Treatment of Rules in Individual Metadata of Flexible Contents Management

The properties of contents stored in a computer system are very wide while the data volume treated in the system becomes very large. It is important to treat each stored object in different manners to reject its properties in the data management for the large amount of stored data. To satisfy the requirement, we propose a method for the autonomous management based on ECA rules stored in metadata of the contents. We study the feasibility of treating a large number of ECA rules corresponding to the number of stored objects. Because the cost for evaluating conditions in the rules becomes dominant to the system perfornzance when the number of objects increases, we divide the conditions into two types, previously evaluable conditions and nuttime evaluable conditions, and construct a discrimination network for the previously evaluable conditions of each event to reduce the cost for processing the rules. We implement the methods in the autonomous disk system, a high functional storage system we proposed, and evaluate the eficiency of them.