rrdtool advanced topics
play

RRDtool advanced Topics Tobias Oetiker OETIKER+PARTNER AG OpenNMS - PowerPoint PPT Presentation

Jan 11, 2024 •181 likes •1.92k views

RRDtool advanced Topics Tobias Oetiker OETIKER+PARTNER AG OpenNMS User Conference 2013 A different kind of Database creating a simple rrd #!/bin/sh 1 PATH =/ scratch/rrd4/bin:$PATH 2 R=rrdtool 3 $R create first.rrd \ 4 --step =300 \

  1. on-disk structure +-------------------------------+ | RRD cookie, DB cfg | Static Header |-------------------------------| : Data Source (DS) Definitions : |-------------------------------| : RR Archive (RRA) Definitions : |===============================| | last update time | Live Head |-------------------------------| : last value for diff : PDP Prep per DS |-------------------------------| : intermediate storage : CDP Prep per RRA and DS |-------------------------------| : RRA Pointers : |===============================| : Round Robin Archives (RRA) : +-------------------------------+

  2. on-disk structure +-------------------------------+ | RRD cookie, DB cfg | Static Header |-------------------------------| : Data Source (DS) Definitions : |-------------------------------| : RR Archive (RRA) Definitions : |===============================| | last update time | Live Head |-------------------------------| : last value for diff : PDP Prep per DS |-------------------------------| : intermediate storage : CDP Prep per RRA and DS |-------------------------------| : RRA Pointers : |===============================| : Round Robin Archives (RRA) : +-------------------------------+

  3. irregular data arrival intervals #!/bin/sh 1 PATH =/ scratch/rrd4/bin:$PATH 2 R=rrdtool 3 $R create real.rrd \ 4 --step =300 \ 5 --start =1199999699 \ 6 DS:distance:COUNTER :600: -40:100 \ 7 RRA:AVERAGE :0.4:1:5 8 9 u(){ 10 $R update real.rrd $1 11 } 12 13 u 1200000000:0 14 u 1200000150:15 15 16 u 1200000310:31 17 u 1200000640:64 18 u 1200000910:91

  4. database after the irregular updates $R fetch real.rrd -s 1200000000 -e 1200000899 AVERAGE 1 distance 1 2 1200000300: 1.0000000000e-01 3 1200000600: 1.0000000000e-01 4 1200000900: 1.0000000000e-01 5 ◮ rrdtool re-binning at work ◮ major difference to a normal db ◮ all bins contain 1.0 ◮ the time is the ’end-time’ of the bin.

  5. database after the irregular updates $R fetch real.rrd -s 1200000000 -e 1200000899 AVERAGE 1 distance 1 2 1200000300: 1.0000000000e-01 3 1200000600: 1.0000000000e-01 4 1200000900: 1.0000000000e-01 5 ◮ rrdtool re-binning at work ◮ major difference to a normal db ◮ all bins contain 1.0 ◮ the time is the ’end-time’ of the bin.

  6. database after the irregular updates $R fetch real.rrd -s 1200000000 -e 1200000899 AVERAGE 1 distance 1 2 1200000300: 1.0000000000e-01 3 1200000600: 1.0000000000e-01 4 1200000900: 1.0000000000e-01 5 ◮ rrdtool re-binning at work ◮ major difference to a normal db ◮ all bins contain 1.0 ◮ the time is the ’end-time’ of the bin.

  7. database after the irregular updates $R fetch real.rrd -s 1200000000 -e 1200000899 AVERAGE 1 distance 1 2 1200000300: 1.0000000000e-01 3 1200000600: 1.0000000000e-01 4 1200000900: 1.0000000000e-01 5 ◮ rrdtool re-binning at work ◮ major difference to a normal db ◮ all bins contain 1.0 ◮ the time is the ’end-time’ of the bin.

