Multi-level Autonomic Business Process Management

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38484-4_14Nowadays, business processes are becoming increasingly complex and heterogeneous. Autonomic Computing principles can reduce this complexity by autonomously managing the software systems and the running processes, their states and evolution. Business Processes that are able to be self-managed are referred to as Autonomic Business Processes (ABP). However, a key challenge is to keep the models of such ABP understandable and expressive in increasingly complex scenarios. This paper discusses the design aspects of an autonomic business process management system able to self-manage processes based on operational adaptation. The goal is to minimize human intervention during the process definition and execution phases. This novel approach, named MABUP, provides four well-defined levels of abstraction to express business and operational knowledge and to guide the management activity; namely, Organizational Level, Technological Level, Operational Level and Service Level. A real example is used to illustrate our proposal.Research supported by CAPES, CNPQ and Spanish Ministry of Science and Innovation.Oliveira, K.; Castro, J.; España Cubillo, S.; Pastor López, O. (2013). Multi-level Autonomic Business Process Management. En Enterprise, Business-Process and Information Systems Modeling. Springer. 184-198. doi:10.1007/978-3-642-38484-4_14S184198España, S., González, A., Pastor, Ó.: Communication Analysis: A Requirements Engineering Method for Information Systems. In: van Eck, P., Gordijn, J., Wieringa, R. (eds.) CAiSE 2009. LNCS, vol. 5565, pp. 530–545. Springer, Heidelberg (2009)Ganek, A.G., Corbi, T.A.: The dawning of the autonomic computing era. IBM Systems Journal 42(1), 5–18 (2003)Gonzalez, A., et al.: Unity criteria for Business Process Modelling. In: Third International Conference on Research Challenges in Information Science, RCIS 2009, pp. 155–164 (2009)Greenwood, D., Rimassa, G.: Autonomic Goal-Oriented Business Process Management. Management, 43 (2007)Haupt, T., et al.: Autonomic execution of computational workflows. In: 2011 Federated Conference on Computer Science and Information Systems, FedCSIS, pp. 965–972 (2011)Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE (2003)Lee, K., et al.: Workflow adaptation as an autonomic computing problem. In: Proceedings of the 2nd Workshop on Workflows in Support of Large-Scale Science, New York, NY, USA, pp. 29–34 (2007)Mosincat, A., Binder, W.: Transparent Runtime Adaptability for BPEL Processes. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 241–255. Springer, Heidelberg (2008)Oliveira, K., et al.: Towards Autonomic Business Process Models. In: International Conference on Software Engineering and Knowledge, SEKE 2012, San Francisco, California, USA (2012)Rahman, M., et al.: A taxonomy and survey on autonomic management of applications in grid computing environments. Concurr. Comput.: Pract. Exper. 23(16), 1990–2019 (2011)Reijers, H.A., Mendling, J.: Modularity in process models: Review and effects. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 20–35. Springer, Heidelberg (2008)Rodrigues Nt., J.A., Monteiro Jr., P.C.L., de O. Sampaio, J., de Souza, J.M., Zimbrão, G.: Autonomic Business Processes Scalable Architecture. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 78–83. Springer, Heidelberg (2008)Strohmaier, M., Yu, E.: Towards autonomic workflow management systems. ACM Press (2006)Terres, L.D., et al.: Selection of Business Process for Autonomic Automation. In: 2010 14th IEEE International Enterprise Distributed Object Computing Conference, pp. 237–246 (October 2010)Tretola, G., Zimeo, E.: Autonomic internet-scale workflows. In: Proceedings of the 3rd International Workshop on Monitoring, Adaptation and Beyond, New York, NY, USA, pp. 48–56 (2010)Vedam, H., Venkatasubramanian, V.: A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis. In: Proceedings of the 1997 American Control Conference, vol. 1, pp. 309–313 (June 1997)Wang, Y., Mylopoulos, J.: Self-Repair through Reconfiguration: A Requirements Engineering Approach. In: 2009 IEEE/ACM International Conference on Automated Software Engineering, pp. 257–268 (November 2009)Yu, T., Lin, K.: Adaptive algorithms for finding replacement services in autonomic distributed business processes. In: Proceedings Autonomous Decentralized Systems, ISADS 2005, pp. 427–434 (2005

Semantic-based policy engineering for autonomic systems

This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

Exploring adaptation & self-adaptation in autonomic computing systems

This panel paper sets out to discuss what self-adaptation means, and to explore the extent to which current autonomic systems exhibit truly self-adaptive behaviour. Many of the currently cited examples are clearly adaptive, but debate remains as to what extent they are simply following prescribed adaptation rules within preset bounds, and to what extent they have the ability to truly learn new behaviour. Is there a standard test that can be applied to differentiate? Is adaptive behaviour sufficient anyway? Other autonomic computing issues are also discussed

Autonomic care platform for optimizing query performance

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security