Name
Abstract | ||
|---|---|---|
ExoGENI is a new GENI-federated Infrastructureas- a-Service (IaaS) framework. In this paper, we evaluate the performance of data-intensive applications on ExoGENI's resources. To simplify experiments, we design an automatic provisioning system called ExoApp. This paper focuses on MapReduce-based applications. Users can easily deploy applications in ExoGENI using ExoApp, without having to manually configure cluster runtime environments. We then conduct a series of experiments using real-world data sets and standard benchmarks through ExoApp. Our result shows that ExoGENI demonstrates similar resource quality when hosting data-intensive applications and its Network-as-a-Service (NaaS) model maintains stable network performance. We finally identify the pros and cons of the ExoGENI's NaaS model in supporting data-intensive applications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GREE.2013.14 | GREE |
Keywords | Field | DocType |
parallel processing,mapreduce,data-intensive,web services,mapreduce-based applications,exogeni,stable network performance,mapreduce-based application,network-as-a-service,software performance evaluation,web service,geni-federated infrastructureas-a-service framework,resource quality,configure cluster runtime environment,similar resource quality,experiment,standard benchmarks,data-intensive application,deploy application,data-intensive applications,naas model,performance evaluation,cluster runtime environments,iaas framework,cloud computing,real-world data set,exoapp,new geni-federated infrastructureas,automatic provisioning system,computational modeling,benchmark testing,programming,bandwidth | Data set,Computer science,Parallel processing,Provisioning,Bandwidth (signal processing),Web service,Benchmark (computing),Operating system,Cloud computing,Distributed computing,Network performance | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ze Yu | 1 | 35 | 3.74 |
| Xinxin Liu | 2 | 179 | 12.98 |
| Min Li | 3 | 48 | 7.59 |
| Kaikai Liu | 4 | 190 | 20.37 |
| Xiaolin Li | 5 | 405 | 37.36 |