  8. optimizing your rrds ◮ update of multi DS RRD is cheep ◮ single update interval per RRD ◮ RRD modification is expensive ◮ RRD size and update performance are independent ◮ RRA complexity affects update performance

  9. optimizing your rrds ◮ update of multi DS RRD is cheep ◮ single update interval per RRD ◮ RRD modification is expensive ◮ RRD size and update performance are independent ◮ RRA complexity affects update performance

  10. optimizing your rrds ◮ update of multi DS RRD is cheep ◮ single update interval per RRD ◮ RRD modification is expensive ◮ RRD size and update performance are independent ◮ RRA complexity affects update performance

  11. optimizing your rrds ◮ update of multi DS RRD is cheep ◮ single update interval per RRD ◮ RRD modification is expensive ◮ RRD size and update performance are independent ◮ RRA complexity affects update performance

  12. optimizing your rrds ◮ update of multi DS RRD is cheep ◮ single update interval per RRD ◮ RRD modification is expensive ◮ RRD size and update performance are independent ◮ RRA complexity affects update performance

  13. fetching data fetch is for reading data from an rrd RRA:AVERAGE :0.5:1:2 \ 1 RRA:AVERAGE :0.5:2:3 2 ◮ one RRA with two 300s entries ◮ one RRA with three 600s entries

  14. fetching data fetch is for reading data from an rrd RRA:AVERAGE :0.5:1:2 \ 1 RRA:AVERAGE :0.5:2:3 2 ◮ one RRA with two 300s entries ◮ one RRA with three 600s entries

  15. playing catch with fetch first pull 300 seconds > rrdtool fetch x.rrd -r 300 \ -s 1200000600 -e 1200000900 AVERAGE 1200000900: 4.0000000000e+01 1200001200: 5.0000000000e+01 then pull 900 seconds > rrdtool fetch x.rrd -r300 \ -s 1200000000 -e 1200000900 AVERAGE 1200000600: 2.5000000000e+01 1200001200: 4.5000000000e+01

  16. fetch recap ◮ looking for complete coverage ◮ resolution is only a suggestion ◮ time stamp in output marks the END of the period ◮ end-time differences caused problems ◮ since 1.3, only the start-time is relevant for coverage ◮ outside the rra everything is NaN

  17. fetch recap ◮ looking for complete coverage ◮ resolution is only a suggestion ◮ time stamp in output marks the END of the period ◮ end-time differences caused problems ◮ since 1.3, only the start-time is relevant for coverage ◮ outside the rra everything is NaN

  18. fetch recap ◮ looking for complete coverage ◮ resolution is only a suggestion ◮ time stamp in output marks the END of the period ◮ end-time differences caused problems ◮ since 1.3, only the start-time is relevant for coverage ◮ outside the rra everything is NaN

  19. fetch recap ◮ looking for complete coverage ◮ resolution is only a suggestion ◮ time stamp in output marks the END of the period ◮ end-time differences caused problems ◮ since 1.3, only the start-time is relevant for coverage ◮ outside the rra everything is NaN

  20. fetch recap ◮ looking for complete coverage ◮ resolution is only a suggestion ◮ time stamp in output marks the END of the period ◮ end-time differences caused problems ◮ since 1.3, only the start-time is relevant for coverage ◮ outside the rra everything is NaN

  21. fetch recap ◮ looking for complete coverage ◮ resolution is only a suggestion ◮ time stamp in output marks the END of the period ◮ end-time differences caused problems ◮ since 1.3, only the start-time is relevant for coverage ◮ outside the rra everything is NaN

  22. Graphing

  23. rrdgraph syntax 101 for graph command syntax, there are two basic rules: 1. --options start with a double dash 2. graphing instructions start with a letter rrdtool graph output DEF:var= rrd : DS : AVARAGE LINE:var# hex-rgb-color :Comment DEF and LINE are graphing instructions .

  24. rrdgraph syntax 101 for graph command syntax, there are two basic rules: 1. --options start with a double dash 2. graphing instructions start with a letter rrdtool graph output DEF:var= rrd : DS : AVARAGE LINE:var# hex-rgb-color :Comment DEF and LINE are graphing instructions .

  25. rrdgraph syntax 101 for graph command syntax, there are two basic rules: 1. --options start with a double dash 2. graphing instructions start with a letter rrdtool graph output DEF:var= rrd : DS : AVARAGE LINE:var# hex-rgb-color :Comment DEF and LINE are graphing instructions .

  26. rrdgraph syntax 101 for graph command syntax, there are two basic rules: 1. --options start with a double dash 2. graphing instructions start with a letter rrdtool graph output DEF:var= rrd : DS : AVARAGE LINE:var# hex-rgb-color :Comment DEF and LINE are graphing instructions .

