Study on emerging implementations of MapReduce
作者： Akhil Goyal , Bharti
DOI: 10.1109/CCAA.2015.7148364
关键词:
摘要: MapReduce is a programming model specifically developed for the management and processing of “Big Data” — extremely large amounts data that expects high level analyzing capabilities. With every passing day volumes generated collected from multiple resources across planet. This must be analyzed in sense volume or speed moving to systems. efficiently execute programs on clusters by utilizing concept parallelism. Till now Google's framework has been considered as most successful implementation Big Data. A number implementations have proposed. paper discusses various emerging model. An emphasis also given leading lacking strength these implementations.
sci-hub.se 参考文章(16)
Jaliya Ekanayake, Hui Li, Bingjing Zhang, Thilina Gunarathne, Seung-Hee Bae, Judy Qiu, Geoffrey Fox, Twister: a runtime for iterative MapReduce high performance distributed computing. pp. 810- 818 ,(2010) , 10.1145/1851476.1851593
Satish Gopalani, Rohan Arora, Comparing Apache Spark and Map Reduce with Performance Analysis using K-Means International Journal of Computer Applications. ,vol. 113, pp. 8- 11 ,(2015) , 10.5120/19788-0531
Diana Moise, Denis Shestakov, Gylfi Gudmundsson, Laurent Amsaleg, Terabyte-scale image similarity search: Experience and best practice international conference on big data. pp. 674- 682 ,(2013) , 10.1109/BIGDATA.2013.6691637
E. Dede, Z. Fadika, J. Hartog, M. Govindaraju, L. Ramakrishnan, D. Gunter, R. Canon, MARISSA: MApReduce Implementation for Streaming Science Applications international conference on e-science. pp. 1- 8 ,(2012) , 10.1109/ESCIENCE.2012.6404432
Kyong-Ha Lee, Yoon-Joon Lee, Hyunsik Choi, Yon Dohn Chung, Bongki Moon, Parallel data processing with MapReduce ACM SIGMOD Record. ,vol. 40, pp. 11- 20 ,(2012) , 10.1145/2094114.2094118
Seema Maitrey, C.K. Jha, Handling Big Data Efficiently by Using Map Reduce Technique computational intelligence. pp. 703- 708 ,(2015) , 10.1109/CICT.2015.140
Vasiliki Kalavri, Vladimir Vlassov, MapReduce: Limitations, Optimizations and Open Issues trust security and privacy in computing and communications. pp. 1031- 1038 ,(2013) , 10.1109/TRUSTCOM.2013.126
Kun Liu, Gaochao Xu, Jun’e Yuan, An Improved Hadoop Data Load Balancing Algorithm Journal of Networks. ,vol. 8, pp. 2816- 2822 ,(2013) , 10.4304/JNW.8.12.2816-2822
Wei Yan, Yuan Xue, Bradley Malin, Scalable and robust key group size estimation for reducer load balancing in MapReduce international conference on big data. pp. 156- 162 ,(2013) , 10.1109/BIGDATA.2013.6691568
Tao Jiang, Qianlong Zhang, Rui Hou, Lin Chai, Sally A. Mckee, Zhen Jia, Ninghui Sun, Understanding the Behavior of In-Memory Computing Workloads ieee international symposium on workload characterization. pp. 22- 30 ,(2014) , 10.1109/IISWC.2014.6983036