Data Grids are an important branch of gird computing which provide mechanisms for the management of large volumes of distributed Data. Energy efficiency has recently emerged as a hot topic in large distributed systems. The development of computing systems is traditionally focused on performance improvements driven by the demand of client’s applications in scientific and business domains. High energy consumption in computer systems leads to their limited performance because of the increased consumption of carbon dioxide and amount of electricity bills. Thus, the goal of design of computer systems has been shifted to power and energy efficiency. Data Grids can solve large scale applications that require a large amount of Data. Data replication is a common solution to improve availability and file access time in such environments. This solution replicates the Data file in many different sites. In this paper, a new Data replication method is proposed that is not only Data aware, but also is energy efficient. Simulation results with CLOUDSIM show that the proposed method gives better energy consumption, average response time, and network usage than other algorithms and prevents the unnecessary creation of replica, which leads to efficient storage usage.

In Data Grid, using reservation is accepted to provide scheduling and service quality.Users need to have an access to the stored Data in geographical environment, which can be solved by using replication, and an action taken to reach certainty. As a result, users are directed toward the nearest version to access information. The most important point is to know in which sites and distributed system the produced versions are located. By selecting a suitable place for versions, the versions having performance, efficiency and lower access time are used. In this study, an efficient method is presented to select the best place for those versions created in Data Grid by using the users’ firefly algorithm which is compared with two algorithms. Results show that firefly algorithm has better performance than others.

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.

The Data Grid technology, which uses the scale of the Internet to solve storage limitation for the huge amount of Data, has become one of the hot research topics. Recently, Data replication strategies have been widely employed in a distributed environment to copy frequently accessed Data in suitable sites. The primary purposes are shortening distances of the file transmission and achieving files from nearby locations to requested sites so as to minimize retrieval time and bandwidth usage. In this paper, we propose a new replica selection strategy which is based on response time and security. However, replication should be used wisely because the storage size of each Data Grid site is limited. In addition, we propose a new replica replacement strategy that considers file availability, time of access, access frequency and size of file. The simulation results report that the proposed strategy can effectively improve mean job time, bandwidth consumption for Data delivery, and Data availability compared with those of the tested algorithms.