  27. normal line RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 00:00 02:00 04:00 06:00 DEF:a=x.rrd:a:AVERAGE DEF:b=x.rrd:b:AVERAGE

  28. lower limit RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 --lower-limit= 0

  29. slope mode RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 --slope-mode

  30. anti-anti-aliasing: graph

  31. anti-anti-aliasing: font

  32. line width RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 LINE 1 :b#ff00ff LINE 4 :a#ffaa00

  33. dashed line RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 LINE1:a#ff00ff:: dashes=10,10,80,10 LINE2:b#ffaa00:: dashes=1,3 : dash-offset=10

  34. DEF with :step RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 DEF:a=x.rrd:a:AVERAGE DEF:b=x.rrd:a:AVERAGE: step=1800

  35. DEF with :start RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 DEF:a=x.rrd:a:AVERAGE DEF:b=x.rrd:a:AVERAGE: start=1200011700

  36. DEF with :reduce RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 DEF:b=x.rrd:a:AVERAGE:step=1800: reduce=MIN DEF:c=x.rrd:a:AVERAGE:step=1800: reduce=MAX DEF:a=x.rrd:a:AVERAGE

  37. AREA simple RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 AREA :a#a1003b LINE:b#11a03b

  38. two AREAs RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 AREA :a#a1003b AREA :b#11a03b

  39. transparent AREA RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 AREA:a#a1003b ff AREA:b#11a03b 60

  40. stacked AREA RRDTOOL / TOBI OETIKER 200 180 160 140 120 100 80 60 40 20 0 00:00 02:00 04:00 06:00 AREA:a#a1003b AREA:b#11a03b:...: STACK

  41. time shift RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 CDEF:b=a SHIFT :b:3600

  42. shifting with extra data RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 CDEF:b=a SHIFT :b:3600 DEF:a=x.rrd:a:AVERAGE: start=1199996100

  43. Revers Polish Notation (RPN) Math

  44. RPN basics: Step 0 15 + 23 = 38 1: NAN 2: NAN 3: NAN

  45. RPN basics: Step 1 15 + 23 = 38 [15] 1: 15 2: NAN 3: NAN

  46. RPN basics: Step 2 15 + 23 = 38 [23] 1: 23 2: 15 3: NAN

  47. RPN basics: Step 3 15+23 = 38 [+] 1: 38 2: NAN 3: NAN

  48. math in the graph (+) RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 CDEF :b=a,20,+

  49. simple gradient RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 CDEF:c=a,4,/ AREA:c#77b7ff AREA:c#5aa8ff::STACK AREA:c#2b8fff::STACK AREA:c#0078ff::STACK

  50. the MAX function RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 a b c c=a,b, MAX

  51. the LIMIT function RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 a b b=a,30,70, LIMIT

  52. the TREND function RRDTOOL / TOBI OETIKER 200 180 160 140 120 100 80 60 40 20 0 00:00 02:00 04:00 06:00 a b b=a,3600, TREND

  53. the TREND with early start RRDTOOL / TOBI OETIKER 200 180 160 140 120 100 80 60 40 20 0 00:00 02:00 04:00 06:00 DEF:a=graph-examples.rrd:a:AVERAGE: start=1199996100 a b b=a,3600,TREND

  54. the TREND and SHIFT RRDTOOL / TOBI OETIKER 200 180 160 140 120 100 80 60 40 20 0 00:00 02:00 04:00 06:00 a CDEF:b=a,3600,TREND SHIFT :b:-1800 b

  55. the IF function RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 a b c=a,b, LT ,a,b, IF ,4,-

  56. horizontal gradient RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 a b=a,75,LE,a,75,IF c=a,50,LE,a,50,IF b=a,25,LE,a,25,IF

  57. about invisibility RRDTOOL / TOBI OETIKER 120 110 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 c=a,b,GT,a, UNKN ,IF d=a,b,LT,b, UNKN ,IF

  58. positional drawing count RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 c= COUNT ,3,%,0,EQ,a,UNKN,IF

  59. access the previous value RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 CDEF:c=COUNT,3,%,0,EQ,a,UNKN,IF d=COUNT,3,%,1,EQ, PREV ,c,IF

  60. positional drawing time RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 c= TIME ,1800,%,900,GE,a,UNKN,IF

  61. positional drawing time-shifting RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 c=TIME, 1,- ,1800,%,900,GE,a,UNKN,IF

