TY - GEN
T1 - A version-Aware computation and storage trade-off strategy for multi-version VoD systems in the cloud
AU - Zhao, Hui
AU - Zheng, Qinghua
AU - Zhang, Weizhan
AU - Du, Biao
AU - Chen, Yuxuan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/11
Y1 - 2016/2/11
N2 - Nowdays, many Video-on-Demand (VoD) providers offer multiple-quality video streaming services to heterogeneous clients, called as multi-version VoD. Some researches focus on video transcoding in real-Time or video layered encoding/decoding, but they are not widely used in VoD industry. Storing multiple versions of the same video is an easy solution, but it consumes lots of storage space. Although there are also a few works about trading-off between transcoding and storage, they did not utilize the transcoding relationships among different versions and took the video popularity into account, which bring that they may have little cost-efficiency for multi-version VoD systems. To minimize the cost, in this paper, we propose a version-Aware transcoding computation and storage trade-off strategy for multi-version VoD systems in the cloud. Firstly, it utilizes the transcoding weight graph to describe the transcoding relationships among different versions of a video. According to the graph, the transcoding computation cost from one version to another version can be calculated. Secondly, it takes the video popularity of different versions, the prices of storage and computation resources in the cloud into account to decide which versions of which videos should be stored or transcoded. We then formulate it as an optimization problem and present a heuristic approximate optimal solution. Finally, we conduct extensive simulations to evaluate our strategy and solution, and the results show that they can significantly lower the cost of multi-version VoD systems.
AB - Nowdays, many Video-on-Demand (VoD) providers offer multiple-quality video streaming services to heterogeneous clients, called as multi-version VoD. Some researches focus on video transcoding in real-Time or video layered encoding/decoding, but they are not widely used in VoD industry. Storing multiple versions of the same video is an easy solution, but it consumes lots of storage space. Although there are also a few works about trading-off between transcoding and storage, they did not utilize the transcoding relationships among different versions and took the video popularity into account, which bring that they may have little cost-efficiency for multi-version VoD systems. To minimize the cost, in this paper, we propose a version-Aware transcoding computation and storage trade-off strategy for multi-version VoD systems in the cloud. Firstly, it utilizes the transcoding weight graph to describe the transcoding relationships among different versions of a video. According to the graph, the transcoding computation cost from one version to another version can be calculated. Secondly, it takes the video popularity of different versions, the prices of storage and computation resources in the cloud into account to decide which versions of which videos should be stored or transcoded. We then formulate it as an optimization problem and present a heuristic approximate optimal solution. Finally, we conduct extensive simulations to evaluate our strategy and solution, and the results show that they can significantly lower the cost of multi-version VoD systems.
KW - computation and storage trade-off
KW - heterogeneous clients
KW - multi-version VoD
KW - transcoding weight graph
UR - https://www.scopus.com/pages/publications/84961956374
U2 - 10.1109/ISCC.2015.7405635
DO - 10.1109/ISCC.2015.7405635
M3 - 会议稿件
AN - SCOPUS:84961956374
T3 - Proceedings - IEEE Symposium on Computers and Communications
SP - 943
EP - 948
BT - 20th IEEE Symposium on Computers and Communication, ISCC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE Symposium on Computers and Communication, ISCC 2015
Y2 - 6 July 2015 through 9 July 2015
ER -