thesis.PDF
|
|
|
- 미지 호
- 4 years ago
- Views:
Transcription
1 (Prefetching Parameters)13 34 (Web Traffic Trace)14 35 (Prefetching Time)18 36 (Performance Metrics) Prefetchable Object List Generator 22 i
2 411 HTTP/10 Response Header HTTP/11 Request Header Squid Freshness Request Generator Accuracy41 62 Wasted Bandwidth46 63 Request Saving Bandwidth Saving Summary [ ] 57 ii
3 Prefetchable Object List HTTP/10 Response Header Cache-Control General Header Field STALE Access Log Format Store Log Format iii
4 Off-peak Periods Peak Periods 51 iv
5 1 Cache Summary Statistics Off-peak Periods Cache Byte Hit Ratio with and without Prefetching Peak Periods Performance Mateics Peak Periods Off-peak Periods Peak Periods 53 v
6 vi
7 MCC 9851M08, Sook-Hyang Kim, A Statistical, Batch and Proxy-Initiated Web Prefetching Scheme for Efficient Internet Bandwidth Usage,,, 2000, 60P, Advisor: J Won-Ki Hong, Text in Korean ABSTACT As the number of World-Wide Web (Web) users grows, Web traffic continues to increase at an exponential rate Currently, Web traffic is one of the major components of Internet traffic Also, high bandwidth usage due to Web traffic is observed during peak periods while leaving bandwidth usage idle during off-peak periods One of the solutions to reduce Web traffic and speed up Web access is through the use of Web caching Unfortunately, Web caching has limitations for reducing network bandwidth usage during peak periods In this thesis, we focus our attention on the use of a prefetching algorithm for reducing bandwidth during peak periods by using off-peak period bandwidth We propose a statistical, batch, proxy-side prefetching algorithm that improves cache hit rate while only requiring a small amount of storage We present simulation results based on Web proxy trace and show that this prefetching algorithm can reduce peak time bandwidth using off-peak bandwidth
8 1 World Wide Web( ),,, [20, 21], (Internet traffic) (Web traffic) (bottleneck) [25, 26] (bandwidth) peak periods, off-peak periods 1 16 subnet 2 ( ), 2 1 1
9 2 ( byte) peak periods off-peak periods peak periods 14:00 16:00 18:00 04:00, 12 off-peak periods 04:00 14:00 16:00 18: peak periods off-peak periods peak periods off-peak periods, (Web caching) [10], [11, 12], 2
10 [7, 27, 28] peak peiods off-peak periods, off-peak periods peak periods [4, 5, 11] (caching server) (Web prefethcing) peak periods off-peak periods,, off-peak periods peak periods , 6 7 3
11 4 2 21,, Padmanabhan Mogul [2] (prediction) (prediction algorithm) Griffioen Appletion[13],
12 hyperlink dependency graph graph A B A B arc (edge) arc A B dependecy graph (Web server traces) trace-driven 45% 2 Wang Crowcroft HotList Manager delay [1] deterministic client-initiated prefetching, Deterministic prefetching Bestavros Server-initiated prefetching [14] D i, D j Bestavros D i time interval T w D j p[i,j] advice Bestavros trace-driven 10% 23% cache miss rate 5
13 Kroeger, 60% [15] Chinen Yamaguchi Wcol [3, 17] HTML (parsing) - (pre-pushing) Wcol Jacobson Cao - [18] - (push) (Web access traces) (overhead) (accuracy) - (pre-push scheme) - 10% 18%, (request) 12% Makatos 6
14 Chronak Top-10 Approach [16, 24] client-proxy-server framework Top-10 (Web server trace), 10% 40%,,, 22,, 221,, 7
15 Server-initiated prefetching :, hyperlink (pushing) [2, 14, 15], Client-initiated prefetching : (agent) [1, 2, 13] Client-initiated prefetching (Web access pattern) Proxy-initiated prefetching :, [3, 17, 18] 8
16 222 (statistical prefetching)[5, 8, 9] (deterministic prefetching) [1] Statistical prefetching : (access log) Deterministic prefetching :, 223 (response time) 9
17 Prefetching for Response Time Reduction :, [2, 5, 13, 16, 18] peak periods Prefetching for Balanced Bandwidth Usage : peak periods off-peak periods Batch prefetching balanced bandwidth usage 10
18 3 Server-initiated Prefetching Client-initiated Prefetching peak bandwidth usage, (parameter) real-world traffic pattern 16 (subnet) (performance metrics) 11
19 31??,???? 32?? ( :, 1 )??, (accuracy) prefetchable objects 12
20 (expiration time) (cache miss)?? 33 (Prefetching Parameters),,,,??,?? Traditional prefetching scheme n 13
21 ?? 100M?? off-peak periods peak periods off-peak periods off-peak periods off-peak periods (Web Traffic Trace) real-world traffic pattern 16 (subnet) ( ) ( ) (cache object hit rate) 5496% (cache byte hit rate) 3142% (request) 4,077,308 total bytes 502G bytes 36G 1 14
22 1 Cache Summary Statistics Number of Client Making Requests 447 Cache Object Hit Rate 5496 % Cache Byte Hit Rate 3142 % Total Number of Objects Requested 4,077,308 Total Bytes to Clients 502 Gbyte 2 (reference count) 7075% % 121, (%) 1 86, , , , , Total 121,
23 3 3 3 total size %, % total bytes Mbyte 4 3 (Mbyte) (%) Total
24 4 5 6 total bytes
25 35 (Prefetching Time) off-peak periods peak periods off-peak periods off-peak periods off-peak periods off-peak periods off-peak periods off-peak periods 80% ( byte) off-peak peirods off-peak periods 04:00 13:00, peakperiods 13:00 04:00 off-peak periods off-peak periods (04:00 13:00) prefetchable object 7 80% 7 18
26 36 (Performance Metrics),?? Request Saving : hit Request saving?? Bandwidth Saving : Bandwidth saving peak periods bandwidth usage peak periods peak periods?? Accuracy : accuracy (prefetched object hit rate) (prefetched byte hit rate) total bytes total bytes Accuracy?? Wasted Bandwidth : 19
27 offpeak periods off-peak periods 20
28 4 8 Prefetching system Caching Server prefetchable object list Prefetchable Object List Generator accesslog storelog cache Request Generator Caching Server clients request response request response request response Internet 8, Squid[7] Squid freeware, Cobalt Network CacheRaQ[29] Packetstorm Technologies Webspeed[30] Squid Squid 21
29 Squid transparent Transparent (browser) (data path) [31] configuration default gateway Prefetchable Object List Generator Request Generator (Squid) Squid, Squid request access log, store log squid (accesslog, storelog) Prefetchable Object List Generator prefetchable object list Request Generator off-peak periods prefetchable object list (request) Squid Squid add-on 41 Prefetchable Object List Generator Prefetchable Object List Generator 22
