Leveraging endpoint flexibility in data-intensive clusters

Modeling robot flexibility for endpoint force control

Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications

MapReduce is a popular programming paradigm for running largescale data-intensive computation. Recently, many frameworks that implement that paradigm have been developed. To leverage such frameworks, however, developers need to familiarize with each framework’s API and rewrite their code. We present Casper, a new tool that automatically translates sequential Java programs to the MapReduce parad...

Optimizing Parallel Job Performance in Data-Intensive Clusters

Optimizing Parallel Job Performance in Data-Intensive Clusters by Ganesh Ananthanarayanan Doctor of Philosophy in Computer Science University of California, Berkeley Professor Ion Stoica, Chair Extensive data analysis has become the enabler for diagnostics and decision making in many modern systems. These analyses have both competitive as well as social benefits. To cope with the deluge in data...

Scheduling File Transfers for Data-Intensive Jobs on Heterogeneous Clusters

This paper addresses the problem of efficient collective scheduling of file transfers requested by a batch of tasks. Our work targets a heterogeneous collection of storage and compute clusters. The goal is to minimize the overall time to transfer files to their respective destination nodes. Two scheduling schemes are proposed and experimentally evaluated against an existing approach, the Insert...

Network Capacity for Data Intensive Applications on Clusters of Workstations

Component software distribution and the use of clusters of workstations are all key trends in today s technology Little attention has been paid however to the network bandwidth re quired for data intensive applications In the context of databases much work has been done in parallelization strategies for monolithic architectures with dedicated specialized networks or over disk arrays We envision...