Mapreduce dalam Layanan Transcoding
DOI:
https://doi.org/10.25126/jtiik.2023106310Abstrak
Penyediaan file video dengan bitrate bervariasi menjadi syarat utama bagi layanan Video On Demand yang menerapkan adaptive streaming. Hal tersebut dilakukan dengan transcoding yang menghasilkan video dengan multi-bitrate. Proses multi-bitrate transcoding membutuhkan waktu yang tidak singkat. Lama durasi waktunya sebanding dengan besarnya bitrate, frame size dan frame rate. Untuk mempersingkat durasi transcoding, dibuat sebuah prototipe layanan transcoding dengan menerapkan Mapreduce. Layanan transcoding dengan Mapreduce terdiri dari satu komputer master dan beberapa komputer worker yang terhubung dalam satu LAN. Dengan dikoordinir oleh komputer master, komputer-komputer worker mengerjakan proses Map dan Reduce. Di dalam proses Map dilakukan transcoding terhadap segmented videos. Di dalam proses Reduce dilakukan penggabungan segmen-segmen video yang telah di-transcode dengan parameter/key (bitrate, frame size, dan frame rate) yang sama menjadi satu video yang utuh. Prototipe layanan transcoding dibuat menggunakan Library FFMPEG untuk transcoding, SCP untuk transfer file, RPC untuk komunikasi antar komputer. Di dalam pengujian prototipe, jumlah komputer worker ditentukan sebanyak 7 buah. Kinerja layanan transcoding sangat memuaskan dengan rata-rata efektifitas transcoding sebesar 71,3% dibandingkan dengan transcoding menggunakan satu komputer.
Abstract
The provision of video files with varying bitrates is the main requirement for Video On Demand services that implement adaptive streaming. Transcoding that produces multiple bitrates achieves that. Multi-bitrate transcoding can take a longer time. That duration is comparable with the large of the video's bitrates, frame sizes, and frame rates. A transcoding service prototype was created based on Mapreduce to shorten the duration. Transcoding service using Mapreduce consists of several computers connected to a LAN, one as master and the other as worker computers. The master coordinates the workers to do the Map and Reduce process. In the Map process, workers transcode the segmented videos. In the Reduce process, workers merge all transcoded video segments with the same key or parameters (bitrate, frame size, and frame rate) into a single video. The transcoding service prototype was created using the FFMPEG library for transcoding, SCP for file transfer, and RPC for communication between computers. In the testing stage, the number of workers is 7 computers. Service performance is very satisfactory, with average transcoding effectiveness of 71,3% compared to transcoding using a single computer.
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