  62. time and resolution issues RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 c=TIME,1756,%,180,GE,a,UNKN,IF

  63. CDEF internals ◮ data may come in different resolutions ◮ all items in a CDEF must have the same resolution ◮ resolution is expanded to greatest common devisor (gcd) ◮ example: gcd(6,9) = 3, gcd(1,6) = 1 trick: an rrd with one a second step. rrdtool create one.rrd --step=1 DS:one:GAUGE:2:U:U RRA:AVERAGE:0.5:1:1

  64. CDEF internals ◮ data may come in different resolutions ◮ all items in a CDEF must have the same resolution ◮ resolution is expanded to greatest common devisor (gcd) ◮ example: gcd(6,9) = 3, gcd(1,6) = 1 trick: an rrd with one a second step. rrdtool create one.rrd --step=1 DS:one:GAUGE:2:U:U RRA:AVERAGE:0.5:1:1

  65. CDEF internals ◮ data may come in different resolutions ◮ all items in a CDEF must have the same resolution ◮ resolution is expanded to greatest common devisor (gcd) ◮ example: gcd(6,9) = 3, gcd(1,6) = 1 trick: an rrd with one a second step. rrdtool create one.rrd --step=1 DS:one:GAUGE:2:U:U RRA:AVERAGE:0.5:1:1

  66. CDEF internals ◮ data may come in different resolutions ◮ all items in a CDEF must have the same resolution ◮ resolution is expanded to greatest common devisor (gcd) ◮ example: gcd(6,9) = 3, gcd(1,6) = 1 trick: an rrd with one a second step. rrdtool create one.rrd --step=1 DS:one:GAUGE:2:U:U RRA:AVERAGE:0.5:1:1

  67. step=1 trick: high resolution cdef RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 DEF:one=1.rrd:one:AVERAGE c=one,POP,TIME,1756,%,180,GE,a,UNKN,IF

  68. Consolidation functions

  69. finding the average RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 b avg 57.7 VDEF :b=a,AVERAGE GPRINT :b:avg %.1lf

  70. calculating min and max RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 max 97.5 23:40 VDEF:max=a,MAXIMUM min 15.9 02:15 VDEF:min=a,MINIMUM

  71. min max code example LINE:a#456:a VDEF:max=a,MAXIMUM LINE:max#123 VRULE:max#123:maximum GPRINT:max:%.1lf GPRINT:max:%H \ :%M :strftime A VDEF result has a value and a time assigned.

  72. Least Squares Line (y=x*m+b) RRDTOOL / TOBI OETIKER 100 90 80 70 60 50 40 30 20 10 0 00:00 02:00 04:00 06:00 VDEF:slope=a,LSLSLOPE (-0.142) VDEF:int=a,LSLINT (63.4) a lsl=a,POP,COUNT,slope,*,int,+

  73. Holt Winters Aberrant Behaviour Detection

  74. about alert generation ◮ when something unexpected happens send an alert ◮ fixed thresholds are too wide a net ◮ moving averages weigh all data equal ◮ holt winters can predict the future ◮ and no one considers himself clever enough to use it

  75. about alert generation ◮ when something unexpected happens send an alert ◮ fixed thresholds are too wide a net ◮ moving averages weigh all data equal ◮ holt winters can predict the future ◮ and no one considers himself clever enough to use it

  76. about alert generation ◮ when something unexpected happens send an alert ◮ fixed thresholds are too wide a net ◮ moving averages weigh all data equal ◮ holt winters can predict the future ◮ and no one considers himself clever enough to use it

  77. about alert generation ◮ when something unexpected happens send an alert ◮ fixed thresholds are too wide a net ◮ moving averages weigh all data equal ◮ holt winters can predict the future ◮ and no one considers himself clever enough to use it

  78. about alert generation ◮ when something unexpected happens send an alert ◮ fixed thresholds are too wide a net ◮ moving averages weigh all data equal ◮ holt winters can predict the future ◮ and no one considers himself clever enough to use it

  79. rrd - holt winters assumptions ◮ data is periodic in nature ◮ data has continuity ◮ data continuity is periodic ◮ recent data is more important

  80. rrd - holt winters assumptions ◮ data is periodic in nature ◮ data has continuity ◮ data continuity is periodic ◮ recent data is more important

