the continued success of the workshop program. This year the workshops included some new emerging areas: Business Process Management and Social Softwarefocusedonthe- teraction of social software and the underlying paradigm of social prod- tion with business processes, by exploring how social software and social production interact with business process management, how business p- cess management has to change to comply with social production, and how business processes may pro?t from social techniques.Model-Driven Engineering for Business Process Management was about the application of model-driven engineering to business process m- agement,byfocusing on researchproblems that arisewhen the model-driven engineering and development methodology is applied to automate the whole lifecycle of business process modeling artifacts (e.g., automatically mapping high-level business process models to executable IT-level work?ows). Process Management for Highly Dynamic and Pervasive Scenarios recognized how nowadays process management systems are also being used in pervasive and highly dynamic situations, such as emergency management, pervasive healthcare and ambient intelligence, thus requiring novel approaches merging traditional BPM with arti?cial intelligence, agent programming and robotics.