  Big data storage management is one of the most challenging issues for Grid
computing environments, since large amount of data intensive applications
frequently involve a high degree of data access locality. Grid applications
typically deal with large amounts of data. In traditional approaches
high-performance computing consists dedicated servers that are used to data
storage and data replication. In this paper we present a new mechanism for
distributed and big data storage and resource discovery services. Here we
proposed an architecture named Dynamic and Scalable Storage Management (DSSM)
architecture in grid environments. This allows in grid computing not only
sharing the computational cycles, but also share the storage space. The storage
can be transparently accessed from any grid machine, allowing easy data sharing
among grid users and applications. The concept of virtual ids that, allows the
creation of virtual spaces has been introduced and used. The DSSM divides all
Grid Oriented Storage devices (nodes) into multiple geographically distributed
domains and to facilitate the locality and simplify the intra-domain storage
management. Grid service based storage resources are adopted to stack simple
modular service piece by piece as demand grows. To this end, we propose four
axes that define: DSSM architecture and algorithms description, Storage
resources and resource discovery into Grid service, Evaluate purpose prototype
system, dynamically, scalability, and bandwidth, and Discuss results.
Algorithms at bottom and upper level for standardization dynamic and scalable
storage management, along with higher bandwidths have been designed.
