27 YouTube YouTube UGC User Generated Content CDN Content Delivery Networks LRU Least Recently Used UGC YouTube CGM Consumer Generated Media CGM CGM U
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1 YouTube
2 27 YouTube YouTube UGC User Generated Content CDN Content Delivery Networks LRU Least Recently Used UGC YouTube CGM Consumer Generated Media CGM CGM UGC UGC YouTube k-means YouTube YouTube 1
3 3 10 YouTube k-means 2
4 1 6 2 YouTube k-means YouTube
5 CCDF CCDF CCDF CCDF CCDF CCDF CCDF CCDF CCDF CCDF CCDF k-means k-means k-means Y = 3 d d = 7 Y d = 14 Y
6
7 1 YouTube[1] UGC User Generated Content OTT Over The Top (YouTube ) ISP Internet Service Provider CDN Contents Delivery Networks LRU Least Recently Used [2] UGC CGM Consumer Generated Media) CGM UGC VoD Video-on-Demand UGC [3] Digg[4] [5] YouTube 6
8 YouTube 3 [6] YouTube 1 [7] YouTube [8] Digg YouTube 30 [9] [8] x x [8] [10] YouTube [11] [12] 1 k-means YouTube [12] 1 1 k-means 7
9 1: 2 YouTube YouTube YouTube YouTube 2.1 YouTube YouTube 1 YouTube API YouTube Data API version3.0 [13] , , 900 8
10 ID YouTube API ID ID ( ID ) 1 1 YouTube API 1 1 YouTube API 1 87, YouTube [14] 1 ( 9
11 1, 000km) 30 ID ID 1 JP WestUS EastUS GB FR DE BR IN SNS Social Networking Service
12 hour 2hour 3hour 6hour CCDF e x10 6 1x10 7 View count 2: CCDF
13 day 2day 3day 7day 14day CCDF e x10 6 1x10 7 View count 3: CCDF day 2day 3day 7day 14day CCDF e x10 6 1x10 7 1x10 8 Total view count 4: CCDF 12
14 hour 4-7hour 8-11hour 12-15hour 16-19hour 20-23hour CCDF e x10 6 1x10 7 View count 5: 1 CCDF hour 4-7hour 8-11hour 12-15hour 16-19hour 20-23hour CCDF e x10 6 1x10 7 View count 6: 1 CCDF 13
15 hour 4-7hour 8-11hour 12-15hour 16-19hour 20-23hour CCDF e x10 6 1x10 7 View count 7: 7 CCDF hour 4-7hour 8-11hour 12-15hour 16-19hour 20-23hour CCDF e x10 6 1x10 7 1x10 8 Total view count 8: 7 CCDF 14
16 CCDF WestUS(5541) EastUS(9829) GB(4145) JP(3505) FR(5153) DE(4217) BR(2783) IN(5727) x10 6 View count 9: 1 CCDF CCDF WestUS(5541) EastUS(9829) GB(4145) JP(3505) FR(5153) DE(4217) BR(2783) IN(5727) x10 6 View count 10: 1 CCDF 15
17 CCDF WestUS(5541) EastUS(9829) GB(4145) JP(3505) FR(5153) DE(4217) BR(2783) IN(5727) x10 6 View count 11: 7 CCDF CCDF WestUS(5541) EastUS(9829) GB(4145) JP(3505) FR(5153) DE(4217) BR(2783) IN(5727) x10 6 1x10 7 Total view count 12: 7 CCDF 16
18 2.3 k-means n k-means YouTube [12] 1 k-means 1 k-means v n 0 1 n v k-means (a) (b) SNS (c) 13(a) (d) (a) 3 17
19 1 14(b) (c) (d) (a) (b) (c) (d) ,
20 Normalized view count cluster1 (29225) cluster2 (15564) cluster3 (17590) cluster4 (15036) cluster5 (10415) View count cluster1 (29225) cluster2 (15564) cluster3 (17590) cluster4 (15036) cluster5 (10415) Hour Hour after upload (a) (b) cluster1 (29225) cluster2 (15564) cluster3 (17590) cluster4 (15036) cluster5 (10415) cluster1 (29225) cluster2 (15564) cluster3 (17590) cluster4 (15036) cluster5 (10415) View count CCDF Day after upload 1e e+006 1e+007 View count (c) 1 (d) 7 13: k-means 19
21 Normalized view count cluster1 (25656) cluster2 (16873) cluster3 (12446) cluster4 (20304) cluster5 (12551) View count cluster1 (25656) cluster2 (16873) cluster3 (12446) cluster4 (20304) cluster5 (12551) Hour Hour after upload (a) (b) cluster1(25656) cluster2(16873) cluster3(12446) cluster4(20304) cluster5(12551) cluster1 (25656) cluster2 (16873) cluster3 (12446) cluster4 (20304) cluster5 (12551) View count CCDF Day after upload 1e e+006 1e+007 View count (c) 1 (d) 7 14: k-means 20
