Adaptive algorithms for managing a distributed data processing workload
IBM Systems Journal
Evaluating multiple attribute items using queries
Proceedings of the 3rd ACM conference on Electronic Commerce
The Vision of Autonomic Computing
Computer
A Model-Driven Transformation Method
EDOC '03 Proceedings of the 7th International Conference on Enterprise Distributed Object Computing
An Artificial Intelligence Perspective on Autonomic Computing Policies
POLICY '04 Proceedings of the Fifth IEEE International Workshop on Policies for Distributed Systems and Networks
Research challenges of autonomic computing
Proceedings of the 27th international conference on Software engineering
IBM Journal of Research and Development
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Dynamic Black-Box Performance Model Estimation for Self-Tuning Regulators
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Automated and Adaptive Threshold Setting: Enabling Technology for Autonomy and Self-Management
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Performance management for cluster-based web services
IEEE Journal on Selected Areas in Communications
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On Demand Computing is a popular vision of the future in which businesses will respond nimbly to new opportunities and threats. Unfortunately, the ever-growing complexity of IT is a key inhibitor of this vision, as it raises the cost and risk of altering systems, rendering them ever more ponderous. Since the purpose of autonomic computing is to reverse the trend of increasing IT complexity, it is a critically important enabler for On Demand Computing, or businessdriven IT. In this paper, we situate autonomic computing within the broader context of business-driven IT, and use the resulting picture to motivate and discuss a research agenda that we and our colleagues at IBM have begun to pursue.