Distributed Database Management Optimization of Performance

Secret sharing and erasure coding-based approaches have been used in distributed storage systems to ensure the confidentiality, integrity, and availability of critical information. To achieve performance goals in data accesses, these data fragmentation approaches can be combined with dynamic replication. In this paper, we consider data partitioning (both secret sharing and erasure coding) and dynamic replication in data grids, in which security and data access performance are critical issues. More specifically, we investigate the problem of optimal allocation of sensitive data objects that are partitioned by using secret sharing scheme or erasure coding scheme and/or replicated.

The grid topology we consider consists of two layers. In the upper layer, multiple clusters form a network topology that can be represented by a general graph. The topology within each cluster is represented by a tree graph. We decompose the share replica allocation problem into two sub problems: the Optimal Inter cluster Resident Set Problem (OIRSP) that determines which clusters need share replicas and the Optimal Intra cluster Share Allocation Problem (OISAP) that determines the number of share replicas needed in a cluster and their placements.

Existing System:

An existing problems in the fields of science, engineering, and business, which cannot be effectively dealt with using the current generation of supercomputers, in fact due to their size and complexity, these problems are often very numerically and/or data intensive and consequently require a variety of heterogeneous resources that are not available on a single machine. A number of teams have conducted experimental studies on the cooperative use of geographically distributed resources unified to act as a single powerful computer. This new approach is known by several names, such as meta computing, scalable computing, global computing, Internet computing, and more recently peer-to-peer or Grid computing.

The early efforts in Grid computing started as a project to link supercomputing sites, but have now grown far beyond their original intent. In fact, many applications can benefit from the Grid infrastructure, including collaborative engineering, data exploration, high-throughput computing, and of course distributed supercomputing. Moreover, due to the rapid growth of the Internet and Web, there has been a rising interest in Web-based distributed computing, and many projects have been started and aim to exploit the Web as an infrastructure for running coarse-grained distributed and parallel applications.

Proposed System:

We consider data partitioning (both secret sharing and erasure coding) and dynamic replication in data grids, in which security and data access performance are critical issues. More specifically, we investigate the problem of optimal allocation of sensitive data objects that are partitioned by using secret sharing scheme or erasure coding scheme and/or replicated. The topology within each cluster is represented by a tree graph. We decompose the share replica allocation problem into two sub problems: the Optimal Intercluster Resident Set Problem (OIRSP) that determines which clusters need share replicas and the Optimal Intracluster Share Allocation Problem (OISAP) that determines the number of share replicas needed in a cluster and their placements.

DATA grid is a distributed computing architecture that integrates a large number of data and computing resources into a single virtual data management system. It enables the sharing and coordinated use of data from various resources and provides various services to fit the needs of high-performance distributed and data-intensive computing. Replication techniques are frequently used to improve data availability and reduce client response time and communication cost. One major advantage of replication is performance improvement, which is achieved by moving data objects close to clients. In full replication all servers keep a complete set of the data objects.

Modules:

  • Network Module
  • Dynamic Randomization Process
  • Secure Data Share
  • Replication Data Grids

Tools Used:

Front End : JAVA