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.

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.

High-performance computing and vast storage are two key factors required for executing data-intensive applications. In comparison with traditional distributed systems like data grid, cloud computing provides these factors in a more affordable, scalable and elastic platform. Furthermore, accessing data files is critical for performing such applications.Sometimes accessing data becomes a bottleneck for the whole cloud workflow system and decreases the performance of the system dramatically. Job scheduling and data replication are two important techniques which can enhance the performance of data-intensive applications. It is wise to integrate these techniques into one framework for achieving a single objective. In this paper, we integrate data replication and job scheduling with the aim of reducing response time by reduction of data access time in cloud computing environment.This is called data replication-based scheduling (DRBS). Simulation results show the effectiveness of our algorithm in comparison with well-known algorithms such as random and round-robin……

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.

As grow as the data-intensive applications in cloud computing day after day, data popularity in this environment becomes critical and important. Hence to improve data availability and efficient accesses to popular data, replication algorithms are now widely used in distributed systems. However, most of them only replicate the static number of replicas on some requested chosen sites and it is obviously not enough for more reasonable performance. In addition, the failure of request is one of the most common issue within the data centers. To compensate these problems, we, propose a new data replication strategy to provide cost-effective availability, minimize the response time of applications and make load balancing for cloud storage. The proposed replication strategy has three different steps which are the identification of data file to replicate, placing new replicas, and replacing replicas. In the first step, it finds the most requested files for replication. In the second step, it selects the best site by consideration of the frequency of requests for replica, the last time the replica was requested, failure probability, centrality factor and storage usage) for storing new replica to reduce access time. In the third step, the replacement decision is made in order to provide better resource usage. The proposed strategy can ascertain the importance of valuable replicas based on the number of accesses in future, the availability of the file, the last time the replica was requested, and size of replica. Our proposed algorithm evaluated by CloudSim simulator and results confirmed the better performance of hybrid replication strategy in terms of mean response time, effective network usages, replication frequency, degree of imbalance, and number of communications.

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.

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.

Large-scale data management is a critical problem in a distributed system such as cloud, P2P system, World Wide Web (WWW), and data Grid. One of the effective solutions is data replication technique, which efficiently reduces the cost of communication and improves the data reliability and response time. Various replication methods can be proposed depending on when, where, and how replicas are generated and removed. In this paper, different replication algorithms are investigated to determine which attributes are assumed in a given algorithm and which are declined. We provide a tabular representation of important factors to facilitate the future comparison of data replication algorithms. This paper also presents some interesting discussions about future works in data replication by proposing some open research challenges.