Data Grid Performance Research Articles

Objective assessment of the performance of data grid replication strategies based on distribution quality

Replication is an important issue for the data grid performance. Indeed, it has for main purposes to improve data access efficiency, provide high availability, decrease bandwidth consumption, improve fault tolerance and enhance scalability. In this regard, where to place a given replica represents an important step in a replication process. In this context, a significant number of placement strategies has been proposed in the literature. Each placement strategy produces a specific replicas distribution through the grid sites. This distribution will directly affect the performance of the grid. In this paper, we propose a new evaluation metric called the distribution quality, and denoted DisQ. This metric quantifies, at a given point in time, the quality of a given distribution of replicas through the grid nodes. DisQ helps to know beforehand that a given distribution will contribute in obtaining interesting data grid performance or it will degrade them. Using DisQ, we propose a correction for the evaluation metrics of replication strategies to make them more reliable. We use the OptorSim simulator to validate our theoretical contributions.

Replica Placement and Predictive Replica Selection Techniques to Improve the Performance of Data Grid

Data grid is a hardware and software infrastructure built on top of Internet and provides sharing and management of distributed data resources. Dynamic data replication techniques are used in data grids to enhance data sharing with reduced access latency and bandwidth consumption. Creating and placing duplicate copies of popular files in data grid is replica placement, whereas, replica selection is selecting the best location to access data for executing job in data grid. Efficient techniques with load balancing and prediction based on SVM and AHL that improve and optimize the replica placement and selection are introduced in this paper. The proposed algorithms reduce the makespan i.e. total job execution time, storage consumption and effective network usage. The proposed techniques consist of two phases. In the first phase, all the nodes in the same region are grouped together and replica is placed in the highest degree and highest frequency node in the region. During second phase, SVM & AHL are used for predicting the behavior of user applications. The jobs are executed in the grid with predicted events. Simulation results reveal that the proposed methodologies perform better in terms of makespan, storage consumption and replication count.

Improving job scheduling performance with parallel access to replicas in Data Grid environment

Data Grid has evolved to be the solution for data-intensive applications, such as High Energy Physics (HEP), astrophysics, and computational genomics. These applications usually have large input of data to be analyzed and these input data are widely replicated across Data Grid to improve the performance. The job scheduling performance on traditional computing jobs can be studied using queuing theory. However, with the addition of data transfer, the job scheduling performance is too complex to be modeled. In this research, we study the impact of data transfer on the performance of job scheduling in the Data Grid environment. We have proposed a parallel downloading system that supports replicating data fragments and parallel downloading of replicated data fragments, to improve the job scheduling performance. The performance of the parallel downloading system is compared with non-parallel downloading system, using three scheduling heuristics: Shortest Turnaround Time (STT), Least Relative Load (LRL) and Data Present (DP). Our simulation results show that the proposed parallel download approach greatly improves the Data Grid performance for all three scheduling algorithms, in terms of the geometric mean of job turnaround time. The advantage of parallel downloading system is most evident when the Data Grid has relatively low network bandwidth and relatively high computing power.