!!!!!! Agenda 2012/2/6
|
|
|
- いつや ひのと
- 7 years ago
- Views:
Transcription
1 Loca%on Based P2P Loca%on Based P2P Clustering with Seeders 5110B /2/6
2 !!!!!! Agenda 2012/2/6
3 - IP [1] P2P P2P [1] 2010 Cisco Visual Networking Index Forecast 2012/2/6
4 P2P (BitTorrent)! P2P!!!!! BitTorrent Peer u u torrent u Peer tracker Peer 2012/2/6 Peer
5 P2P Loca%on Based ClusteringP2P 2010 P2P BitTorrent P2P 2012/2/6
6 Intelligent tracker Cluster 1 Cluster 2 Cluster 3 Cluster N 2012/2/6
7 P2P 2012/2/6
8 2012/2/6
9 ! Intelligent Tracker Fuzzy c- means u K- means, PAM u u NMF u Fuzzy c- means PAM (Par%%on Around Medoids) = K- medois method 2012/2/6
10 Request Intelligent tracker Fuzzy c- means PAM User User xx.xx.xxx.xxx xx.xxx.xx.xxx xx.x.xxx.xxx PAM (Par%%on Around Medoids) = K- medois method
11 PAM 2 Fuzzy c- means no yes
12 ! ~]# less /var/log/hapd/access_log [20/Oct/2011:14:21: ] GET /opentracker/announce.php? info_hash=%cf9%f6p%20%15%b9%deq6%dc%2c%ca %A1%BB8%F0%A0%D3%82&peer_id=T03H lo07rb9jaku&port=42031&uploaded=0&downloaded=0&ler=0&no_peer_id=1&compact =1&event=started&key=VhGRK6 HTTP/ BitTornado/T " ler = 0 ler1
13
14 !!!! PlanetLab! Case Case Case Case n = 30 = 4 n = 618 =
15 Case1 n=
16 Case2 n=
17 Case3 n=
18 Case4 n=
19 !! Case 1 4 HD 2Mbps
20 !!!!!
21
22
23 !!
24 !! n=30 (planet lab )! k=4! k- means! 10
25 P2P
26 ! kmeans pam! !! kmenas! pam medoids K- means method centroid O(Nk) K- medois method (Par%%on Around Medoids) medoid outlier O(N2k) 2012/2/6
27 K- means! c, c, 1 2, c k K i= 1 x c i x c i 2 c, c, 2,! K K 1!!! c k x i
28 Fuzzy c- means! Fuzzy c- means K- means g ik J = N K i= 1 k = 1 ( g ik ) m x i c k 2! gik ck x i c k (0) 1. (t ) (t ) c c k g ik (t ) g g ( t+1) ik ik c k 2. (3.2) J k 3. (3.2) J
29 !!!! 0 1
30 UP
31 ! Fuzzy c- means Fuzzy c- means [3] [3] R, 2007.
32 n n / n BitTorrent BitTorrent n ID
33 ! BitTorrent Earliest- First Rarest- First Earliest- First 2012/2/6
34 ! interval %me! default = 900[s] 90[s]!!!!!
35
36 !! P2P 2Mbps 2Mbps : YouTube HD!! (Case1 n=30)! Eariest First!! 200MB! 256KB [2] Rarest First 1256KB = 2Mbit 1 1[s] [2], Loca%on Based Clustering P2P, 2010
37 P2P! Mbps Mbps
38 P2P (Mbps) Mbps (Mbps) Mbps
39
40 !!!!! RTT!!
41 ! PlanetLab Case1 30!! or! ping! TTL RTT!
42 RTT (ms) RTT (ms)
43 RTT ms ms
44
45 ms RTT RTT ms ' 2 3 RTT 100ms 1' 2 3 RTT (ms)
46 !!!