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

RRDtool RRDtool \aHr-aHr-deE-t:ul\ n [ E, fr. round robin database tool ] :a system to store and

RRDtool RRDtool \aHr-aHr-deE-t:ul\ n [ E, fr. round robin database tool ] :a system to store and

RRDtool RRDtool \aHr-aHr-deE-t:ul\ n [ E, fr. round robin database tool ] :a system to store and display time-series data (i.e. utilized bandwidth, machine-room temperature, server load average). R. stores the data in a very compact way so that

525 views • 30 slides
RRDtool RRDtool \aHr-aHr-deE-t:ul\ n [ E, fr. round robin database tool ] :a system to store and

RRDtool RRDtool \aHr-aHr-deE-t:ul\ n [ E, fr. round robin database tool ] :a system to store and

RRDtool RRDtool \aHr-aHr-deE-t:ul\ n [ E, fr. round robin database tool ] :a system to store and display time-series data (i.e. utilized bandwidth, machine-room temperature, server load average). R. stores the data in a very compact way so that

338 views • 29 slides
Write your own monitoring About RRDtool Programming software with RRDtool Summary Tobias

Write your own monitoring About RRDtool Programming software with RRDtool Summary Tobias

Write your own monitoring software with RRDtool Tobias Oetiker Motivation Write your own monitoring About RRDtool Programming software with RRDtool Summary Tobias Oetiker ISG.EE - ETH Zrich OSCON 2005 Portland, Oregon, August 3, 2005

580 views • 39 slides
Advanced MySQL topics Presented by : John A Mahady AndrewInfoServices.com Topics Topics

Advanced MySQL topics Presented by : John A Mahady AndrewInfoServices.com Topics Topics

Advanced MySQL topics Presented by : John A Mahady AndrewInfoServices.com Topics Topics Introduction Stored Procedures Views Triggers Cursors ODBC & OO Base Press Release - April 15, 2009 Next Week's MySQL

438 views • 40 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks The Intel Internet Exchange Architecture 10/30/03 Tilman Wolf 1 Overview Overview We have looked at Network processor concepts

771 views • 18 slides
Dealing With Missing Data Possible Future Topics Novice user topics: Advanced topics:

Dealing With Missing Data Possible Future Topics Novice user topics: Advanced topics:

Dealing With Missing Data Possible Future Topics Novice user topics: Advanced topics: Using R Growth curve modeling of eye Contrast coding & fixations hypothesis testing Overfitting Random slopes & Path

723 views • 27 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks Microengine Programming I 11/18/03 Tilman Wolf 1 Overview Overview Lab 2: IP forwarding on IXP1200 Any problems with Part I?

791 views • 24 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks Course Summary 12/04/03 Tilman Wolf 1 Final Projects Final Projects Any questions? Please send slides by Tuesday noon via email

395 views • 37 slides
A few announcements CSC630: Advanced topics in interactive data analysis catalog

A few announcements CSC630: Advanced topics in interactive data analysis catalog

A few announcements CSC630: Advanced topics in interactive data analysis catalog advertises as Advanced topics in Software Systems Research seminar all paper readings, discussions, presentations, projects CSC630: Advanced

647 views • 29 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks A Course on Network System Design Fall 2003 Tilman Wolf 1 Welcome Welcome Who am I? Tilman Wolf, Assistant Professor in ECE

601 views • 17 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks Software-Based Router Architectures 9/25/03 Tilman Wolf 1 TCP Connection Recognition TCP Connection Recognition Track currently

700 views • 13 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks IXP1200 Microengines 11/06/03 Tilman Wolf 1 Overview Overview More details on Microengines Instruction Store Registers

283 views • 25 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks Network Measurement 12/02/03 Tilman Wolf 1 Overview Overview Lab 3 requires performance measurement Throughput Collecting

543 views • 22 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks Design Basics of Network Processors 10/14/03 Tilman Wolf 1 Network Processors Network Processors Programmable packet processing

289 views • 27 slides
Outline Introduction CS 239 Subject of class Advanced Topics in Network Class topics

Outline Introduction CS 239 Subject of class Advanced Topics in Network Class topics

Outline Introduction CS 239 Subject of class Advanced Topics in Network Class topics and organization Security Reading material Class web page Peter Reiher Grading April 3, 2006 Projects Office hours Lecture

