With the development of TV data services, the video conversion equipment in the past was concentrated, and the storage capacity and processing functions did not have scalability, and could not adapt to large-scale video processing. The video editing task is an information-intensive task, and a large number of video transcoding tasks are transferred to a distributed system, making large-scale video transcoding work possible. The purpose of this article is to integrate the research of HDFS and MapReduce distributed transcoding system, fully consider the advantages of HDFS and MapReduce, combine video encoding and decoding technology and video submission method, and propose a group-based classification GOP. Video clips are transcoded individually, and the entire transcoding system is easily configured and managed through a workflow. The results show the impact of changing the number of cluster nodes on transcoding performance, with 10 and 50 GB datasets having faster transcoding speeds in the system compared to 1, 2, 4 and 6 GB datasets. Effects of changing block size and manual copy number on transcoding performance. The experimental results show that the system achieves the best transcoding time performance when the block copy number is set to 4.

Amarn Charun. Distributed Transcoding System Integrating HDFS and MapReduce. Distributed Processing System (2020), Vol. 1, Issue 3: 46-54. https://doi.org/10.38007/DPS.2020.010306.

[1] Veiga J, Exposito R R, Taboada G L, et al. Enhancing in-memory efficiency for MapReduce-based data processing. Journal of Parallel and Distributed Computing, 2018, 120(OCT.):323-338.

[2] Kim J, Kim M H. An efficient parallel processing method for skyline queries in MapReduce. Journal of Supercomputing, 2018, 74(2):1-50.

[3] Hosseini B, Kiani K. FWCMR: A scalable and robust fuzzy weighted clustering based on MapReduce with application to microarray gene expression. Expert Systems with Applications, 2018, 91(jan.):198-210.

[4] Martin D, Martinez-Ballesteros M, Garcia-Gil D, et al. MRQAR: a generic MapReduce framework to discover Quantitative Association Rules in Big Data problems. Knowledge-Based Systems, 2018, 153(AUG.1):176-192.

[5] Neshatpour K, Malik M, Sasan A, et al. Energy-efficient acceleration of MapReduce applications using FPGAs. Journal of Parallel and Distributed Computing, 2018, 119(SEP.):1-17.

[6] Burawis M. Query Optimization: Fund Data Generation Applying NonClustered Indexing and MapReduced Data Cube Numerosity Reduction Method. International Journal of Advanced Trends in Computer Science and Engineering, 2020, 9(1.1 S I):102-109.

[7] Oswalt M S, Ananth J P. MapReduce and Optimized Deep Network for Rainfall Prediction in Agriculture. The Computer Journal, 2020, 63(6):900-912.

[8] Addisie A, Bertacco V. Collaborative Accelerators for Streamlining MapReduce on Scale-up Machines With Incremental Data Aggregation. IEEE Transactions on Computers, 2020, PP(99):1-1.

[9] Moutafis P, Mavrommatis G, Vassilakopoulos M, et al. Efficient processing of all-k-nearest-neighbor queries in the MapReduce programming framework. Data & Knowledge Engineering, 2019, 121(MAY):42-70.

[10] Gimenez-Alventosa V, Molto G, Caballer M. A framework and a performance assessment for serverless MapReduce on AWS Lambda. Future Generation Computer Systems, 2019, 97(AUG.):259-274.

[11] Patil A. Securing mapreduce programming paradigm in hadoop, cloud and big data eco-system. Journal of Theoretical and Applied Information Technology, 2018, 96(3):756-766.

[12] Ciofi D, Albolino S, Dagliana G, et al. Prevalence and multicenter observational study on falls of hospitalized children and Italian, linguistic-cultural validation of the Humpty Dumpty Fall Scale. Professioni infermieristiche, 2020, 73(4):296-304.

[13] Murlistyarini S, Aninda L P, Widyarti S, et al. Exosomes of Adipose-derived Stem Cells Conditioned Media Promotes Retinoblastoma and Forkhead-Box M1 Protein Expression. Open Access Macedonian Journal of Medical Sciences, 2020, 9(A):422-427.

[14] Yousif R Z, Kareem S W, Baban S M. An approach for enhancing data confidentiality in Hadoop. Indonesian Journal of Electrical Engineering and Computer Science, 2020, 20(3):1547-1555.

[15] Prabowo S, Abdurohman M. Studi Perbandingan Performa Algoritma Penjadwalan untuk Real Time Data Twitter pada Hadoop. Komputika Jurnal Sistem Komputer, 2020, 9(1):43-50.

[16] Yousif R Z, Kareem S W, Abdalwahid S M. Enhancing Approach for Information Security in Hadoop. Polytechnic Journal, 2020, 10(1):81-87.

[17] Ayyathan D M, Koganti P, Marcu-Malina V, et al. SMURF2 prevents detrimental changes to chromatin, protecting human dermal fibroblasts from chromosomal instability and tumorigenesis. Oncogene, 2020, 39(2):1-15.

[18] Mitra A, Kundu A, Chattopadhyay M, et al. On the Exploration of Equal Length Cellular Automata Rules Targeting a MapReduce Design in Cloud. International Journal of Cloud Applications and Computing, 2018, 8(2):1-26.