30 Squid access log store log access log Squid requests store log Prefetch Object List Generator Squid (refresh algorithm) prefetchable object HTTP response message Squid (3 days) [7] 9 prefetchable object list 9 Prefetchable Object List Prefetchable Object List Generator Squid, store log store log HTTP response header HTTP response header HTTP request header Squid 23
31 411 HTTP/10 Response Header HTTP [22, 23, 33] / (Request/Response), ( : GET, HEAD, POST) TCP,, TCP HTTP method, URI, protocol version,,,,,, HTTP response header HTTP/10 response header 10 Full-Response = Status-Line *(General-Header Response-Header Entity-Header) CRLF [Entity-Body] Status-Line = HTTP-Version Status-Code Reason-Phrase CRLF General-Header = Date Progma Response-Header = Location Server WWW-Authenticate Entity-Header = Allow Content-Encoding Content-Length Content-type Expires Last-Modified extension-header extension-header = HTTP-header 10 HTTP/10 Response Header 24
32 Full-Response Status-Line HTTP (Status-Code) (Reason-Phrase) CRLF Entity CRLF Status-Code?? 1xx : Informational -?? 2xx : Success -?? 3xx : Redirection -?? 4xx : Client Error -?? 5xx : Server Error - (response message), General-Header, Request-Header, Response-Header, Entity-Header RFC [32] 25
33 General Header Full-Request Full-Response, General Header Date Progma Date Progma / Response Header Status-Line Request-URI Entity-Header Entity-Body,,, (metainformation), entity body Squid HTTP response header?? Date : General Header Date, RFC 822[32] orig-date Date : Wed, 15 Oct :12:31 GMT?? Expires : Entity Header Expires, 26
34 Expires Expires : Mon, 01 Nov :00:00 GMT?? Last-Modified : Entity Header Last-Modified Last-Modified Last-Modified : Tue, 14 Oct :45:26 GMT 412 HTTP/11 Request Header HTTP/10, URL TCP / / congestion congestion 27
35 , HTTP/11 [23] HTTP/11, Squid Cache-Control Request Header max-age HTTP/10 HTTP HTTP/11, HTTP/11 General Header Cache-Control Cache-Control (directive) (cache directive) Cache-Control cache-response-diective Cache-Control Cache-Control = "Cache-Control" ":" 1#cache-directive cache-directive = cache-request-directive cache-response-directive cache-request-directive = "no-cache" "no-store "max-age" "=" delta-seconds "max-stale" [ "=" delta-seconds ] "min-fresh" "=" delta-seconds "no-transform" "only-if-cached" cache-extension cache-extension = token [ "=" ( token quoted-string ) ] 11 Cache -Control General Header Field 28
36 cache-request-directive max-age Squid (maximum object age) max-age, max-age max-age Squid CLIENT_MAX_AGE 413 Squid, (expiration time) (cache miss) Squid 13 Squid default value OBJ_DATE OBJ_LASTMOD Expires OBJ_DATE, OBJ_LASTMOD, Expires HTTP Respons Header [22] CLIENT_MAX_AGE HTTP/11 Cache-Control Request Header 29
37 [23] Squid refresh_pattern CONF_MIN, CONF_PERCENT, CONF_MAX squidconf HTTP Response Header Expires default Expires [7] NOW, OBJ_AGE LM_AGE LM_FACTOR OBJ_AGE LM_AGE 12 OBJ_DATE, Expires, OBJ_LASTMOD millisecond UNIX time ( : Fri Oct 15 04:00: GMT = ) CLIENT_MAX_AGE, OBJ_AGE, LM_AGE, LM_FACTOR second CONF_MAX CONF_MIN minute CONF_PERCENT % OBJ_DATE Expires OBJ_LASTMOD : : : CLIENT_MAX_AGE : OBJ_AGE = NOW - OBJ_DATE (sec) LM_AGE = OBJ_DATE - OBJ_LASTMOD (sec) LM_FACTOR = OBJ_AGE / LM_AGE (sec) CONF_MAX CONF_MIN : 4320 (min, 3 days) : 0 (min) CONF_PERCENT : 02 (20%) 12 30
38 Is CLIENT_MAX_AGE present in the request? yes Is OBJ_AGE more than CLIENT_MAX_AGE? yes STALE(1) no no Is Expires present in the response? yes Has the object already expired? yes no STALE(2) FRESH no Is OBJ_AGE more than CONF_MAX? yes STALE(3) no Is OBJ_DATE more than OBJ_LASTMOD? yes LM_FACTOR less than the CONF_PERCENT? yes no FRESH STALE(4) no Is OBJ_AGE less than CONF_MIN? yes no FRESH STALE(5) STALE 5 Expires, OBJ_DATE, OBJ_LASTMOD, NOW HTTP CLIENT_MAX_AGE 100 sec OBJ_AGE 120 sec OBJ_AGE CLIENT_MAX_AGE STALE (STALE(1)) Expires NOW, (STALE(2)) OBJ_AGE CONF_MAX (STALE(3)) 3 OBJ_DATE OBJ_LASTMOD LM_FACTOR (30%) CONF_PERCENT (20%) (STALE(4)) 31
39 OBJ_AGE (5760 min) CONF_MIN (0 min) STALE (STALE(5)) STALE(1) CLIENT_MAX_AGE : 100 (sec) OBJ_AGE : 120 (sec) STALE(2) EXPIRES : Fri, 01 Oct :00:00 GMT NOW : Sun, 03 Oct :01:20 GMT STALE(3) OBJ_AGE : 5760 min (4 days) CONF_MAX :4320 min (3 days) STALE(4) OBJ_LASTMOD : Wed, 29 Sep :00:00 GMT OBJ_DATE : Fri, 01 Oct :00:00 GMT LM_FACTOR : 30 % CONF_PERCENT : 20 % STALE(5) OBJ_AGE : 5760 min (4 days) CONF_MIN : 0 min 14 STALE 414 Freshness FRESH, STALE STALE squid OBJ_DATE, 32
40 OBJ_LASTMOD, Expires squid storelog, CONF_MAX, CONF_MIN, CONF_PERCENT squid squidconf CLIENT_MAX_AGE 15 Squid access log, 16 Squid store log 15 Access Log Format?? Time UNIX time milliseconds?? Elapsed (connection)?? Remotehost IP?? Code/Status Code ( : TCP_HIT, TCP_MISS, etc) Status HTTP status code?? Byte?? Method HTTP request Method ( : GET, HEAD, POST)?? URL URL 33
41 Time Action Status OBJ_DATE OBJ_LASTMOD Expires Type Len Method URL 16 Store Log Format store log Time, Status, Method, URL access log OBJ_DATE, OBJ_LASTMOD, Expires 413?? Action RELASE, SWPIN, SWPOUT RELASE SWAPOUT SWAPIN?? Type, text/html, image/gif?? Len expect-len real-len, expect-len HTTP Content-Length Response Header, real-len NOW, fresh stale access log stale stale Squid NOW 34
42 NOW? Current Pr efetchngtime? PrefetchingFrequency Current PrefetchingTime : Pr efetchingfrequency : off-peak periods (prefetching frequency) , (16 04:00) : :00 access log store log Last prefetching time Currnt Prefetching time Next prefetching time Input Data (logs) Prefetching Frequency 10/15 4:00 10/16 4:00 10/17 4:
43 42 Request Generator Request generator Prefetchable Obejct List off-peak periods Prefetchable Object List Generator Prefetchable Object List request off-peak periods request HTTP command-line Web client wget [19] crontab off-peak periods Request Generator Off-peak periods off-peak WAN SNMP agent SNMP agent (polling), 36
