Name
Title | ||
|---|---|---|
Machine-Learning Based Performance Estimation For Distributed Parallel Applications In Virtualized Heterogeneous Clusters |
Abstract | ||
|---|---|---|
In a virtualized heterogeneous cluster, for a distributed parallel application which runs in multiple virtual machines (VMs) concurrently, there are a huge number of possible ways to place its VMs. This paper investigates a performance estimation technique for distributed parallel applications in virtualized heterogeneous clusters. We first analyze the effects of different VM configurations on the performance of various distributed parallel applications. We then present a machine learning based performance model for a distributed parallel application. Using a heterogeneous cluster with two different types of nodes, we show that our machine-learning based models can estimate the runtimes of distributed parallel applications with modest error rates. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDCS.2017.310 | 2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017) |
Field | DocType | ISSN |
Cluster (physics),Virtual machine,Computer science,Parallel computing,Performance estimation,Quality of service,Heterogeneous cluster,Performance model,Interference (wave propagation),Distributed computing | Conference | 1063-6927 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seontae Kim | 1 | 3 | 1.39 |
| Nguyen Pham | 2 | 0 | 0.34 |
| Woongki Baek | 3 | 402 | 25.85 |
| Young-Ri Choi | 4 | 391 | 42.28 |