Abstract
Apache Spark and Hadoop are open source frameworks for big data processing, which have been adopted by many companies. In order to implement a reliable big data system that can satisfy processing target completion times, accurate resource provisioning and job execution time estimations are needed. In this paper, time estimation and resource minimization schemes for Spark and Hadoop systems are presented. The proposed models use the probability of failure in the estimations to more accurately formulate the characteristics of real big data operations. The experimental results show that the proposed Spark adaptive failure-compensation and Hadoop adaptive failure-compensation schemes improve the accuracy of resource provisions by considering failure events, which improves the scheduling success rate of big data processing tasks.
| Original language | English |
|---|---|
| Article number | 8605312 |
| Pages (from-to) | 9658-9666 |
| Number of pages | 9 |
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)
- Engineering(all)
Cite this
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Time Estimation and Resource Minimization Scheme for Apache Spark and Hadoop Big Data Systems with Failures. / Lee, Jinbae; Kim, Bobae; Chung, Jong-Moon.
In: IEEE Access, Vol. 7, 8605312, 01.01.2019, p. 9658-9666.Research output: Contribution to journal › Article
TY - JOUR
T1 - Time Estimation and Resource Minimization Scheme for Apache Spark and Hadoop Big Data Systems with Failures
AU - Lee, Jinbae
AU - Kim, Bobae
AU - Chung, Jong-Moon
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Apache Spark and Hadoop are open source frameworks for big data processing, which have been adopted by many companies. In order to implement a reliable big data system that can satisfy processing target completion times, accurate resource provisioning and job execution time estimations are needed. In this paper, time estimation and resource minimization schemes for Spark and Hadoop systems are presented. The proposed models use the probability of failure in the estimations to more accurately formulate the characteristics of real big data operations. The experimental results show that the proposed Spark adaptive failure-compensation and Hadoop adaptive failure-compensation schemes improve the accuracy of resource provisions by considering failure events, which improves the scheduling success rate of big data processing tasks.
AB - Apache Spark and Hadoop are open source frameworks for big data processing, which have been adopted by many companies. In order to implement a reliable big data system that can satisfy processing target completion times, accurate resource provisioning and job execution time estimations are needed. In this paper, time estimation and resource minimization schemes for Spark and Hadoop systems are presented. The proposed models use the probability of failure in the estimations to more accurately formulate the characteristics of real big data operations. The experimental results show that the proposed Spark adaptive failure-compensation and Hadoop adaptive failure-compensation schemes improve the accuracy of resource provisions by considering failure events, which improves the scheduling success rate of big data processing tasks.
UR - http://www.scopus.com/inward/record.url?scp=85061182412&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061182412&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2891001
DO - 10.1109/ACCESS.2019.2891001
M3 - Article
AN - SCOPUS:85061182412
VL - 7
SP - 9658
EP - 9666
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 8605312
ER -