Name
Abstract | ||
|---|---|---|
Cloud computing aims to give users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. We present a new job execution environment Flextic that exploits scalable static scheduling techniques to provide the user with a flexible pricing model, such as a tradeoff between different degrees of execution speed and execution price, and at the same time, reduce scheduling overhead for the cloud provider. We have evaluated a prototype of Flextic on Amazon EC2 and compared it against Hadoop. For various data parallel jobs from machine learning, image processing, and gene sequencing that we considered, Flextic has low scheduling overhead and reduces job duration by up to 15% compared to Hadoop, a dynamic cloud scheduler. |
Year | Venue | Keywords |
|---|---|---|
2011 | HotCloud | new job execution environment,cloud provider,job duration,cloud computing,execution price,low scheduling overhead,dynamic cloud scheduler,scalable static scheduling technique,parallel job,execution speed |
Field | DocType | Citations |
Fair-share scheduling,Computer science,Scheduling (computing),Image processing,Exploit,Real-time computing,Rate-monotonic scheduling,Dynamic priority scheduling,Operating system,Cloud computing,Scalability,Distributed computing | Conference | 8 |
PageRank | References | Authors |
0.60 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas A. Henzinger | 1 | 14827 | 1317.51 |
| Anmol V. Singh | 2 | 108 | 4.88 |
| Vasu Singh | 3 | 186 | 9.36 |
| Thomas Wies | 4 | 515 | 28.26 |
| Damien Zufferey | 5 | 358 | 22.61 |