Computation Capability Deduction Architecture for MapReduce on Cloud Computing
- Resource Type
- Conference
- Authors
- Huang, Tzu-Chi; Chu, Kuo-Chih; Huang, Guo-Hao; Shen, Yan-Chen; Shieh, Ce-Kuen
- Source
- 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) PDCAT Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2017 18th International Conference on. :368-375 Dec, 2017
- Subject
- Computing and Processing
Task analysis
Cloud computing
Computers
Computer architecture
Acceleration
Market research
Programming
MapReduce
Cloud Computing
Computation Capability Deduction Architecture
Slot Number
Task Allocation
- Language
MapReduce gradually becomes the de facto programming standard of applications on cloud computing. However, MapReduce needs a cloud administrator to manually configure parameters of the run-time system such as slot numbers for Map and Reduce tasks in order to get the best performance. Because the manual configuration has a risk of performance degradation, MapReduce should utilize the Computation Capability Deduction Architecture (CCDA) proposed in this paper to avoid the risk. MapReduce can use CCDA to help the run-time system to distribute appropriate numbers of tasks over computers in a cloud at run time without any manual configuration made by a cloud administrator. According to experiment observations in this paper, MapReduce can get great performance improvement with the help of CCDA in data-intensive applications such as Inverted Index and Word Count that are usually required to process big data on cloud computing.