In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth. The major bottleneck to supporting fast data access in Grids is the high latencies of Wide Area Networks and Internet. Effective scheduling can reduce the amount of data transferred across the internet by dispatching a job to where the needed data are present. Another s...

Data Replication is an important aspect to be considered under the cloud and particularly so in the area of data intensive applications. Various replication strategies used in Amazon, Google and Microsoft cloud offerings are discussed followed by comparison of current research scenario and associated algorithms through a suitable tabulation. The merits and gaps existing in these algorithms and ...

Data Grid is a geographically distributed environment that deals with data intensive application in scientific and enterprise computing. Data replication is a common method used to achieve efficient and fault-tolerant data access in Grids. In this paper, a dynamic data replication strategy, called Enhanced Latest Access Largest Weight (ELALW) is proposed. This strategy is an enhanced version of...

Accessing remote services and data is one of the most important applications in Mobile Ad Hoc Networks. Data replication is one of the most popular techniques used to increase data availability by replicating frequently accessed data items locally or nearby. In MANETs, data replication helps to avoid data losses in case of unpredictable network partition and also aids in reducing the number of ...

In dynamic environment of data grid effective job scheduling methods consider location of required data in dispatching jobs to resources. Also, job scheduling methods are combined with data replication mechanisms to reduce remote data access as well as save network bandwidth. In this paper, we combine job scheduling method and dynamic data replication to reduce data access delay and job executi...

With the development of grid computing, Data Grid, as an important branch of Grid Computing focuses on supporting an efficient management mechanism for controlled sharing and large amounts of distributed data. Data intensive jobs are one major kind of jobs in Data Grid, and their scheduling strategies are regarded as one of the most important research fields. Dynamic replication in data grid ai...

Data Grid is composed of a large number of distributed computation and storage resources to facilitate the management of the huge distributed and sharing data resources efficiently. Dynamic replication can reduce the file storage time and use the grid resources effectively in a Data Grid environment. The Data Grid topology is divided into three layers: Regional level, LAN level, the grid site l...

Data Grid is a good solution to large scale data management problems including efficient file transfer and replication. Dynamic data replication in Data Grid aims to improve data access time and to utilize network and storage resources efficiently. Since the data files are very large and the Grid storages are limited, managing replicas in storages for the purpose of more effective utilization o...

Cloud computing provides services and infrastructures to enable end-users to access, modify and share massive geographically distributed data. There are increasing interests in developing data-intensive (big data) applications in this computing environment that need to access huge datasets. Accessing such data in an efficient way is deterred with factors such as dynamic changes in resource avai...