44 5 (input parameter) 51 trace-driven CacheRaQ[29] 16 (subnet) Squid (input parameter)?? Logs : access log store log?? Off-peak periods : 35 off-peak periods 04:00 13:00 04:00 37
45 ?? : 33 52?? : Squid, 413 NOW (Web traffic trace),, (Prefetchable Web Objects) (expiration time) 1, 2, 3, 4, 5 38
46 off-peak periods Prefetchable Object List Generator off-peak periods prefetchable object list performance analyzer prefetchable object list request saving bandwidth saving accracy wasted bandwidth Prefetchable Object List Generator access log store log prefetchable object list 39
47 Simulation Model accesslog storelog Prefetchable Object List Generator Prefetchable Object List Performance Analyzer Performance Metrics With Prefetching Performance Metrics Without Prefetching 18 Performance Analyzer prefetchable object list access log prefetchable object list access log request saving ( ), bandwidth saving ( ) accuracy (, ) access log wasted bandwidth request saving bandwidth saving accuracy wasted bandwidth 40
48 6 3 accuracy wasted bandwidth, performance parameter request saving bandwidth saving 61 Accuracy Accuracy daily prefetchable object list % % 1 prefetchable object 5064% 1955% linear 41
49 4 (%) (%) 1 16,274 (5064 %) 2,476 (1955 %) 1522 % 2 5,525 ( 172 %) 2,286 (1805 %) 4138 % 3 3,008 ( 936 %) 1,804 (1424 %) 5996 % 4 1,966 ( 612 %) 1,430 (1129 %) 7276 % 5 5,358 (1668 %) 4,469 (3687 %) 8714 % Total 32,131 (10000 %) 12,665 (10000 %) 3942 % 19 prefetched object hit
50 % % accuracy 5 (%) (%) 1 545,399,428 (6343%) 47,336,276 (2502%) 868 % 2 154,356,860 (1795%) 56,170,230 ( 297%) 3639 % 3 64,124,072 ( 746%) 25,570,556 (1352%) 3989 % 4 33,972,272 ( 395%) 15,026,692 ( 794%) 4423 % 5 62,024,975 ( 721%) 45,057,416 (2382%) 7264 % Total 859,877,607 (100%) 189,161,170 (100%) 22 % 21 43
51 , 2, 3,
52 2 6426% %25% 2 461% 1 22% % 22 % % 451% % 535 % % 626 % % 726 % 23 45
53 62 Wasted Bandwidth Wasted bandwidth Mbyte ( Mbyte) 178% Mbyte 46% M 178 % M 46 % M 20 % M 10 % M 05 % off-peak periods off-peak periods off-peak Mbyte 1 off-peak 519%, off-peak periods 2 283%, 3 167%, 4 108%, 5 72% 8 off-peak periods 24 46
54 8 Off-peak Periods Off-peak periods % % % % 5 72 % 24 Off-peak Periods 63 Request Saving 9 55%, 1 47
55 43% 593% %, 3 272%, 4 21%, 5 161% % 9 ( ) 55 % ( + ) ( 1 ) 593 % ( + ) ( 2 ) 585 % ( + ) ( 3 ) 577 % ( + ) ( 4 ) 571 % ( + ) ( 5 ) 567 %
56 64 Bandwidth Saving %, 1 501%, %, 3 227%, 4 159%, 5 119% % 10 ( ) 314 % ( + ) ( 1 ) 364 % ( + ) ( 2 ) 352 % ( + ) ( 3 ) 337 % ( + ) ( 4 ) 330 % ( + ) ( 5 ) 326 % 26 Request saving 49
57 26 peak periods 11 peak periods Peak Periods Peak periods 1 64 % 2 48 % 3 29 % 4 20 % 5 15 % 50
58 27 Peak Periods 65 Summary 12 Request saving 12 Performance Mateics Bandwidth saving Wasted bandwidth Prefetched object hit rate Accuracy Prefetched byte hit rate % 501 % 178 % 394 % 22 % % 378 % 46 % 643 % 451% % 227 % 20 % 765 % 535 % % 159 % 10 % 833 % 626 % % 119 % 05 % 871 % 726 % off-peak periods peak periods 51
59 off-peak periods peak periods Peak Periods Off-peak Periods Peak periods off-peak periods 1 64 % 519 % 2 48 % 283 % 3 29 % 167 % 4 20 % 108 % 5 15 % 72 % E p? B s / B w E B B s p w : : ( Mbyte) : ( Mbyte) 1, 2, 3, 4, 5, 52
60 peak periods peak periods peak periods peak periods 4% peak periods Peak Periods B s (Mbyte) B w (Mbyte) E p (Mbyte) Peak periods % % % % % 14 peak periods 4% 1, peak periods 4% 2 53
61 2 141,824,895 bytes peak periods 54,000 seconds (19 hours) peak periods peak periods = ( *8)/54000 = 2052 Kbps 2 peak periods 2052 Kbps 54
62 7 peak periods, off-peak periods peak periods request saving 353% bandwidth saving 378% %, 451% peak bandwidth usage 2052 Kbps peak periods prefetching scheme squid add-on feature 55
63 Web traffic trace off-peak periods (eg, 20:00~08:00),, real-time prefetchable object list 56
64 [1] Z Wang and J Crowcroft, Prefetching in World Wide Web, IEEE Globecom 96, [2] V Padmanabhan and J Mogul, Using Predictive Prefetching to Improve World Wide Web Latency, Computer Communication Review, 26(3):22-36, July 1996 [3] Ken-ichi Chinen and Suguru Yanaguchi, An Interactive Prefetching Proxy Server for Improvement of WWW Latency, INET 97, 1997, [4] Arthur Goldberg, Ilya Pevzner and Robert Buff, Caching Characteristic of Internet and Intranet Web proxy Traces, In Computer Measurement Group Conference (CMG 98), Anaheim, CA, December 1998, [5] David Barnes and Neil G Smith, An Analysis of World-Wide Web Proxy Cache Performance and its Application to the Modelling and Simulation of Network Traffic, In Proceedings of the Fourth International Conference on Telecommunication Systems Modeling and Analysis, March 1996, [6] Themistoklis Palpanas and Alberto Mendelzon, Web Prefetching Using Partial Match Prediction, In Web Caching Workshop WCW 99, 1999, [7] Squid Internet Object Cache, available from [8] Gihan VDias, Graham Cope and Ravi Wijayaratne, A Smart Internet Caching System, INET 96 Conference, 1996, whatis/conferences/inet/96/proceedings/a4/a4_3htm [9] Katsuo Doi, WWW Access by Proactively Controlled Caching Proxy, Sharp Technical Journal, No 66, December 1996 [10] Brad Duska, David Marwood, and Michael JFeeley, The Measured Access Characteristics of World-Wide Web Client Proxy Caches, In Usenix Symposium on Internet Technologies and Systems (USITS), Monterey, CA, USA, December , Usenix, Projects/SPA/Report/Reporthtml 57
