Grid applications that involve large data transfers. The selection of a replica can, however, significantly influence the efficiency of a replication scheme. Many current approaches assume that a significant amount of data is available, such as network status information, log files of historical Grid

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support to grid users (e.g., scientists). To address these limitations, we present GLIDE, a prototype light-weight, data-intensive middleware infrastructure that enables access to the robust data and computational power of the grid on DREAM platforms. We illustrate GLIDE on an example file sharing

-grained rollback and replay to help debug software. Flashback uses shadow processes to efficiently roll back in-memory state of a process, and logs a process ' interactions with the system to support deterministic replay. Both shadow processes and logging of system calls are implemented in a lightweight

impact performance for distributed data-intensive applications. To address this resource allocation problem, we propose an architecture that adopts a “what if ” methodology to guide allocation decisions taken by the IaaS. The architecture uses a prediction engine with a lightweight simulator to estimate

The recent shift towards write-intensive workload on big data (e.g., financial trading, social user-generated data streams) has pushed the proliferation of log-structured key-value stores, represented by Google’s BigTable [1], Apache HBase [2] and Cassandra [3]. While providing key-based data

and quantity, making it a write-intensive domain. Log-structured storage is capable of providing high write throughput, and hence is a natural choice for managing large-scale observational data. In this paper, we propose an approach to indexing and queryingobservationaldatainlog-structuredstorage. Based on key

One of the first motivations of using grids comes from applications managing large data sets like for example in High Energy Physic or Life Sciences. To improve the global throughput of software environments, replicas are usually put at wisely selected sites. Moreover, computation requests have

design sup-port to grid users (e.g., scientists). To address these limitations, we present GLIDE, a prototype light-weight, data-intensive middleware infrastructure that enables access to the robust data and computational power of the grid on DREAM platforms. We illustrate GLIDE on an example file

ParaTrac is a user-level profiler using file system and process tracing techniques for data-intensive workflow applications. ParaTrac enables users to quickly understand the detailed I/O characteristics from entire application to specific processes or files, it also automatically exploits fine