Name
Abstract | ||
|---|---|---|
An expanding wealth of ubiquitous, heterogeneous, and interconnected embedded devices is behind most of the exponential growth of the “Big Data” phenomenon. Meanwhile, the same embedded devices continue to improve in terms of computational capabilities, thus closing the gap with more traditional computers. Motivated by these trends, we developed a heterogeneous computing system for MapReduce applications that couples cloud computing with distributed embedded computing. Specifically, our system combines a central cluster of Linux servers with a broadband network of embedded set-top box (STB) devices. The MapReduce platform is based on the Hadoop software framework, which we modified and optimized for execution on the STBs. Experimental results confirm that this type of heterogeneous computing system can offer a scalable and energy-efficient platform for the processing of large-scale data-intensive applications. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CloudCom.2012.6427483 | CloudCom |
Keywords | DocType | ISBN |
mapreduce,set-top boxes,mapreduce utilizing hadoop,broadband network,hadoop software framework,big data,heterogeneous computing system,embedded computing,large-scale data-intensive applications,mapreduce application,mapreduce platform,set-top box (stb),embedded device,heterogeneous embedded devices,energy-efficient platform,energy conservation,stb devices,embedded set-top box,big data phenomenon,interconnected embedded devices,linux,ubiquitous computing,linux server,embedded set-top box devices,broadband embedded computing system,ubiquitous embedded devices,benchmark,linux server central cluster,embedded systems,cloud computing,hadoop,distributed embedded computing,computational capability,java | Conference | 978-1-4673-4509-5 |
Citations | PageRank | References |
3 | 0.44 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| YoungHoon Jung | 1 | 19 | 2.23 |
| Luca P. Carloni | 2 | 1713 | 120.17 |
| Richard Neill | 3 | 3 | 0.44 |