65 [11] Marc Abrams, CRStandridge, GAbdulla, SWilliams, and EAFox, Caching Proxies: Limitations and Potentials, In Proceedings of the Fourth International WWW Conference, 1995, WWW4html [12] Anawat Chankhunthod et al, A Hierarchical Internet Object Cache, Technical Conference, Usenix 1996, [13] James Griffioen and Randy Appleton Reducing File System Latency using a Predictive Approach, Proceddings of the 1994 Summer USENIX Technical Conference, Boston, Massachusetts, USA, 1994, [14] Azer Bestavros, Speculative Data Dissemination and Service to Reduce Server Load, Network Traffic and Service Tome in Distributed Information System, In International Conference on Data Engineering, pages , New Orleans, LO, February 1996 [15] Tomas M Kroeger, Darrell D E Long, and Jeffrey C Mogul Exploring the bounds of web latency reduction from caching and prefetching, In Proceedings of USENIX Symposium on Internet Technology and Systems, December 1997, usits97/kroegerhtml [16] Evangelos P Margatos and Catherine E Chronaki, A top-10 Approach to Prefetching on the Web, Technical report, In Proceedings of INET' 98 (The Internet Summit), Geneva, Switzerland, July 1998, proj/arch-vlsi/os/wwwhtml [17] Wcol Group, WWW Collector the prefetching proxy server for WWW, 1997, [18] Li Fan, Quinn Jacobson, Pei Cao, and Wei Lin, Web Prefetching Between Low-Bandwidth Clients and Proxies: Potential and Performance, In Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS '99), Atlanta, GA, May 1999, [19] Wget, available from 142/wget_tochtml [20] Tim Bray, Measuring the Web, In Proceedings of the Fifth International 58
66 World Wide Web Conference, pages , Paris, France, May 1996 [21] Allison Woodruff, Paul M Aoki, Eric Brewer, Paul Gauthier, and Lawrence A Rowe, An Investigation of Documents from the WWW, In Proceedings of the Fifth International WWW Conference, pages , Paris, France, May 1996 [22] T Berners-Lee, R Fielding, and H Frystyk, Hypertext Transfer Protocol HTTP/10, RFC 1945, May, 1996 [23] R Fielding, J Gettys, J Mogul, H Frystyk, L Masinter, P Leach and T Berners-Lee, Hypertext Transfer Protocol - HTTP/11, RFC 2616, June 1999 [24] Evangelos Markatos, Catherine E Chronaki, A Top-10 Approach to Prefetching on the Web, Technical Report No 173, ICS-FORTH, Heraklion, Crete, Greece,August 1996 [25] Ghaleb Abdulla, Edward A Fox, Marc Abrams, and Stephen Williams, WWW Proxy Traffic Characterization with Application to Caching, Technical Report TR-97-03, Computer Science Department, Virginia Tech, March 1997, [26] James E Pitkow, Summary of WWW characterizations, In Proceedings of the Seventh International World Wide Web Conference, Brisbane, Australia, April 1998, com1877htm [27] Pei Cao, Edward W Felten, Anna R Karlin, and Kai Li, A Study of Integrated Prefetching and Caching Strategies, In Proceedings of the ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems, May 1995, [28] Eric A Brewer, Paul Gauthier, and Dennis McEvoy, The long-term viability of large-scale caching, In Proceedings of the Third International WWW Caching Workshop, Manchester, England, June 1998, [29] CacheRaQ of Cobalt Network, available from [30] Webspeed of Packetstorm Technologies, available from [31] Bert Williams, Transparent web caching solutions, In Proceedings of the Third International WWW Caching Workshop, Manchester, England, June 59
67 [32] David H Crocker, Standard For The Format Of Arpa Internet Text Message, RFC 822, August 13, 1982 [33], Protocals for the World-Wide Web, available form 60
68 61
thesis-shk
DPNM Lab, GSIT, POSTECH Email: shk@postech.ac.kr 1 2 (1) Internet World-Wide Web Web traffic Peak periods off-peak periods peak periods off-peak periods 3 (2) off-peak peak Web caching network traffic
1217 WebTrafMon II
(1/28) (2/28) (10 Mbps ) Video, Audio. (3/28) 10 ~ 15 ( : telnet, ftp ),, (4/28) UDP/TCP (5/28) centralized environment packet header information analysis network traffic data, capture presentation network
DBPIA-NURIMEDIA
논문 10-35-03-03 한국통신학회논문지 '10-03 Vol. 35 No. 3 원활한 채널 변경을 지원하는 효율적인 IPTV 채널 관리 알고리즘 준회원 주 현 철*, 정회원 송 황 준* Effective IPTV Channel Control Algorithm Supporting Smooth Channel Zapping HyunChul Joo* Associate
°í¼®ÁÖ Ãâ·Â
Performance Optimization of SCTP in Wireless Internet Environments The existing works on Stream Control Transmission Protocol (SCTP) was focused on the fixed network environment. However, the number of
PowerPoint 프레젠테이션
Reasons for Poor Performance Programs 60% Design 20% System 2.5% Database 17.5% Source: ORACLE Performance Tuning 1 SMS TOOL DBA Monitoring TOOL Administration TOOL Performance Insight Backup SQL TUNING
Intra_DW_Ch4.PDF
The Intranet Data Warehouse Richard Tanler Ch4 : Online Analytic Processing: From Data To Information 2000. 4. 14 All rights reserved OLAP OLAP OLAP OLAP OLAP OLAP is a label, rather than a technology
SMB_ICMP_UDP(huichang).PDF
SMB(Server Message Block) UDP(User Datagram Protocol) ICMP(Internet Control Message Protocol) SMB (Server Message Block) SMB? : Microsoft IBM, Intel,. Unix NFS. SMB client/server. Client server request
Voice Portal using Oracle 9i AS Wireless
Voice Portal Platform using Oracle9iAS Wireless 20020829 Oracle Technology Day 1 Contents Introduction Voice Portal Voice Web Voice XML Voice Portal Platform using Oracle9iAS Wireless Voice Portal Video
<C0CCBCBCBFB52DC1A4B4EBBFF82DBCAEBBE7B3EDB9AE2D313939392D382E687770>
i ii iii iv v vi 1 2 3 4 가상대학 시스템의 국내외 현황 조사 가상대학 플랫폼 개발 이상적인 가상대학시스템의 미래상 제안 5 웹-기반 가상대학 시스템 전통적인 교수 방법 시간/공간 제약을 극복한 학습동기 부여 교수의 일방적인 내용전달 교수와 학생간의 상호작용 동료 학생들 간의 상호작용 가상대학 운영 공지사항,강의록 자료실, 메모 질의응답,
[ReadyToCameral]RUF¹öÆÛ(CSTA02-29).hwp
![[ReadyToCameral]RUF¹öÆÛ(CSTA02-29).hwp](/thumbs/80/82353018.jpg "[ReadyToCameral]RUF¹öÆÛ(CSTA02-29).hwp")
RUF * (A Simple and Efficient Antialiasing Method with the RUF buffer) (, Byung-Uck Kim) (Yonsei Univ. Depth of Computer Science) (, Woo-Chan Park) (Yonsei Univ. Depth of Computer Science) (, Sung-Bong
歯최덕재.PDF
ISP Monitoring Tool OSPF SNMP, Metric MIB OSPFECMP 1 11 [6], Metric ISP(Internet Service Provider) Monitoring Tool, [5] , (Network Management System) SNMP ECMP Cost OSPF ECMP IGP(Interior Gateway Protocol)
SLA QoS
SLA QoS 2002. 12. 13 Email: really97@postech.ac.kr QoS QoS SLA POS-SLMS (-Service Level Monitoring System) SLA (Service Level Agreement) SLA SLA TM Forum SLA QoS QoS SLA SLA QoS QoS SLA POS-SLMS ( Service
DBPIA-NURIMEDIA
무선 센서 네트워크 환경에서 링크 품질에 기반한 라우팅에 대한 효과적인 싱크홀 공격 탐지 기법 901 무선 센서 네트워크 환경에서 링크 품질에 기반한 라우팅에 대한 효과적인 싱크홀 공격 탐지 기법 (A Effective Sinkhole Attack Detection Mechanism for LQI based Routing in WSN) 최병구 조응준 (Byung
Remote UI Guide
Remote UI KOR Remote UI Remote UI PDF Adobe Reader/Adobe Acrobat Reader. Adobe Reader/Adobe Acrobat Reader Adobe Systems Incorporated.. Canon. Remote UI GIF Adobe Systems Incorporated Photoshop. ..........................................................
