Self-Organizing Intelligent Data
PhD Research Programme
Bournemouth University, UK. 1996-2000
Information resources are currently managed using database technology. Users access information on database management servers using client applications, and server administrators control client access to the information using authentication, views, etc. Market globalisation trends are leading organisations to collaborate and share information resources for mutual benefits, but yet still, retain ownership of their individual part of the collaborating information resources. Naturally, a huge growth in the number of users of the system follows, and an exponential increase in concurrent data traffic between clients and servers is inevitable. To overcome this problem, either high bandwidth communication links need to be used, or data reallocation needs to be carried out dynamically to cope with continuous changes in information access patterns.
This work proposes a new data management model for distributed information systems that assumes no global knowledge of either data schema or network topology, and as such, it allows for the creation of flexible and scalable information systems that can inherently cope with the above requirements.
The main aim is to identify the intelligence requirements a data object may need, (how to acquire, use, and adapt knowledge), in order to be able to exhibit intelligent self-organization behaviour (partition, migrate, replicate, and/or combine) in response to dynamic changes in demand patterns and environment resource usage. In general, data will acquire and use self-organization knowledge using fuzzy logic, and adapt that knowledge (learn by experience) using genetic algorithms.
Related data objects are packaged into autonomous data sources capable of exhibiting self-organisation behaviour, the goal of which is to improve overall system's performance. By monitoring their access patterns and system resource distribution, data sources can decide to partition, migrate or replicate near their users, and/or combine with other data sources.
Using simulation, it is shown that significant performance improvements over conventional systems with no dynamic relocation can be achieved when using such a data self-organisation model. Experimental results showed that different types of self-organisation actions contribute to the improvements to varying degrees. Migration, while contributing the most under typical operational conditions, replication can contribute significantly more for systems with low levels of update queries. On the other hand, partitioning and combining, although does contribute to the performance improvements, their cost out-weigh their contribution and hence they are kept to minimum.
More importantly, collective emergent self-organisation behaviour has been observed which shows an increase in the locality of reference for data access. This directly translates into significant reductions in both data access time and network traffic volume.
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