Name
Abstract | ||
|---|---|---|
Recently, virtualized environments such as cloud computing and a virtual cluster are used popularly by lots of MapReduce applications to reap the benefits of low cost and flexibility. However, the I/O bottleneck of the virtualization software gives a burden especially for processing big data. To relieve the burden, we propose a novel burstiness-aware I/O scheduler. Our analysis has revealed that the I/O bottleneck is caused by I/O interferences among the bursty I/Os triggered by different virtual machines, especially when they execute the map and/or reduce tasks. The I/O interferences result in frequent context switches in the virtualization software and long seek distances in a disk. Our proposed I/O scheduler first detects I/O burstiness of a virtual machine on-line. Then, it schedules bursty virtual machines in a round-robin fashion so that a scheduled virtual machine utilizes most of I/O bandwidth without interferences. Real implementation based experiments have shown that our scheduler can enhance the I/O performance up to 23% with an average of 20%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BIGCOMP.2014.6741458 | BigComp |
Keywords | Field | DocType |
mapreduce framework,mapreduce,bursty virtual machines,virtual machine,scheduling,i/o scheduler,i/o interferences,big data processing,virtual machines,burstiness-aware i/o scheduler,i/o interference,virtualized environments,implementation,i/o bandwidth,virtual cluster,big data,burstiness-aware,cloud computing,virtualization software | Virtualization,Bottleneck,Virtual machine,Scheduling (computing),Computer science,Input/output,Burstiness,Operating system,Context switch,Distributed computing,Cloud computing | Conference |
ISSN | Citations | PageRank |
2375-933X | 2 | 0.41 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sewoog Kim | 1 | 2 | 1.43 |
| Dongwoo Kang | 2 | 153 | 19.98 |
| Jongmoo Choi | 3 | 87 | 11.95 |
| Junmo Kim | 4 | 2 | 0.41 |