歯홍원기.PDF
WWW (World-Wide Web), 1, 1969 ARPANET[1], MRTG[2], Etherfind[3],, WWW TCPdump[4], WebTrafMon[5] (World-Wide Web) WWW MIB SNMP agent SNMP manager,, SNMP agent SNMP manager , NT manager, [8], WebTrafMon[5]
학습영역의 Taxonomy에 기초한 CD-ROM Title의 효과분석
,, Even the short history of the Web system, the techniques related to the Web system have b een developed rapidly. Yet, the quality of the Webbased application software has not improved. For this reason,
#Ȳ¿ë¼®
http://www.kbc.go.kr/ A B yk u δ = 2u k 1 = yk u = 0. 659 2nu k = 1 k k 1 n yk k Abstract Web Repertoire and Concentration Rate : Analysing Web Traffic Data Yong - Suk Hwang (Research
Cache_cny.ppt [읽기 전용]
![Cache_cny.ppt [읽기 전용]](/thumbs/103/157709625.jpg "Cache_cny.ppt [읽기 전용]")
Application Server iplatform Oracle9 A P P L I C A T I O N S E R V E R i Improving Performance and Scalability with Oracle9iAS Cache Oracle9i Application Server Cache... Oracle9i Application Server Web
µðÇÃÇ¥Áö±¤°í´Ü¸é
Review 2 2013 JAN.FEB. vol. 23 Display Focus 3 Review 4 2013 JAN.FEB. vol. 23 Display Focus 5 Review 6 2013 JAN.FEB. vol. 23 Display Focus 7 Review 8 2013 JAN.FEB. vol. 23 Display Focus 9 Preview 2013.1
High Resolution Disparity Map Generation Using TOF Depth Camera In this paper, we propose a high-resolution disparity map generation method using a lo
High Resolution Disparity Map Generation Using TOF Depth Camera In this paper, we propose a high-resolution disparity map generation method using a low-resolution Time-Of- Flight (TOF) depth camera and
SK IoT IoT SK IoT onem2m OIC IoT onem2m LG IoT SK IoT KAIST NCSoft Yo Studio tidev kr 5 SK IoT DMB SK IoT A M LG SDS 6 OS API 7 ios API API BaaS Backend as a Service IoT IoT ThingPlug SK IoT SK M2M M2M
¼Ł¿ï¸ðµåÃÖÁ¾
Fashion Fashion Blue ocean Passion Chance Contents Blue ocean Fashion Passion Contents Chance Fashion Blue ocean Blue ocean 003 Blue ocean 004 Fashion Blue ocean 005 Blue ocean http://blog.naver.com/klcblog?redirect=log&logno=90041062323
ÀÌÀç¿ë Ãâ·Â
Analysis on Smart TV Services and Future Strategies TV industry has tried to realize a long-cherished dream of making TVs more than just display devices. Such efforts were demonstrated with the internet
Software Requirrment Analysis를 위한 정보 검색 기술의 응용
EPG 정보 검색을 위한 예제 기반 자연어 대화 시스템 김석환 * 이청재 정상근 이근배 포항공과대학교 컴퓨터공학과 지능소프트웨어연구실 {megaup, lcj80, hugman, gblee}@postech.ac.kr An Example-Based Natural Language System for EPG Information Access Seokhwan Kim
TTA Verified : HomeGateway :, : (NEtwork Testing Team)
TTA Verified : HomeGateway :, : (NEtwork Testing Team) : TTA-V-N-05-006-CC11 TTA Verified :2006 6 27 : 01 : 2005 7 18 : 2/15 00 01 2005 7 18 2006 6 27 6 7 9 Ethernet (VLAN, QoS, FTP ) (, ) : TTA-V-N-05-006-CC11
THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. vol. 29, no. 10, Oct ,,. 0.5 %.., cm mm FR4 (ε r =4.4)
THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. 2018 Oct.; 29(10), 799 804. http://dx.doi.org/10.5515/kjkiees.2018.29.10.799 ISSN 1226-3133 (Print) ISSN 2288-226X (Online) Method
3ÆÄÆ®-14
chapter 14 HTTP >>> 535 Part 3 _ 1 L i Sting using System; using System.Net; using System.Text; class DownloadDataTest public static void Main (string[] argv) WebClient wc = new WebClient(); byte[] response
À±½Â¿í Ãâ·Â
Representation, Encoding and Intermediate View Interpolation Methods for Multi-view Video Using Layered Depth Images The multi-view video is a collection of multiple videos, capturing the same scene at
歯1.PDF
200176 .,.,.,. 5... 1/2. /. / 2. . 293.33 (54.32%), 65.54(12.13%), / 53.80(9.96%), 25.60(4.74%), 5.22(0.97%). / 3 S (1997)14.59% (1971) 10%, (1977).5%~11.5%, (1986)
µðÇÃÇ¥Áö±¤°í´Ü¸é
2013. JAN. FEB. VOL.23 2013. JAN. FEB. VOL.23 Review Preview Company Technical Point Focus Issue Market Trend Industrial Trend Policy Report KDIA News Tour Statistics KDIA 02 10 11 12 15 16 22 28 36 38
untitled
CAN BUS RS232 Line Ethernet CAN H/W FIFO RS232 FIFO IP ARP CAN S/W FIFO TERMINAL Emulator COMMAND Interpreter ICMP TCP UDP PROTOCOL Converter TELNET DHCP C2E SW1 CAN RS232 RJ45 Power
VOL.76.2008/2 Technical SmartPlant Materials - Document Management SmartPlant Materials에서 기본적인 Document를 관리하고자 할 때 필요한 세팅, 파일 업로드 방법 그리고 Path Type인 Ph
인터그래프코리아(주)뉴스레터 통권 제76회 비매품 News Letters Information Systems for the plant Lifecycle Proccess Power & Marine Intergraph 2008 Contents Intergraph 2008 SmartPlant Materials Customer Status 인터그래프(주) 파트너사
SRC PLUS 제어기 MANUAL
,,,, DE FIN E I N T R E A L L O C E N D SU B E N D S U B M O TIO
PowerChute Personal Edition v3.1.0 에이전트 사용 설명서
PowerChute Personal Edition v3.1.0 990-3772D-019 4/2019 Schneider Electric IT Corporation Schneider Electric IT Corporation.. Schneider Electric IT Corporation,,,.,. Schneider Electric IT Corporation..
