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DObjects : A Metacomputing Framework with Dynamic Query Processing for Distributed Data Networks

Many contemporary applications rely heavily on large scale distributed and heterogeneous data sources; examples are enterprise end-system management, workflow management, and computer-supported collaborative work. The key constraints for building a distributed data query infrastructure for such applications are: 1) scalability: the system needs to scale both for number of data sources and number of clients without sacrificing data integrity, 2) heterogeneity: the system needs to provide a unified access for heterogeneous data sources, and 3) network and resource dynamics: query processing needs to adapt to dynamic network and resource conditions to maximize resources utilization and scalability. To address these issues, we designed and developed DObjects, a general-purpose distributed data objects framework that provides an easy and scalable way of querying and operating data from heterogeneous data sources. We describe our novel architecture built on top of a metacomputing platform. By hiding the details of resource sharing and using object/relational mapping, DObjects simplifies the development of efficient and scalable integrated distributed data networks. In addition, we present the details of the dynamic query execution engine within our metacomputing framework that dynamically adapts to network and node conditions. We present an extensive performance evaluation through simulations as well as a real implementation.