Microsoft PowerPoint _junki.pptx
地理情報を考慮した P2P ストリーミング Optimized P2P Streaming with Geographical Information 後藤研究室修士 2 年 5109B021 9 大村淳己 関連発表 大村淳己, 高田和也, 後藤滋樹, Location Based Clusteringを用いたP2P ストリーミング, 電子情報通信学会技術研究報告, vol. 110, no. 373,
P2P P2P Winny 3 P2P 15 20 P2P 1 P2P, i
26 P2P Reduction of search packets by sharing peer information in P2P communication 1175073 2015 2 27 P2P P2P Winny 3 P2P 15 20 P2P 1 P2P, i Abstract Reduction of search packets by sharing peer information
Web Web Web
P2P Web Proxy The Cooperation P2P Web Proxy for reduction of offer resources 1165054 26 3 20 1 5 2 7 2.1...................................... 7 2.1.1 Web...................... 7 2.1.2 Web.....................
橡Taro9-生徒の活動.PDF
3 1 4 1 20 30 2 2 3-1- 1 2-2- -3- 18 1200 1 4-4- -5- 15 5 25 5-6- 1 4 2 1 10 20 2 3-7- 1 2 3 150 431 338-8- 2 3 100 4 5 6 7 1-9- 1291-10 - -11 - 10 1 35 2 3 1866 68 4 1871 1873 5 6-12 - 1 2 3 4 1 4-13
HTTP
2008 P2P 2009 2 6 5107B024-5 1 5 1.1...................................... 5 1.2...................................... 6 1.3..................................... 7 2 HTTP 8 2.1.............................
自律分散協調2010-5.pptx
WinnyP2P Winny P2P P2P: Peer to Peer Peer: Client/server Nodeserver client Servant P2P P2P Pure P2P model A B IP P2P(pros) Client/Server P2P Client/Server P2P Google P2P(cons) P2P : P2P WWW( ) WWW Peer
book.dvi
P2P Web Proxy 1120180 24 3 16 1 3 2 5 2.1 Web........................ 5 2.2 Web Proxy.................................... 10 2.2.1 P2P Web Proxy.............................. 11 3 P2P Web Proxy 13 3.1...................................
24 Region-Based Image Retrieval using Fuzzy Clustering
24 Region-Based Image Retrieval using Fuzzy Clustering 1130323 2013 3 9 Visual-key Image Retrieval(VKIR) k-means Fuzzy C-means 2 200 2 2 20 VKIR 5 18% 54% 7 30 Fuzzy C-means i Abstract Region-Based Image
サービス付き高齢者向け住宅賠償責任保険.indd
1 2 1 CASE 1 1 2 CASE 2 CASE 3 CASE 4 3 CASE 5 4 3 4 5 6 2 CASE 1 CASE 2 CASE 3 7 8 3 9 10 CASE 1 CASE 2 CASE 3 CASE 4 11 12 13 14 1 1 2 FAX:03-3375-8470 2 3 3 4 4 3 15 16 FAX:03-3375-8470 1 2 0570-022808
-----------------------------------------------------------------------------------------1 --------------------------------------------------------------------------------------1 -------------------------------------------------------------------------------------1
›¼’à”v“lŠÍ1−ª
3 1 1 2 3 4 5 6 7 8 8 10 12 14 16 18 20 22 24 2 1 2 3 4 5 6 7 8 9 10 11 12 26 28 30 32 34 36 38 40 42 44 46 48 50 4 3 1 2 3 4 5 52 54 56 58 60 6 62 7 8 9 10 64 66 68 70 72 5 1 1 4 2 5 6 6 7 1 8 1 9 2 10
1 2 3 4 1 2 3 4 1 2 3 4 12 3 4
1 2 3 4 5 6 1 2 3 4 5 6 1 2 1 2 1 2 1 2 1 2 3 4 12 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 12 3 4 1 2 3 4 5 6 7 8 1 2 3 46 7 1 2 3 4 5 6 7 1 2 3 4 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 16 7 8 92 3 46 7 :