thesis
( Design and Implementation of a Generalized Management Information Repository Service for Network and System Management ) ssp@nile nile.postech.ac..ac.kr DPE Lab. 1997 12 16 GMIRS GMIRS GMIRS prototype
ORANGE FOR ORACLE V4.0 INSTALLATION GUIDE (Online Upgrade) ORANGE CONFIGURATION ADMIN O
Orange for ORACLE V4.0 Installation Guide ORANGE FOR ORACLE V4.0 INSTALLATION GUIDE...1 1....2 1.1...2 1.2...2 1.2.1...2 1.2.2 (Online Upgrade)...11 1.3 ORANGE CONFIGURATION ADMIN...12 1.3.1 Orange Configuration
09È«¼®¿µ 5~152s
Korean Journal of Remote Sensing, Vol.23, No.2, 2007, pp.45~52 Measurement of Backscattering Coefficients of Rice Canopy Using a Ground Polarimetric Scatterometer System Suk-Young Hong*, Jin-Young Hong**,
thesis
CORBA TMN Surveillance System DPNM Lab, GSIT, POSTECH Email: mnd@postech.ac.kr Contents Motivation & Goal Related Work CORBA TMN Surveillance System Implementation Conclusion & Future Work 2 Motivation
인문사회과학기술융합학회
Vol.5, No.5, October (2015), pp.471-479 http://dx.doi.org/10.14257/ajmahs.2015.10.50 스마트온실을 위한 가상 외부기상측정시스템 개발 한새론 1), 이재수 2), 홍영기 3), 김국환 4), 김성기 5), 김상철 6) Development of Virtual Ambient Weather Measurement
Mstage.PDF
Wap Push June, 2001 Contents About Mstage What is the Wap Push? SMS vs. Push Wap push Operation Wap push Architecture Wap push Wap push Wap push Example Company Outline : (Mstage co., Ltd.) : : 1999.5
ARMBOOT 1
100% 2003222 : : : () PGPnet 1 (Sniffer) 1, 2,,, (Sniffer), (Sniffer),, (Expert) 3, (Dashboard), (Host Table), (Matrix), (ART, Application Response Time), (History), (Protocol Distribution), 1 (Select
I
I II III (C B ) (C L ) (HL) Min c ij x ij f i y i i H j H i H s.t. y i 1, k K, i W k C B C L p (HL) x ij y i, i H, k K i, j W k x ij y i {0,1}, i, j H. K W k k H K i i f i i d ij i j r ij i j c ij r ij
04-다시_고속철도61~80p
Approach for Value Improvement to Increase High-speed Railway Speed An effective way to develop a highly competitive system is to create a new market place that can create new values. Creating tools and
07.... 01V28.
National Election Commission 9 September S M T W T F S 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23/30 24 25 26 27 28 29 11 November S M T W T F S 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Interstage5 SOAP서비스 설정 가이드
Interstage 5 Application Server ( Solaris ) SOAP Service Internet Sample Test SOAP Server Application SOAP Client Application CORBA/SOAP Server Gateway CORBA/SOAP Gateway Client INTERSTAGE SOAP Service
0. 들어가기 전
컴퓨터네트워크 14 장. 웹 (WWW) (3) - HTTP 1 이번시간의학습목표 HTTP 의요청 / 응답메시지의구조와동작원리이해 2 요청과응답 (1) HTTP (HyperText Transfer Protocol) 웹브라우저는 URL 을이용원하는자원표현 HTTP 메소드 (method) 를이용하여데이터를요청 (GET) 하거나, 회신 (POST) 요청과응답 요청
6.24-9년 6월
리눅스 환경에서Solid-State Disk 성능 최적화를 위한 디스크 입출력요구 변환 계층 김태웅 류준길 박찬익 Taewoong Kim Junkil Ryu Chanik Park 포항공과대학교 컴퓨터공학과 {ehoto, lancer, cipark}@postech.ac.kr 요약 SSD(Solid-State Disk)는 여러 개의 낸드 플래시 메모리들로 구성된
Intro to Servlet, EJB, JSP, WS
! Introduction to J2EE (2) - EJB, Web Services J2EE iseminar.. 1544-3355 ( ) iseminar Chat. 1 Who Are We? Business Solutions Consultant Oracle Application Server 10g Business Solutions Consultant Oracle10g
<35335FBCDBC7D1C1A42DB8E2B8AEBDBAC5CDC0C720C0FCB1E2C0FB20C6AFBCBA20BAD0BCAE2E687770>
Journal of the Korea Academia-Industrial cooperation Society Vol. 15, No. 2 pp. 1051-1058, 2014 http://dx.doi.org/10.5762/kais.2014.15.2.1051 멤리스터의 전기적 특성 분석을 위한 PSPICE 회로 해석 김부강 1, 박호종 2, 박용수 3, 송한정 1*
05(533-537) CPLV12-04.hwp
모바일 OS 환경의 사용자 반응성 향상 기법 533 모바일 OS 환경의 사용자 반응성 향상 기법 (Enhancing Interactivity in Mobile Operating Systems) 배선욱 김정한 (Sunwook Bae) 엄영익 (Young Ik Eom) (Junghan Kim) 요 약 사용자 반응성은 컴퓨팅 시스템에서 가장 중요 한 요소 중에 하나이고,
UDP Flooding Attack 공격과 방어
황 교 국 (fullc0de@gmail.com) SK Infosec Co., Inc MSS Biz. Security Center Table of Contents 1. 소개...3 2. 공격 관련 Protocols Overview...3 2.1. UDP Protocol...3 2.2. ICMP Protocol...4 3. UDP Flood Test Environment...5
I T C o t e n s P r o v i d e r h t t p : / / w w w. h a n b i t b o o k. c o. k r
I T C o t e n s P r o v i d e r h t t p : / / w w w. h a n b i t b o o k. c o. k r I T C o t e n s P r o v i d e r h t t p : / / w w w. h a n b i t b o o k. c o. k r Jakarta is a Project of the Apache
` Companies need to play various roles as the network of supply chain gradually expands. Companies are required to form a supply chain with outsourcing or partnerships since a company can not
http://www.kbc.go.kr/pds/2.html Abstract Exploring the Relationship Between the Traditional Media Use and the Internet Use Mee-Eun Kang This study examines the relationship between
PBNM CIM(Common Information Model) DEN, COPS LDAP 21 CIM (Common Information Model) CIM, specification schema [7]
![PBNM CIM(Common Information Model) DEN, COPS LDAP 21 CIM (Common Information Model) CIM, specification schema [7]](/thumbs/88/115085650.jpg "PBNM CIM(Common Information Model) DEN, COPS LDAP 21 CIM (Common Information Model) CIM, specification schema [7]")
(Policy-Based Network Management Technology) ((ksok, dsyun)@ktcokr) PBNM CIM(Common Information Model) DEN, COPS LDAP 21 CIM (Common Information Model) CIM, specification schema [7] 1 CIM core model hierarchy
