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mKernel: a manageable kernel for EJB-based systems
Proceedings of the 1st international conference on Autonomic computing and communication systems
Security Contexts in Autonomic Systems
Computational Intelligence and Security
Intelligent agents: are they feasible in Swarm-array computing?
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Achieving intelligent agents and its feasibility in swarm-array computing?
WSEAS Transactions on Computers
Enabling user authentication and authorization to support context-aware UPnP applications
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
Elements of self-adaptive systems: a decentralized architectural perspective
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Autonomic tracing of production processes with mobile and agent-based computing
Information Sciences: an International Journal
On self-adaptation in systems-of-systems
Proceedings of the First International Workshop on Software Engineering for Systems-of-Systems
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As a rapidly growing field, Autonomic Computing is a promising new approach for developing large scale distributed systems. However, while the vision of achieving self-management in computing systems is well established, the field still lacks a commonly accepted definition of ýwhatý an Autonomic Computing system is. Without a common definition to dictate the direction of development, it is not possible to know whether a system or technology is a part of Autonomic Computing, or if in fact an Autonomic Computing system has already been built. The purpose of this paper is to establish a standardised and quantitative definition of Autonomic Computing through the application of the Quality Metrics Framework described in IEEE Std 1061-1998 [1]. Through the application of this methodology, stakeholders were systematically analysed and evaluated to obtain a balanced and structured definition of Autonomic Computing. This definition allows for further development and implementation of quality metrics, which are project-specific, quantitative measurements that can be used to validate the success of future Autonomic Computing projects.