Abstract: A Data Grid connects a collection of geographically distributed computational and storage resources that enables users to share data and other resources. Data replication, a technique much discussed by Data Grid researchers in recent years creates multiple copies of file and places them in various locations to shorten file access times. In this paper, a dynamic data replication strategy, called Modified Dynamic Hierarchical Replication (MDHR) is proposed. This strategy is an enhanced version of Dynamic Hierarchical Replication (DHR). However, replication should be used wisely because the storage capacity of each Grid site is limited. Thus, it is important to design an effective strategy for the replication replacement task. MDHR replaces replicas based on the last time the replica was requested, number of access, and size of replica. It selects the best replica location from among the many replicas based on response time that can be determined by considering the data transfer time, the storage access latency, the replica requests that waiting in the storage queue, the distance between nodes and CPU process capability. Simulation results utilizing the OptorSim show MDHR achieves better performance overall than other strategies in terms of job execution time, effective network usage and storage usage.

Data grids support access to widely distributed storage for large numbers of users accessing potentially many files. To enhance access time, replication at nearby sites may be used. Data replication, a technique much investigated by data grid researchers in past years creates multiple replicas of file and places them in conventional locations to shorten file access times. One of the problems in data replication is creation of replicas, replica placement and replica selection. Dynamic creation of replicas in an appropriate site by data replication strategy can increase the systems performance. In this paper, we propose a data replication algorithm, called the Utility-base Data Replication (UDR) algorithm that improves file access time. Each grid site has its own capabilities and characteristics; therefore, choosing one specific site from many sites that have the needed data is a key and significant decision. The replica selection problem has been studied by many researchers who only considered response time as a criterion for the selection process. Therefore, in this study, we addressed the problem of how to select the best replica for the users' jobs. Our approach is simulated using a data grid simulator, OptorSim, developed by European Data Grid projects. Comparing to the previous work the experimentation shows the improvement in the overall performance.

Data Grid is an infrastructure that controls huge amount of data files, and provides intensive computational resources across geographically distributed collaboration. The heterogeneity and geographic dispersion of grid resources and applications place some complex problems such as job scheduling. Most existing scheduling algorithms in Grids only focus on one kind of Grid jobs which can be data-intensive or computation-intensive. However, only considering one kind of jobs in scheduling does not result in suitable scheduling in the viewpoint of all systems, and sometimes causes wasting of resources on the other side. To address the challenge of simultaneously considering both kinds of jobs, a new Integrated Job Scheduling Strategy (IJSS) is proposed in this paper. At one hand, the IJSS algorithm considers both data and computational resource availability of the network, and on the other hand, considering the corresponding requirements of each job, it determines a value called W to the job. Using the W value, the importance of two aspects (being data or computation intensive) for each job is determined, and then the job is assigned to the available resources. The simulation results with OptorSim show that IJSS outperforms comparing to the existing algorithms mentioned in literature as number of jobs increases.