<313630313032C6AFC1FD28B1C7C7F5C1DF292E687770>
양성자가속기연구센터 양성자가속기 개발 및 운영현황 DOI: 10.3938/PhiT.25.001 권혁중 김한성 Development and Operational Status of the Proton Linear Accelerator at the KOMAC Hyeok-Jung KWON and Han-Sung KIM A 100-MeV proton linear accelerator
API STORE 키발급및 API 사용가이드 Document Information 문서명 : API STORE 언어별 Client 사용가이드작성자 : 작성일 : 업무영역 : 버전 : 1 st Draft. 서브시스템 : 문서번호 : 단계 : Docum
API STORE 키발급및 API 사용가이드 Document Information 문서명 : API STORE 언어별 Client 사용가이드작성자 : 작성일 : 2012.11.23 업무영역 : 버전 : 1 st Draft. 서브시스템 : 문서번호 : 단계 : Document Distribution Copy Number Name(Role, Title) Date
untitled
PMIS 발전전략 수립사례 A Case Study on the Development Strategy of Project Management Information System 류 원 희 * 이 현 수 ** 김 우 영 *** 유 정 호 **** Yoo, Won-Hee Lee, Hyun-Soo Kim, Wooyoung Yu, Jung-Ho 요 약 건설업무의 효율성
44-4대지.07이영희532~
A Spatial Location Analysis of the First Shops of Foodservice Franchise in Seoul Metropolitan City Younghee Lee* 1 1 (R) 0 16 1 15 64 1 Abstract The foodservice franchise is preferred by the founders who
Analyst Briefing
. Improve your Outlook on Email and File Management iseminar.. 1544(or 6677)-3355 800x600. iseminar Chat... Improve your Outlook on Email and File Management :, 2003 1 29.. Collaboration Suite - Key Messages
bn2019_2
arp -a Packet Logging/Editing Decode Buffer Capture Driver Logging: permanent storage of packets for offline analysis Decode: packets must be decoded to human readable form. Buffer: packets must temporarily
VZ94-한글매뉴얼
KOREAN / KOREAN VZ9-4 #1 #2 #3 IR #4 #5 #6 #7 ( ) #8 #9 #10 #11 IR ( ) #12 #13 IR ( ) #14 ( ) #15 #16 #17 (#6) #18 HDMI #19 RGB #20 HDMI-1 #21 HDMI-2 #22 #23 #24 USB (WLAN ) #25 USB ( ) #26 USB ( ) #27
Journal of Educational Innovation Research 2019, Vol. 29, No. 1, pp DOI: * Suggestions of Ways
Journal of Educational Innovation Research 2019, Vol. 29, No. 1, pp.65-89 DOI: http://dx.doi.org/10.21024/pnuedi.29.1.201903.65 * Suggestions of Ways to Improve Teaching Practicum Based on the Experiences
(Exposure) Exposure (Exposure Assesment) EMF Unknown to mechanism Health Effect (Effect) Unknown to mechanism Behavior pattern (Micro- Environment) Re
EMF Health Effect 2003 10 20 21-29 2-10 - - ( ) area spot measurement - - 1 (Exposure) Exposure (Exposure Assesment) EMF Unknown to mechanism Health Effect (Effect) Unknown to mechanism Behavior pattern
SchoolNet튜토리얼.PDF
Interoperability :,, Reusability: : Manageability : Accessibility :, LMS Durability : (Specifications), AICC (Aviation Industry CBT Committee) : 1988, /, LMS IMS : 1997EduCom NLII,,,,, ARIADNE (Alliance
표현의 자유
49 정보 인권과 민주주의를 위한 입법 과제 장여경* 오병일* 정민경* 1) 목 차 I. 문제 제기 1. 정보화 정책의 주요 문제점과 과제 2. 대안으로서 정보인권 II. 표현의 자유 1. 개념 2. 입법 과제 III. 프라이버시권 1. 개념 2. 입법 과제 IV. 정보문화향유권 1. 개념 2. 입법 과제 V. 정보접근권과 인터넷 망중립성 1. 개념 2. 입법
歯이시홍).PDF
cwseo@netsgo.com Si-Hong Lee duckling@sktelecom.com SK Telecom Platform - 1 - 1. Digital AMPS CDMA (IS-95 A/B) CDMA (cdma2000-1x) IMT-2000 (IS-95 C) ( ) ( ) ( ) ( ) - 2 - 2. QoS Market QoS Coverage C/D
03.Agile.key
CSE4006 Software Engineering Agile Development Scott Uk-Jin Lee Division of Computer Science, College of Computing Hanyang University ERICA Campus 1 st Semester 2018 Background of Agile SW Development
DBPIA-NURIMEDIA
The e-business Studies Volume 17, Number 6, December, 30, 2016:275~289 Received: 2016/12/02, Accepted: 2016/12/22 Revised: 2016/12/20, Published: 2016/12/30 [ABSTRACT] SNS is used in various fields. Although
Secure Programming Lecture1 : Introduction
Malware and Vulnerability Analysis Lecture4-1 Vulnerability Analysis #4-1 Agenda 웹취약점점검 웹사이트취약점점검 HTTP and Web Vulnerability HTTP Protocol 웹브라우저와웹서버사이에하이퍼텍스트 (Hyper Text) 문서송수신하는데사용하는프로토콜 Default Port
PowerPoint 프레젠테이션
Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Internet Page 8 Page 9 Page 10 Page 11 Page 12 1 / ( ) ( ) / ( ) 2 3 4 / ( ) / ( ) ( ) ( ) 5 / / / / / Page 13 Page 14 Page 15 Page 16 Page 17 Page 18 Page
03이경미(237~248)ok
The recent (2001-2010) changes on temperature and precipitation related to normals (1971-2000) in Korea* Kyoungmi Lee** Hee-Jeong Baek*** ChunHo Cho**** Won-Tae Kwon*****. 61 (1971~2000) 10 (2001~2010).
untitled
: 2009 00 00 : IMS - 1.0 : IPR. IMS,.,. IMS IMS IMS 1). Copyright IMS Global Learning Consortium 2007. All Rights Reserved., IMS Korea ( ). IMS,. IMS,., IMS IMS., IMS.,., 3. Copyright 2007 by IMS Global
Smart Power Scope Release Informations.pages
v2.3.7 (2017.09.07) 1. Galaxy S8 2. SS100, SS200 v2.7.6 (2017.09.07) 1. SS100, SS200 v1.0.7 (2017.09.07) [SHM-SS200 Firmware] 1. UART Command v1.3.9 (2017.09.07) [SHM-SS100 Firmware] 1. UART Command SH모바일
PWR PWR HDD HDD USB USB Quick Network Setup Guide xdsl/cable Modem PC DVR 1~3 1.. DVR DVR IP xdsl Cable xdsl Cable PC PC DDNS (
PWR PWR HDD HDD USB USB Quick Network Setup Guide xdsl/cable Modem PC DVR 1~3 1.. DVR DVR IP xdsl Cable xdsl Cable PC PC DDNS (http://ddns.hanwha-security.com) Step 1~5. Step, PC, DVR Step 1. Cable Step
untitled
1... 2 System... 3... 3.1... 3.2... 3.3... 4... 4.1... 5... 5.1... 5.2... 5.2.1... 5.3... 5.3.1 Modbus-TCP... 5.3.2 Modbus-RTU... 5.3.3 LS485... 5.4... 5.5... 5.5.1... 5.5.2... 5.6... 5.6.1... 5.6.2...
