Scheduling Heuristics for Live Video Transcoding on Cloud Edges

doi:10.3969/j.issn.1673-5188.2017.02.005

ZTE Communications ›› 2017, Vol. 15 ›› Issue (2): 35-41.DOI: 10.3969/j.issn.1673-5188.2017.02.005

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Scheduling Heuristics for Live Video Transcoding on Cloud Edges

Panagiotis Oikonomou¹, Maria G. Koziri¹, Nikos Tziritas², Thanasis Loukopoulos¹, XU Cheng-Zhong²

¹ University of Thessaly, Lamia 35100, Greece
² Research Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Received:2017-02-09 Online:2017-04-25 Published:2019-12-24
About author:Panagiotis Oikonomou (paikonom@uth.gr) received his Diploma degree (2008) and M.Sc. degree (2010) from the Department of Electrical and Computer Engineering, University of Thessaly, Greece. He is currently a Ph.D. candidate at the same Department. His research interests include optimization algorithms and fuzzy logic methods.|Maria G. Koziri (mkoziri@uth.gr) received her Diploma degree in computer engineering from the Technical University of Crete, Greece in 2003 and Ph.D. degree in computer science from the University of Thessaly, Greece in 2007. She is currently a visiting lecturer in the Computer Science Department of the University of Thessaly. Her research interests include video compression, scalable video coding, rate-distortion optimization and computer architecture.|Nikos Tziritas (nikolaos@siat.ac.cn) received his B.Sc. degree from the Technological Educational Institute of Serres, Greece in 2004, and M.Sc. and Ph.D. degrees from the University of Thessaly, Greece in 2006 and 2011, respectively. He is currently an associate professor in Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. His work has appeared in over 35 publications. He is the recipient of the Award for Excellence for Early Career Researchers in Scalable Computing from IEEE Technical Committee in Scalable Computing in 2016.|Thanasis Loukopoulos (luke@dib.uth.gr) received his Ph.D. degree in computer science from the Hong Kong University of Science and Technology, China. He is currently a lecturer at the Department of Computer Science and Biomedical Informatics of the University of Thessaly, Greece. His research interests are in green computing, cloud computing, WSNs, scheduling, load balancing and video coding parallelization. His work appeared in over 50 publications. He had the best paper award in ICPP 2001.|XU Cheng-Zhong (cz.xu@siat.ac.cn) received the Ph.D. degree in computer science from the University of Hong Kong, China in 1993. He is currently a professor in the Department of Electrical and Computer Engineering of Wayne State University, China and the director of Cloud and Internet Computing Laboratory (CIC) and Sun’s Center of Excellence in Open Source Computing and Applications (OSCA). His research interest is mainly in scalable distributed and parallel systems and wireless embedded computing devices. He has published two books and more than 160 articles in peer-reviewed journals and conferences in these areas.

Abstract

Abstract:

Efficient video delivery involves the transcoding of the original sequence into various resolutions, bitrates and standards, in order to match viewers’ capabilities. Since video coding and transcoding are computationally demanding, performing a portion of these tasks at the network edges promises to decrease both the workload and network traffic towards the data centers of media providers. Motivated by the increasing popularity of live casting on social media platforms, in this paper we focus on the case of live video transcoding. Specifically, we investigate scheduling heuristics that decide on which jobs should be assigned to an edge mini-datacenter and which to a backend datacenter. Through simulation experiments with different QoS requirements we conclude on the best alternative.

Key words: video transcoding, edge computing, scheduling, heuristics, x264

Panagiotis Oikonomou, Maria G. Koziri, Nikos Tziritas, Thanasis Loukopoulos, XU Cheng-Zhong. Scheduling Heuristics for Live Video Transcoding on Cloud Edges[J]. ZTE Communications, 2017, 15(2): 35-41.

Figures/Tables 8

References 26

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Name	Value
Dataset duration	75,079 s
Average broadcast duration	23,263.4 s
Max broadcast duration	1.05E6 s
Number of broadcasts	786,100
Total transcoding tasks	1,244,450
Percentage of braodcasts at 1080p	26.21%
Percentage of braodcasts at 720p	53.24%
Percentage of braodcasts at 480p	11.18%
Percentage of braodcasts at 360p	7.49%
Percentage of braodcasts at 240p	1.86%

Name	Value
Dataset duration	75,079 s
Average broadcast duration	23,263.4 s
Max broadcast duration	1.05E6 s
Number of broadcasts	786,100
Total transcoding tasks	1,244,450
Percentage of braodcasts at 1080p	26.21%
Percentage of braodcasts at 720p	53.24%
Percentage of braodcasts at 480p	11.18%
Percentage of braodcasts at 360p	7.49%
Percentage of braodcasts at 240p	1.86%

Name	Resolution	Frames	Time 240p (fps)	Time 360p (fps)	Time 480p (fps)	Time 720p (fps)
BasketballDrive	1920×1080	500	15.78	5.37	3.78	1.47
BQTerrace	1920×1080	600	29.10	9.11	6.42	2.37
Cactus	1920×1080	500	25.48	8.77	5.09	1.96
Kimono	1920×1080	240	18.54	6.05	4.33	1.62
ParkScene	1920×1080	240	24.95	7.62	5.28	1.94
PeopleOnStreet	2560×1600	150	10.01	2.82	2.00	0.74
Traffic	2560×1600	150	27.36	8.06	5.77	2.17
Average	-	-	21.60	6.82	4.66	1.75
Weights	-	-	1.00	3.16	4.63	12.34

Name	Resolution	Frames	Time 240p (fps)	Time 360p (fps)	Time 480p (fps)	Time 720p (fps)
BasketballDrive	1920×1080	500	15.78	5.37	3.78	1.47
BQTerrace	1920×1080	600	29.10	9.11	6.42	2.37
Cactus	1920×1080	500	25.48	8.77	5.09	1.96
Kimono	1920×1080	240	18.54	6.05	4.33	1.62
ParkScene	1920×1080	240	24.95	7.62	5.28	1.94
PeopleOnStreet	2560×1600	150	10.01	2.82	2.00	0.74
Traffic	2560×1600	150	27.36	8.06	5.77	2.17
Average	-	-	21.60	6.82	4.66	1.75
Weights	-	-	1.00	3.16	4.63	12.34

Scheduling Heuristics for Live Video Transcoding on Cloud Edges

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