- 1 - 2 - 3 - 4 - 5 - 6 - 7 - 8 - 9 - 10 - 11 2-12 - 1 - 2 - 3 - 4 - 5 - 6 - 7 - 1 - 2 - 3 6 1 1-4 - 5 - 6 - 1 - 2 - 3 - 4 - 5 - 6 - 7 - 8 130 3 130 5 2 50 1.5 48 59 62 63-9 - 1 - 2 - 3 () - 4 - 5 -
S1460...........\1.E4
3 4 3 4 5 3 4 5 3 4 3 4 3 3 3 3 4 3 4 3 3 4 4 3 3 3 3 4 3 4 3 3 3 3 3 3 4 3 4 3 4 5 4 3 5 4 3 3 4 5 3 4 5 4 3 4 3 3 4 3 4 3 3 3 3 3 4 3 3 4! "! " " 0 6 ! " 3 4 3 3 4 3 ! " 3 3 4 5 4 5 6 3 3
孫文について
1924 1 1 165 ( ) 3 1987 YouTube http://www.youtube.com/watch?v=mttee1lhbkg http://www.youtube.com/watch?v=ibyhsmir9fa&list=pl8fdacdbcb7a4b2a7 http://www.youtube.com/watch?v=cppajh6il5q&list=pl96acdb31e24545b7
日経テレコン料金表（2016年4月）
1 2 3 4 8,000 15,000 22,000 29,000 5 6 7 8 36,000 42,000 48,000 54,000 9 10 20 30 60,000 66,000 126,000 166,000 50 100 246,000 396,000 1 25 8,000 7,000 620 2150 6,000 4,000 51100 101200 3,000 1,000 201
73 p.1 22 16 2004p.152
1987 p.80 72 73 p.1 22 16 2004p.152 281895 1930 1931 12 28 1930 10 27 12 134 74 75 10 27 47.6 1910 1925 10 10 76 10 11 12 139 p.287 p.10 11 pp.3-4 1917 p.284 77 78 10 13 10 p.6 1936 79 15 15 30 80 pp.499-501
122011pp.139174 18501933
122011pp.139174 18501933 122011 1850 3 187912 3 1850 8 1933 84 4 1871 12 1879 5 2 1 9 15 1 1 5 3 3 3 6 19 9 9 6 28 7 7 4 1140 9 4 3 5750 58 4 3 1 57 2 122011 3 4 134,500,000 4,020,000 11,600,000 5 2 678.00m
2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226. 1893 B pp. 1 2. p. 3.
1 73 72 1 1844 11 9 1844 12 18 5 1916 1 11 72 1 73 2 1862 3 1870 2 1862 6 1873 1 3 4 3 4 7 2 3 4 5 3 5 4 2007 p. 117. 2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226.
29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23
29 2011 3 pp.55 86 19 1886 2 13 1 1 21 1888 1 13 2 3,500 3 5 5 50 4 1959 6 p.241 21 1 13 2 p.14 1988 p.2 21 1 15 29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23 1
Microsoft Word - 映画『東京裁判』を観て.doc
1 2 3 4 5 6 7 1 2008. 2 2010, 3 2010. p.1 4 2008 p.202 5 2008. p.228 6 2011. 7 / 2008. pp.3-4 1 8 1 9 10 11 8 2008, p.7 9 2011. p.41 10.51 11 2009. p. 2 12 13 14 12 2008. p.4 13 2008, p.7-8 14 2008. p.126
() L () 20 1
() 25 1 10 1 0 0 0 1 2 3 4 5 6 2 3 4 9308510 4432193 L () 20 1 PP 200,000 P13P14 3 0123456 12345 1234561 2 4 5 6 25 1 10 7 1 8 10 / L 10 9 10 11 () ( ) TEL 23 12 7 38 13 14 15 16 17 18 L 19 20 1000123456
308 ( ) p.121
307 1944 1 1920 1995 2 3 4 5 308 ( ) p.121 309 10 12 310 6 7 ( ) ( ) ( ) 50 311 p.120 p.142 ( ) ( ) p.117 p.124 p.118 312 8 p.125 313 p.121 p.122 p.126 p.128 p.156 p.119 p.122 314 p.153 9 315 p.142 p.153
戦後の補欠選挙
1 2 11 3 4, 1968, p.429., pp.140-141. 76 2005.12 20 14 5 2110 25 6 22 7 25 8 4919 9 22 10 11 12 13 58154 14 15 1447 79 2042 21 79 2243 25100 113 2211 71 113 113 29 p.85 2005.12 77 16 29 12 10 10 17 18
