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Content archived on 2024-06-16

Self Management for large-scale distributed systems based on structured overlay Networks and components


The goal of SELFMAN is to make large-scale distributed applications that are self managing, by combining the strong points of component models and structured overlay networks. One of the key obstacles to deploying large-scale applications running on networks such as the Internet is the issue of management. Currently many specialized personnel are needed to keep large Internet applications running. SELFMAN will contribute to removing this obstacle, and thus enable the development of many more Internet applications. In the context of SELFMAN, we define self management along four axes: self configuration (systems configure themselves according to high-level management policies), self healing (systems automatically handle faults and repair them), self tuning (systems continuously monitor their performance and adjust their behaviour to optimize resource usage and meet service level agreements), and self protection (systems protect themselves against security attacks). SELFMAN will provide self management by combining a component model with a structured overlay network.

The component model will support dynamic configuration, the ability of part of the system to reconfigure other parts at run-time, which is the key property that underlies the self-management abilities. Basing the system on a structured overlay network enables SELFMAN to extend the abilities of the component model to large-scale distributed systems. In contrast to peer-to-peer file-sharing applications, structured overlay networks provide guarantees for efficient communication and reorganization in case of failures. Combining this with the component model, SELFMAN will build high-level self-management properties on top of these low-level properties. SELFMAN will do both foundational research and applied research: we will extend the J2EE platform for self management and we will build an advanced platform for self management. We will evaluate using industrial scenarios and trace data.

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Participants (6)