AUTONOMIC COMPUTING SYSTEMS UTILIZING AGENTS FOR RISK MITIGATION OF IT GOVERNANCE

Authors

  • Bokolo Anthony Jnr Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
  • Noraini Che Pa Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
  • Teh Noranis Mohd Aris Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
  • Rozi Nor Haizan Nor Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
  • Yusmadi Yah Jusoh Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.6490

Keywords:

Software autonomic computing, risk, risk mitigation agents, & IT governance

Abstract

Risk mitigation has gained relevance during the last years and has helped to solve risk and improve decision making among decision makers in IT Governance. However, there is still a increasing need of developing innovative tools that can help IT Practitioners to solve risk in IT Governance. Existing risk mitigation approaches or tools lacks need for adequate data which is very important in mitigating risk and there is difficulty of mitigating risk generally in IT Governance. This paper present an autonomic computing model developed to mitigate risk; mainly operational and technical in IT Governance by measuring the risk and providing risk report to the management and staffs in organisations. Autonomic Computing Systems (ACSs) are systems that manage themselves. The core of Autonomic Computing Systems are type of agent with advanced capacities for reasoning to measure the risk probability and risk impact based on available data in the knowledge base or previous experiences.  The Autonomic Computing Systems provide risk advice aimed at providing decision support to management hence mitigating risk in IT Governance. Data was collected via purposely sampling using interview by case study among 13 Malaysia universities. The data was analyzed by Nvivo to get an insight on the current risk mitigation practices and process, after which a risk mitigation model has been developed using autonomic agents.

References

John, D., Isaac, N. and Admire, K. 2009. Intelligent Risk Management Tools for Software Development. SACLA 2009 29 June-1 Mpekweni Beach Resort South Africa. 33-40.

Siridech, K., Corbitt, B. and Pittayachawan, S. 2008. ICT Risk Management in Organizations: Case studies in Thai Business. 19th Australasian Conference on Information System 3-5 Dec 2008 Christchurch. 513-522.

Mirela, G. 2011. Risk Management in IT Governance Framework. The Bucharest Academy of Economic Studies Romania. 14(3): 545-552.

Mohammad, R. N., Koen, B. and Stamatis V. 2007. Autonomic Computing Systems: Issues and Challenges. Computer Engineering Laboratory Faculty of Electrical Engineering, Mathematics, and Computer Science Technical University of Delft, the Netherlands. 538-543.

Javier, B., María, L. B., Juan, P., Juan M. C. and María, A. P. 2012. A Multi-Agent System for Web-based Risk Management in Small and Medium Business. Journal of Expert Systems with Applications. doi:10.1016/j.eswa.2012.01.001. 6921-6931.

Mihalis, G. and Michalis L. 2011. A Multi-agent Based Framework for Supply Chain Risk Management. Journal of Purchasing and Supply Management. doi:10.1016/j.pursup.2010.05.001. 23-31.

Masoomeh, M., Abdollah, A. and Monireh, H. 2013. Knowledge-collector Agents: Applying Intelligent Agents in Marketing Decisions with Knowledge Management Approach. Knowledge-Based Systems. 181-193.

Xianli, S., Min, H. and Xingwei, W. 2011. Web and Multi-agent Based Virtual Enterprise Risk Management System. IEEE 2011 Chinese Control and Decision Conference (CCDC). 902-906.

Khoo, Y. B., Zhou, M. and Kayis, B. 2009. An Agent-based Risk Management Tool for Concurrent Engineering Projects. Complexity International. 1-11.

Ruan, J. and Qin, S. F. 2009. A Generic Conceptual Model for Risk Analysis in a Multi-agent Based Collaborative Design Environment. Proceedings of the 19th CIRP Design Conference Competitive Design. 30-31.

Shikha, R. and Selvarani, R. 2012. An Efficient Method of Risk Assessment using Intelligent Agents. Second International Conference on Advance Computing and Communication Technologies. IEEE. 123-126.

Pratim, D. and William, A. 2010. Software and Human Agents in Knowledge Codification. Knowledge Management Research and Practice. Operational Research Society. 45-60.

Davide, A., Dulmin, R. and Mininno, V. 2012. Risk Assessment in ERP Projects. Information Systems. doi:10.1016/j.is.2011.10.00. 183-199.

Kayis, B., Zhou, M., Savci, S., Khoo, Y. B., Ahmed, A., Kusumo, R. and Rispler. A. 2007. IRMAS–development of a Risk Management Tool for Collaborative Multi-Site, Multi-Partner New Product Development Projects. Journal of Manufacturing Technology Management. 18(4): 387-414.

Georgakarakou, C. E. and Economides, A. A. 2006. Software Agent Technology: An Overview. Agent and Web Service Technologies in Virtual Enterprises, N. Protogeros (ed.), Idea Group Publication. 1-21.

Downloads

Published

2015-11-26

How to Cite

AUTONOMIC COMPUTING SYSTEMS UTILIZING AGENTS FOR RISK MITIGATION OF IT GOVERNANCE. (2015). Jurnal Teknologi (Sciences & Engineering), 77(18). https://doi.org/10.11113/jt.v77.6490