A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Replica maintenance strategy for data grid

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration.Increasing the performance of such system can be achieved by improving the overall resource usage, which includes network and storage resources.Improving network resource usage is achieved by good utilization of network bandwidth that is considered as an important factor affecting job execution time.Meanwhile, improving storage resource usage is achieved by good utilization of storage space usage. Data replication is one of the methods used to improve the performance of data access in distributed systems by replicating multiple copies of data files in the distributed sites.Having distributed the replicas to various locations, they need to be monitored.As a result of dynamic changes in the data grid environment, some of the replicas need to be relocated.In this paper we proposed a maintenance replica placement strategy termed as Unwanted Replica Deletion Strategy (URDS) as a part of Replica maintenance service.The main purpose of the proposed strategy is to find the placement of unwanted replicas to be deleted.OptorSim is used to evaluate the performance of the proposed strategy. The simulation results show that URDS requires less execution time and consumes less network usage and has a best utilization of storage space usage compared to existing approaches

Replica Creation Algorithm for Data Grids

Data grid system is a data management infrastructure that facilitates reliable access and sharing of large amount of data, storage resources, and data transfer services that can be scaled across distributed locations. This thesis presents a new replication algorithm that improves data access performance in data grids by distributing relevant data copies around the grid. The new Data Replica Creation Algorithm (DRCM) improves performance of data grid systems by reducing job execution time and making the best use of data grid resources (network bandwidth and storage space). Current algorithms focus on number of accesses in deciding which file to replicate and where to place them, which ignores resources’ capabilities. DRCM differs by considering both user and resource perspectives; strategically placing replicas at locations that provide the lowest transfer cost. The proposed algorithm uses three strategies: Replica Creation and Deletion Strategy (RCDS), Replica Placement Strategy (RPS), and Replica Replacement Strategy (RRS). DRCM was evaluated using network simulation (OptorSim) based on selected performance metrics (mean job execution time, efficient network usage, average storage usage, and computing element usage), scenarios, and topologies. Results revealed better job execution time with lower resource consumption than existing approaches. This research contributes replication strategies embodied in one algorithm that enhances data grid performance, capable of making a decision on creating or deleting more than one file during same decision. Furthermore, dependency-level-between-files criterion was utilized and integrated with the exponential growth/decay model to give an accurate file evaluation

Economy-based data replication broker

Data replication is one of the key components in data grid architecture as it enhances data access and reliability and minimises the cost of data transmission. In this paper, we address the problem of reducing the overheads of the replication mechanisms that drive the data management components of a data grid. We propose an approach that extends the resource broker with policies that factor in user quality of service as well as service costs when replicating and transferring data. A realistic model of the data grid was created to simulate and explore the performance of the proposed policy. The policy displayed an effective means of improving the performance of the grid network traffic and is indicated by the improvement of speed and cost of transfers by brokers.<br /

A dynamic replication strategy based on exponential growth/decay rate

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration.To increase resource availability and to ease resource sharing in such environment, there is a need for replication services.Data replication is one of the methods used to improve the performance of data access in distributed systems.In this paper, we include issues arising in data replication domain and also we propose a dynamic replication strategy that is based on exponential growth or decay rate. The purpose of the proposed strategy is to identify which files to be replicated.This is achieved by estimating number of accessed of a file in the upcoming time interval.The greater the value, the more popular the file is and therefore will be selected to be replicate

Preliminary specification and design documentation for software components to achieve catallaxy in computational systems

This Report is about the preliminary specifications and design documentation for software components to achieve Catallaxy in computational systems. -- Die Arbeit beschreibt die Spezifikation und das Design von Softwarekomponenten, um das Konzept der Katallaxie in Grid Systemen umzusetzen. Eine Einführung ordnet das Konzept der Katallaxie in bestehende Grid Taxonomien ein und stellt grundlegende Komponenten vor. Anschließend werden diese Komponenten auf ihre Anwendbarkeit in bestehenden Application Layer Netzwerken untersucht.Grid Computing

A dynamic replica creation: Which file to replicate?

Data Grid is an infrastructure that manages huge amount of data files and provides intensive computational resources across geographically distributed collaboration.To increase resource availability and to ease resource sharing in such environment, there is a need for replication services.Data replication is one of the methods used to improve the performance of data access in distributed systems.In this paper, we propose a dynamic replication strategy that is based on exponential growth or decay rate and dependency level of data files (EXPM).Simulation results (via Optorsim) show that EXPM outperformed LALW in the measured metrics – mean job execution time, effective network usage and average storage usage