![宅建練馬表478号1_4ol [更新済み].eps](/thumbs/39/20197934.jpg "宅建練馬表478号1_4ol [更新済み].eps")
001 No.3/12 1 1 2 3 4 5 6 4 8 13 27 33 39 001 No.3/12 4 001 No.3/12 5 001 No.3/12 6 001 No.3/12 7 001 8 No.3/12 001 No.3/12 9 001 10 No.3/12 001 No.3/12 11 Index 1 2 3 14 18 21 001 No.3/12 14 001 No.3/12
1 2 3 4 10 5 30 87 50 20 3 7 2 2 6 3 70 7 5 10 20 20 30 14 5 1,000 24 112 2 3 1 8 110 9 JR 10 110 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 25 30 31 32 25 A 33 B C D E F G PR PR or 34 35
IP IP DHCP..
NICE 2008 4 14 1 NICE 2 1.1.................... 2 2 3 2.1........................................ 3 2.2....................................... 5 2.3.................................... 6 2.4...................................
, ,
41 42 73 121 121 10 122 11 122 12 131 13 131 15 10 133 16 11 133 17 12 136 18 13 141 19 14 141 20 15 146 21 16 149 22 17 149 23 174 18 24 73 19 241,301 25 20 242,301 (1) 26 21 331 27 22 241,341 28 23 242,341
中国の憲法改正
2004 10 13 11 13 14 33 67 80 89 16 10 59 81 98 136 1982 12 1988 1993 1999 20 04 2004 1987 13 1988 1992 14 1993 1997 15 1999 2002 16 2004 60 2002 11 12 20 2003 10 28 2003 6 2003.10.28, 2003, p.16 2002 2002,
プラネット広報誌 『 ＰＬＡＮＥＴvanvan 』 76号(2007年10月発行)
2 3 4 5 6 7 8 9 From PLANET 10 From PLANET N E W F A C E 11 272 273 116 107 22 27 27 239 245 240 124 90 79 188 5 3 241 216 337 45,303 463 57,618 703 255 245 620 483 437 606 597 298 330 467 http //www.planet-van.co.jp
目 次
1 2 3 t 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 IP 169 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67
- 1 - - 0.5%5 10 10 5 10 1 5 1
- - - 1 - - 0.5%5 10 10 5 10 1 5 1 - 2 - - - - A B A A A B A B B A - 3 - - 100 100 100 - A) ( ) B) A) A B A B 110 A B 13 - 4 - A) 36 - - - 5 - - 1 - 6-1 - 7 - - 8 - Q.15 0% 10% 20% 30% 40% 50% 60% 70%
総セク報告書（印刷発出版_.PDF
- 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 8 - - 9 - - 10 - - 11 - IP 110 110 IP 110 110 - 12-110 2 IP 3 1 110 2 IP 3 1 - 13 - - 14 - IP - 15 - 17 11-16 - - 17 - - 18 - FAX (*1) http://www.kantei.go.jp/jp/singi/titeki2/kettei/040527f.html
Gnutella TCP/IP Ping, Query 4. QueryHit 5. HTTP 2
jnutella.org hidekazu.umeda@skyley.com 1. Gnutella Gnutella descriptor) GTKT (Gnutella Toolkit) 2. Jnutella P2P JPPP JPPP SDK Gnutella 1. Gnutella 2. 3. descriptor) 4. GTKT (Gnutella Toolkit) 1 Gnutella
untitled
186 17 100160250 1 10.1 55 2 18.5 6.9 100 38 17 3.2 17 8.4 45 3.9 53 1.6 22 7.3 100 2.3 31 3.4 47 OR OR 3 1.20.76 63.4 2.16 4 38,937101,118 17 17 17 5 1,765 1,424 854 794 108 839 628 173 389 339 57 6 18613
untitled
1. 3 14 2. 1 12 9 7.1 3. 5 10 17 8 5500 4. 6 11 5. 1 12 101977 1 21 45.31982.9.4 79.71996 / 1997 89.21983 41.01902 6. 7 5 10 2004 30 16.8 37.5 3.3 2004 10.0 7.5 37.0 2004 8. 2 7 9. 6 11 46 37 25 55 10.