442 views • 8 slides
ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer

ECE 697J Advanced Topics Advanced Topics ECE 697J in Computer Networks in Computer Networks Active Networks 10/09/03 Tilman Wolf 1 Dynamic Functionality Dynamic Functionality Networks implement various packet processing

290 views • 16 slides
Development of Taiwans Bunun Tribe Nan-An Tribe Natural Environment in Nan-An Location

Development of Taiwans Bunun Tribe Nan-An Tribe Natural Environment in Nan-An Location

An Integrated Approach for Sustainable Development of Taiwans Bunun Tribe Nan-An Tribe Natural Environment in Nan-An Location Location Nan-An Farmland The First Organic Farmland at the foot of Mount Jade Pesticide Pollution M

784 views • 41 slides
Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing

Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing

Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data. Marta Ba nbura Michele Modugno European Central Bank 6th Eurostat Colloquium, 29 September 2010 Framework Dynamic factor model y t f t

631 views • 44 slides
Canadian Human Rights Tribunal Proceeding Update Nishnawbe Aski Nation (NAN) Chiefs-in-Assembly

Canadian Human Rights Tribunal Proceeding Update Nishnawbe Aski Nation (NAN) Chiefs-in-Assembly

Canadian Human Rights Tribunal Proceeding Update Nishnawbe Aski Nation (NAN) Chiefs-in-Assembly Special Winter Assembly, Thunder Bay February 1, 2018 Introduction Purpose: to provide the NAN Chiefs in Assembly with an update on the Caring

523 views • 19 slides
Te Kwai Toro He rangi t matawhitiwhiti He rangi t matawhanui A person with narrow vision

Te Kwai Toro He rangi t matawhitiwhiti He rangi t matawhanui A person with narrow vision

Te Kwai Toro He rangi t matawhitiwhiti He rangi t matawhanui A person with narrow vision has restricted horizon A person with a wide vision has plentiful opportunities Iti te matakahi, pangia ki te ttara pakaru ai The wedge may be

98 views • 5 slides
RESEARCH, READ & LEARN NAN B. CARMACK, DIRECTOR, LVA LIBRARY DEVELOPMENT AND NETWORKING

RESEARCH, READ & LEARN NAN B. CARMACK, DIRECTOR, LVA LIBRARY DEVELOPMENT AND NETWORKING

RESEARCH, READ & LEARN NAN B. CARMACK, DIRECTOR, LVA LIBRARY DEVELOPMENT AND NETWORKING Todays Outcomes 1. Participants will know the offerings beginning Oct. 1 2. Participants will have an understanding of the function of each asset 3.

151 views • 11 slides
Examining the Feasibility of Waqf (Islamic endowment)-based Islamic Insurance Product from the

Examining the Feasibility of Waqf (Islamic endowment)-based Islamic Insurance Product from the

Examining the Feasibility of Waqf (Islamic endowment)-based Islamic Insurance Product from the Malaysian Legal Perspective DR. NAN NOORHIDAYU MEGAT LAKSANA DR. MARHANUM CHE MOHD SALLEH DR. NOR AZIZAN CHE EMBI International Conference on

765 views • 17 slides
Airport Guideway and Stations Design-Build Contract April 16, 2020 1 Purpose Information

Airport Guideway and Stations Design-Build Contract April 16, 2020 1 Purpose Information

AGS Project Limits Extension Contract No. CT-HRT-1600385 Airport Guideway and Stations Design-Build Contract April 16, 2020 1 Purpose Information brief to the Committee regarding potential change order to extend the AGS Project Limits

736 views • 12 slides
THE BOARD OF REGENTS AND NEW YORK STATE EDUCATION DEPARTMENTS REOPENING SCHOOLS TASK FORCE

THE BOARD OF REGENTS AND NEW YORK STATE EDUCATION DEPARTMENTS REOPENING SCHOOLS TASK FORCE

THE BOARD OF REGENTS AND NEW YORK STATE EDUCATION DEPARTMENTS REOPENING SCHOOLS TASK FORCE RECOVERING, REBUILDING & RENEWING THE SPIRIT OF OUR SCHOOLS June 2020 1 HOUSEKEEPING: USING ZOOM This will provide a chat window on the right

696 views • 43 slides

More recommend