The PRAGMA Testbed - Building a Multi-Application International Grid
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Map-reduce-merge: simplified relational data processing on large clusters
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
IEEE Internet Computing
A virtual network (ViNe) architecture for grid computing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
The Hadoop Distributed File System
MSST '10 Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)
Enhancing MapReduce via Asynchronous Data Processing
ICPADS '10 Proceedings of the 2010 IEEE 16th International Conference on Parallel and Distributed Systems
A hierarchical framework for cross-domain MapReduce execution
Proceedings of the second international workshop on Emerging computational methods for the life sciences
Exploring MapReduce efficiency with highly-distributed data
Proceedings of the second international workshop on MapReduce and its applications
| Hi-index | 0.00 |
We present a Hierarchical MapReduce framework that gathers computation resources from different clusters and runs MapReduce jobs across them. The applications implemented in this framework adopt the Map-Reduce-Global Reduce model where computations are expressed as three functions: Map, Reduce, and Global Reduce. Two scheduling algorithms are introduced: Compute Capacity Aware Scheduling for compute-intensive jobs and Data Location Aware Scheduling for data-intensive jobs. Experimental evaluations using a molecule binding prediction tool, Auto Dock, and grep demonstrate promising results for our framework.