DSTP-AN: A Distributed System for Transaction Processing Based on Data Resource Migration in ATM Networks

The dynamic migration of data resources has become a strong tool for transaction processing in broadband networks such as ATM. In this paper, a distributed system that takes advantage of data resource migration for transaction processing in ATM networks has been proposed. The proposed system provides mechanisms to select the transaction processing method, to migrate data resources in a way that reduces the time delay and message traffic in locating and accessing them. The first mechanism selects one of the two transaction processing methods: the traditional method that uses two phase commit protocol and other new method based on data resource migration. The second mechanism attempts to improve performance by making each site follow a local policy for directing requests to locate and access data resources as well as migrating them through the system. For this, a new scheme that focuses on reducing the time delay and message traffic needed to access the migratory data resources is proposed. The performance of the proposed scheme has also been evaluated and compared with one of the existing schemes by a simulation study under different system parameters such as frequency of access to the data resources, frequency of data resource migrations, scale of network, etc

ABSTRACT DISTRIBUTED LINEAR HASHING AND PARALLEL PROJECTION IN MAIN MEMORY DATABASES

This paper extends the concepts of the distributed linear hashed main memory file system with the objective of supporting higher level parallel dambase operations. The basic distributed linear hashing technique provides a high speed hash based dynamic file system on a NUMA atchitecture multi-processor system. Distributed linear hashing has been extended to include the ability to perform high speed parallel scans of the hashed file. The fast scan feature provides load balancing to compensate for uneven distributions of records and uneven processing speed among different processors. These extensions are used to implement a parallel projection capability. The performance of distributed linear hashing and parallel projection is investigated. 1

A Content-Addressable Network for Similarity Search in Metric Spaces

Because of the ongoing digital data explosion, more advanced search paradigms than the traditional exact match are needed for contentbased retrieval in huge and ever growing collections of data produced in application areas such as multimedia, molecular biology, marketing, computer-aided design and purchasing assistance. As the variety of data types is fast going towards creating a database utilized by people, the computer systems must be able to model human fundamental reasoning paradigms, which are naturally based on similarity. The ability to perceive similarities is crucial for recognition, classification, and learning, and it plays an important role in scientific discovery and creativity. Recently, the mathematical notion of metric space has become a useful abstraction of similarity and many similarity search indexes have been developed. In this thesis, we accept the metric space similarity paradigm and concentrate on the scalability issues. By exploiting computer networks and applying the Peer-to-Peer communication paradigms, we build a structured network of computers able to process similarity queries in parallel. Since no centralized entities are used, such architectures are fully scalable. Specifically, we propose a Peer-to-Peer system for similarity search in metric spaces called Metric Content-Addressable Network (MCAN) which is an extension of the well known Content-Addressable Network (CAN) used for hash lookup. A prototype implementation of MCAN was tested on real-life datasets of image features, protein symbols, and text — observed results are reported. We also compared the performance of MCAN with three other, recently proposed, distributed data structures for similarity search in metric spaces