WIDE 1
WIDE 1 2 Web Web Web Web Web Web Web Web Web Web? Web Web Things to cover Web Web Web Web Caching Proxy 3 Things NOT covered / How to execute Perl Scripts as CGI binaries on Windows NT How to avoid access
untitled
K-Means 1 5 2 K-Means 7 2.1 K-Means.............................. 7 2.2 K-Means.......................... 8 2.3................... 9 3 K-Means 11 3.1.................................. 11 3.2..................................
... 4 HTML IP TC
MFC-7820N ... 4 HTML... 5... 7... 7... 7... 8... 8... 8... 9 1... 11... 12... 13... 13... 14... 15... 15... 16... 18... 18 IP... 19 2... 21... 22... 22... 22 TCP/IP... 23 IP... 24 IP... 25... 26... 27
23
Master's Thesis / 修 士 論 文 映 像 配 信 の 中 断 から 復 旧 までの 時 間 を 短 縮 するネットワーク 再 構 築 手 法 の 改 良 隅 田, 貴 久 三 重 大 学, 2011. 三 重 大 学 大 学 院 地 域 イノベーション 学 研 究 科 博 士 前 期 課 程 地 域 イノベーション 学 専 攻 http://hdl.handle.net/10076/12400
Chapter16
16 Flat Clustering (cluster) 16.1 (unsupervised learning) 16.1 3 17 (13 237 ) (distance measure) 16.1 2 3 (flat clustering) (hierarchical clustering) 17 17 2 (hard) (soft) (Latent semantic indexing) (18
160mm OR16-34 ORB16-34 OR16-35 ORB16-35 OR16-43 ORB16-43 OR16-44 ORB16-44 OR16-45 ORB16-45 OR16-46 ORB16-46 OR16-47 ORB16-47 OR16-48 ORB16-48 OR16-53
IP44 120mm OR12-33 ORB12-33 OR12-34 ORB12-34 OR12-35 ORB12-35 OR12-36 ORB12-36 OR12-43 ORB12-43 OR12-44 ORB12-44 OR12-45 ORB12-45 OR12-46 ORB12-46 OR12-47 ORB12-47 OR12-48 ORB12-48 OR12-49 ORB12-49 OR12-53
PowerPoint プレゼンテーション
1 Agenda Aspera GB ( ) IP FTP IP Aspera FASP Aspera Aspera Aspera FASP FTP Aspera Solution FASP WAN FTP 1. 2. 3. Aspera WAN 1. 2. 3. FASP FASP Aspera FASP UDP FASP UDP TCP Figure 1 FASP Adaptive Rate
Microsoft Word - toyoshima-deim2011.doc
DEIM Forum 2011 E9-4 252-0882 5322 252-0882 5322 E-mail: t09651yt, sashiori, kiyoki @sfc.keio.ac.jp CBIR A Meaning Recognition System for Sign-Logo by Color-Shape-Based Similarity Computations for Images
wireless
133 + 00 Mbps + 1300 600 Mbps 1300 + 40Mbps 66+ 300 Mbps 66+ 300Mbps 11n a 11n g b 300+ 300 Mbps 11n g b 300Mbps 11 12 MAC.11..9...6. SP3 MAC.11..9...6. 3F 40 163 Mbps 116 Mbps % UP! Mbps0 0 0 10 200 3F