DYFRAM: dynamic fragmentation and replica management in distributed database systems

Abstract

In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.

Data Replication for the Distributed Database using Decision Support Systems

“…Peter A. Boncz et al [26] have proposed the P2P paradigm was a promising approach for distributed data management, particularly in scenarios where scalability was a major issue or where central authority/coordinators was not aviable solution. P2P data management had several dimensions affecting the design, the capabilities, as well as the limitations of the system.…”

The DASCOSA-DB Grid Database System

“…Tables in DASCOSA-DB may be horizontally fragmented based on the primary key, and DASCOSA-DB provides an adaptive fragmentation and replication sys-tem [10] that automatically moves data between sites as needed. In this section, the fragmentation process and then the replication of the fragments are described.…”

“…The individual features of DASCOSA-DB have been evaluated experimentally in earlier papers [8,10,18]. In this section, it is showed how the system, as a whole, scales.…”

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

“…Providing an illusion of infinite resources with increasing database workloads is an NP-Hard optimization problem where the tasks need to be scheduled optimally in order to answer the required services [1,2]. Cloud database query engines can take advantage of common tasks and efficiently manage the resources by using a well-known database optimization technique, Multiple Query Optimization (MQO) [3][4][5][6][7][8]. Although MQO requires significant search for the identification of common tasks among queries, it has been successfully applied to complex intelligently to reduce communication costs, are developed and experimentally evaluated.…”