- i - - ii - - iii - - iv - - v - - vi - - 1 - - 2 - - 3 - 1) 통계청고시제 2010-150 호 (2010.7.6 개정, 2011.1.1 시행 ) - 4 - 요양급여의적용기준및방법에관한세부사항에따른골밀도검사기준 (2007 년 11 월 1 일시행 ) - 5 - - 6 - - 7 - - 8 - - 9 - - 10 -
USB USB DV25 DV25 REC SRN-475S REC SRN-475S LAN POWER LAN POWER Quick Network Setup Guide xdsl/cable Modem PC DVR 1~3 1.. DVR DVR IP xdsl Cable xdsl C
USB USB DV25 DV25 REC SRN-475S REC SRN-475S LAN POWER LAN POWER Quick Network Setup Guide xdsl/cable Modem PC DVR 1~3 1.. DVR DVR IP xdsl Cable xdsl Cable PC PC Step 1~5. Step, PC, DVR Step 1. Cable Step
0125_ 워크샵 발표자료_완성.key
WordPress is a free and open-source content management system (CMS) based on PHP and MySQL. WordPress is installed on a web server, which either is part of an Internet hosting service or is a network host
PCServerMgmt7
Web Windows NT/2000 Server DP&NM Lab 1 Contents 2 Windows NT Service Provider Management Application Web UI 3 . PC,, Client/Server Network 4 (1),,, PC Mainframe PC Backbone Server TCP/IP DCS PLC Network
09권오설_ok.hwp
(JBE Vol. 19, No. 5, September 2014) (Regular Paper) 19 5, 2014 9 (JBE Vol. 19, No. 5, September 2014) http://dx.doi.org/10.5909/jbe.2014.19.5.656 ISSN 2287-9137 (Online) ISSN 1226-7953 (Print) a) Reduction
The Self-Managing Database : Automatic Health Monitoring and Alerting
The Self-Managing Database : Automatic Health Monitoring and Alerting Agenda Oracle 10g Enterpirse Manager Oracle 10g 3 rd Party PL/SQL API Summary (Self-Managing Database) ? 6% 6% 12% 55% 6% Source: IOUG
Web Application Hosting in the AWS Cloud Contents 개요 가용성과 확장성이 높은 웹 호스팅은 복잡하고 비용이 많이 드는 사업이 될 수 있습니다. 전통적인 웹 확장 아키텍처는 높은 수준의 안정성을 보장하기 위해 복잡한 솔루션으로 구현
02 Web Application Hosting in the AWS Cloud www.wisen.co.kr Wisely Combine the Network platforms Web Application Hosting in the AWS Cloud Contents 개요 가용성과 확장성이 높은 웹 호스팅은 복잡하고 비용이 많이 드는 사업이 될 수 있습니다. 전통적인
4 CD Construct Special Model VI 2 nd Order Model VI 2 Note: Hands-on 1, 2 RC 1 RLC mass-spring-damper 2 2 ζ ω n (rad/sec) 2 ( ζ < 1), 1 (ζ = 1), ( ) 1
: LabVIEW Control Design, Simulation, & System Identification LabVIEW Control Design Toolkit, Simulation Module, System Identification Toolkit 2 (RLC Spring-Mass-Damper) Control Design toolkit LabVIEW
김기남_ATDC2016_160620_[키노트].key
![김기남_ATDC2016_160620_[키노트].key](/thumbs/85/91488855.jpg "김기남_ATDC2016_160620_[키노트].key")
metatron Enterprise Big Data SKT Metatron/Big Data Big Data Big Data... metatron Ready to Enterprise Big Data Big Data Big Data Big Data?? Data Raw. CRM SCM MES TCO Data & Store & Processing Computational
45-51 ¹Ú¼ø¸¸
A Study on the Automation of Classification of Volume Reconstruction for CT Images S.M. Park 1, I.S. Hong 2, D.S. Kim 1, D.Y. Kim 1 1 Dept. of Biomedical Engineering, Yonsei University, 2 Dept. of Radiology,
1 : UHD (Heekwang Kim et al.: Segment Scheduling Scheme for Efficient Bandwidth Utilization of UHD Contents Streaming in Wireless Environment) (Specia
(Special Paper) 23 6, 2018 11 (JBE Vol. 23, No. 6, November 2018) https://doi.org/10.5909/jbe.2018.23.6.813 ISSN 2287-9137 (Online) ISSN 1226-7953 (Print) UHD a), a) Segment Scheduling Scheme for Efficient
<313120C0AFC0FCC0DA5FBECBB0EDB8AEC1F2C0BB5FC0CCBFEBC7D15FB1E8C0BAC5C25FBCF6C1A42E687770>
한국지능시스템학회 논문지 2010, Vol. 20, No. 3, pp. 375-379 유전자 알고리즘을 이용한 강인한 Support vector machine 설계 Design of Robust Support Vector Machine Using Genetic Algorithm 이희성 홍성준 이병윤 김은태 * Heesung Lee, Sungjun Hong,
The characteristic analysis of winners and losers in curling: Focused on shot type, shot accuracy, blank end and average score SungGeon Park 1 & Soowo
The characteristic analysis of winners and losers in curling: Focused on shot type, shot accuracy, blank end and average score SungGeon Park 1 & Soowon Lee 2 * 1 Program of Software Convergence, Soongsil
DBPIA-NURIMEDIA
논문 10-35-08-15 한국통신학회논문지 '10-08 Vol.35 No. 8 건설생산성 향상을 위한 건설현장 내 RFID 네트워크 시스템 적용 방안 준회원 김 신 구*, 정회원 이 충 희*, 이 성 형*, 종신회원 김 재 현* Method of RFID Network System Application for Improving of Construction
ISO17025.PDF
ISO/IEC 17025 1999-12-15 1 2 3 4 41 42 43 44, 45 / 46 47 48 49 / 410 411 412 413 414 5 51 52 53 54 / 55 56 57 58 / 59 / 510 A( ) ISO/IEC 17025 ISO 9001:1994 ISO 9002:1994 B( ) 1 11 /, / 12 / 1, 2, 3/ (
Microsoft PowerPoint - XP Style
Business Strategy for the Internet! David & Danny s Column 유무선 통합 포탈은 없다 David Kim, Danny Park 2002-02-28 It allows users to access personalized contents and customized digital services through different
THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE Feb.; 29(2), IS
THE JOURNAL OF KOREAN INSTITUTE OF ELECTROMAGNETIC ENGINEERING AND SCIENCE. 2018 Feb.; 29(2), 93 98. http://dx.doi.org/10.5515/kjkiees.2018.29.2.93 ISSN 1226-3133 (Print) ISSN 2288-226X (Online) UHF-HF
歯A1.1함진호.ppt
The Overall Architecture of Optical Internet ETRI ? ? Payload Header Header Recognition Processing, and Generation A 1 setup 1 1 C B 2 2 2 Delay line Synchronizer New Header D - : 20Km/sec, 1µsec200 A
Subnet Address Internet Network G Network Network class B networ
Structure of TCP/IP Internet Internet gateway (router) Internet Address Class A Class B Class C 0 8 31 0 netid hostid 0 16 31 1 0 netid hostid 0 24 31 1 1 0 netid hostid Network Address : (A) 1 ~ 127,
±è¼ºÃ¶ Ãâ·Â-1
Localization Algorithms Using Wireless Communication Systems For efficient Localization Based Services, development of accurate localization algorithm has to be preceded. In this paper, research trend
슬라이드 제목 없음
ITU-T sjkoh@cs.knu.ac.kr ITU International Telecommunication Union 1934, UN Formerly, as known as CCITT http://www.itu.int ITU 2/36 ITU ITU Secretary General PP: ITU 3 Sectors ITU-T (Director, WTSA) ITU-Telecom.
ETL_project_best_practice1.ppt
ETL ETL Data,., Data Warehouse DataData Warehouse ETL tool/system: ETL, ETL Process Data Warehouse Platform Database, Access Method Data Source Data Operational Data Near Real-Time Data Modeling Refresh/Replication
Backup Exec
(sjin.kim@veritas.com) www.veritas veritas.co..co.kr ? 24 X 7 X 365 Global Data Access.. 100% Storage Used Terabytes 9 8 7 6 5 4 3 2 1 0 2000 2001 2002 2003 IDC (TB) 93%. 199693,000 TB 2000831,000 TB.