secure data objects replication in data grid
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01-03-2011, 12:09 AM


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21-05-2013, 02:26 PM

SECURE DATA OBJECTS REPLICATION IN DATA GRID


.doc   SECURE DATA OBJECTS.doc (Size: 31 KB / Downloads: 24)

ABSTRACT:

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.

INTRODUCTION:

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. Many data grid applications are being developed or proposed, such as DoD’s Global Information Grid (GIG) for both business and military domains, NASA’s Information Power Grid GMESS Health-Grid for medical services, data grids for Federal Disaster Relief, etc. These data grid applications are designed to support global collaborations that may involve large amount of information, intensive computation, real time, or non real time communication. Success of these project and implimentations can help to achieve significant advances in business, medical treatment, disaster relief, research, and military and can result in dramatic benefits to the society.
There are several important requirements for data grids, including information survivability, security, and access performance. For example, consider a first responder team responding to a fire in a building with explosive chemicals. The data grid that hosts building safety information, such as the building layout and locations of dangerous chemicals and hazard containment devices, can help draw relatively safe and effective rescue plans. Delayed accesses to these data can endanger the responders as well as increase the risk to the victims or cause severe damages to the property. At the same time, the information such as location of hazardous chemicals is highly sensitive and, if falls in the hands of terrorists, could cause severe consequences. Thus, confidentiality of the critical information should be carefully protected. The above example indicates the importance of data grids and their availability, reliability, accuracy, and responsiveness. Replication is frequently used to achieve access efficiency, availability, and information survivability. The underlying infrastructure for data grids can generally be classified into two types: cluster based and peer-to-peer Systems.

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 and implimentation 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 project and implimentations 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.
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