22 Normalized view count cluster1 (27234) cluster2 (12079) cluster3 (10727) cluster4 (18786) cluster5 (19004) View count cluster1 (27234) cluster2 (12079) cluster3 (10727) cluster4 (18786) cluster5 (19004) Hour Hour after upload (a) (b) cluster1 (27234) cluster2 (12079) cluster3 (10727) cluster4 (18786) cluster5 (19004) cluster1 (27234) cluster2 (12079) cluster3 (10727) cluster4 (18786) cluster5 (19004) View count CCDF Day after upload 1e e+006 1e+007 View count (c) 1 (d) 7 15: k-means 21
23 3 YouTube Y 3.1 A B P (B A) P (B A) = P (B)P (A B) P (A) n F 1,, F n C p(c F 1,, F n ) p(c F 1,, F n ) = p(c)p(f 1,, F n C) p(f 1,, F n ) C p(c, F 1,, F n ) = p(c)p(f 1 C)p(F 2 C)p(F 3 C) C classify classify(f 1,, f n ) = arg max c n p(c = c) p(f i = f i C = c) (1) i= H Y d H Y + d H Y Y Y 22
24 16: YouTube Data API 1 3. Y YouTube Data API Y d YouTube Data API Y + d Y Y d d 23
25 1: ID Y Y =H 1 2 Y =L abcdefghijk H lmnopqrstuv L wxyz H d 1 1% 2 d d 1% 3.1 n F 1,, F n 2.3 Y v Y 0 1 Y Y Y , 830 p(c = c) p(f i = f i C = c) p(c = c) p(f i = f i C = c) classify NBC: Naive Bayes Classifier VCS: View Count based Selection 24
26 Y d = 7 Y = % % 3 7 d = 14 Y = % % 3 14 Y = 3 d 1 17(a) 2 17(b) 1 2 d = 7 Y 1 18(a) 2 18(b) Y/3 25
27 2: % 2 8 1% : % % Y d = 14 Y 1 19(a) 2 19(b) d = 7 Y 3 26
28 1 0.8 Accuracy NBC VCS 21 d 28 (a) d Accuracy NBC VCS 21 d 28 (b) d 17: Y = 3 d 27
29 1 NBC VCS 0.8 Accuracy Y (hour) (a) d 1 NBC VCS 0.8 Accuracy Y (hour) (b) d 18: d = 7 Y 28
30 1 NBC VCS 0.8 Accuracy Y (hour) (a) d 1 NBC VCS 0.8 Accuracy Y (hour) (b) d 19: d = 14 Y 29
31 4 YouTube 1 k-means 3 Good 30
32 NTT 31
33 [1] YouTube. [2] N. Kamiyama, R. Kawahara, T. Mori, and H. Hasegawa, Multicast Pre-distribution VoD System, IEICE transactions on communications, vol. E96-B, pp , June [3] K. Lerman and T. Hogg, Using a Model of Social Dynamics to Predict Popularity of News, in Proceedings of the nineteenth international conference on World Wide Web, pp , Apr [4] Digg. [5] F. Figueiredo, F. Benevenuto, and J. M. Almeida, The tube over time: characterizing popularity growth of YouTube videos, in Proceedings of the fourth ACM international conference on Web search and data mining, pp , Feb [6] Y. Borghol, S. Mitra, S. Ardon, N. Carlsson, D. Eager, and A. Mahanti, Characterizing and modelling popularity of user-generated videos, Performance Evaluation, vol. 68, pp , Nov [7] G. Gürsun, M. Crovella, and I. Matta, Describing and forecasting video access patterns, in Proceedings of IEEE INFOCOM, pp , Apr [8] G. Szabo and B. A. Huberman, Predicting the popularity of online content, Communications of the ACM, vol. 53, pp , Aug [9] H. Pinto, J. M. Almeida, and M. A. Gonçalves, Using early view patterns to predict the popularity of youtube videos, in Proceedings of the sixth ACM international conference on Web search and data mining, pp , Feb [10] G. Chatzopoulou, C. Sheng, and M. Faloutsos, A first step towards understanding popularity in YouTube, in Proceedings of INFOCOM IEEE Conference on Computer Communications Workshops, pp. 1 6, Mar [11] J. M. Tirado, D. Higuero, F. Isaila, and J. Carretero, Multi-model prediction for enhancing content locality in elastic server infrastructures, in Proceedings of the 32
34 eighteenth International Conference on High Performance Computing, pp. 1 9, Dec [12] Y. Kitade, Analyzing popularity dynamics of YouTube content and its application to content cache design, Master s thesis, Graduate School of Information Science and Technology, Osaka University, Feb [13] YouTube Data API. [14] YouTube Help. 33
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