We all know that "knowledge is power", but how realistic is aiming for transparency in our own IT environments? The interaction between clients, servers, applications and users is often difficult to analyze, much less quantify. Come join Daniel Reimann to take a look at the history of your infrastructure and prepare you for future projects such as consolidations or infrastructure additions (e.g. IBM Connections). We will show you how and why you should be looking at your infrastructure as a whole, rather than individual technology silos. Find out where the hidden challenges of your IBM Notes/Domino environment are, what impact they have on your network and how you can fix it! A bolt of lightning for your DeLore...erm...infrastructure!
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
1. Back to the future
Understand and Optimize your IBM Notes/Domino infrastructure
Daniel Reimann
2. Introduction
• The goal: Thinking about going to the cloud, implementing Connections,
migrating, consolidating or growing your environment?
– Crucial data lives in various places in your IT infrastructure
– Doing any of the above projects requires you to look at this data in a “big picture”
view
• The challenge: How do you connect the various information silos?
– Knowledge is spread out in the environment in numerous repositories on several
servers
– In many situations, companies aren't even aware of the information available to them
• How we can help: Going to back to the future to move forward
– Find out how your environment looks today and get surprising results
– Use these gathered results to solve existing issues and be well prepared for
implementing or connecting new services and optimizing or extending your
infrastructure
4. About DNA®
• Service Offering
• Unique multi dimensional Insight in Utilisation &
Configuration of IBM Notes/Domino
• Overall Health Check / SWOT of Customer Environment
• Data Collection -> Analysis -> Reporting
• IBM & Business Partners reselling DNA World Wide
• Executed remotely by panagenda/Trust Factory
Zero Impact: means:
ZERO Impact
Impact
means:
Nothing to Install
No Installation
Leaves No Footprint
Leave No Footprint
No Dependancy
Deliver in a Few Days
No Footprint
5. DNA Service Modules
®
DNA ® Health Check
Starting Point for
all DNA Services
Server Consolidation Support
Optional Service
Root Cause & Performance
See slides at the end of
this presentation
Optional Service
See slides at the end of
this presentation
Source Code Analysis
Optional Service
See slides at the end of
this presentation
6. What Results will it Provide?
• Reveales Realistic Ambitions for:
•
•
•
•
Cleanup & Optimization Opportunities
Server Consolidation & Network (Optional Module)
Application Migration (Optional Module)
Performance Optimization (Optional Module)
• Interactive Slide Deck
• Linked to Common Spreadsheets & Check Lists
• Recommendations
• Presented Live
• Along with Explanation & Interpretation of Analysis Results by Subject Matter
Expert
7. What can you do with the results?
• IT Director / CIO:
•
•
•
•
Executive Decision-Making Support
Validation of Business Case
Define Innovation & Cloud Strategy
Current State of Affairs
Each DNA
®
delivers:
Quick Wins
Project Wins
Strategic Wins
• Project Manager & Teams:
• DNA Facts & Findings Help Focus & Prioritize
• Detailed Helicopter View replaces Micro View
• Prepare any Scenario with DNA Data Points
• For Administration & Support:
• Cleanup & Optimize by Means of Actionable Check Lists
• Improve Service Levels
8. Scope
• Activity by Rich Clients
• Web users not included
• 7 days
• 26 Servers in Scope
9. Management Summary
• Today:
26 Domino Servers
4 Different Releases
22544, GB Storage
Databases:
102, Integrity Issues
1020, Open to Anonymous
Directories:
110, Conflicts/Duplicates
18 Weak Passwords
• Tomorrow:
6 Domino Servers *
1 Single 9.0 Release
8316, GB Storage
Issues solved
* Based on observed session concurrency of 2.167
(clustered, excl. special functions )
10. Domino Environment Overview
1 Domino Directory
2,806 Users Registered
2,064 Users Active
3,345 Databases Touched
2,055 Users sending email
46 Servers Registered
149,515 Views Indexed
26 Servers Analyzed
15,572 Databases Deployed
1,177,671 Views Defined
4 Domino Releases
176 View Storage (GB)
4 Operating Systems
144,164 ACL Entries
589 Groups Registered
22,594 Db Storage (GB)
23,605 Group Members
11. DNA Benchmark
Active versus Registered Users
100 %
2,064 active users
80 %
60 %
40 %
20 %
0%
Demo Inc
Lowest Customer
Unused Licenses, Web Users, Regular Absense
DNA Average
Highest Customer
14. DNA Bechmark
Network Bandwidth Consumption
8
30
6
20
4
10
2
0
Demo Inc.
Lowest Customer
DNA Average
Highest Customer
server to clients
8,7
1,9
10,3
38,2
clients to server
1,6
0,5
2,1
8,1
0
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e
s
)
c
r
e
p
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t
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o
l
i
k
(
50
15. User Demand Profiling
(Demo Company, 2064, active accounts)
20%
Remote Users <
> Of f ice Workers
System Accounts
15%
10%
5%
0%
2
4
6
8
10
12
14
Distinct Hours Online per Day
16
18
20
22
24
16. End User Demand Characteristics
Demo Company
100%
75%
50%
25%
0%
Notes Sessions
Document Reads
Document Writes
Db Transactions
Network Traffic
Session Duration
19%
6%
0%
0%
4%
0%
3%
5%
47%
8%
1%
1%
mail files
52%
69%
47%
62%
87%
89%
directories
17%
22%
2%
23%
6%
3%
applications
6%
4%
4%
3%
5%
4%
check new mail
system dbs
17. User Demand on 3341, Databases
Demo Company
100.000.000
y
B
o
l
i
K
v
r
n
S
s
e
t
10.000.000
1.000.000
100.000
10.000
1.000
100
10
1
0
1
10
100
1.000
10.000
100.000
1.000.000
10.000.000
100.000.000
Kilo Bytes Read from Server
Application
Domino Directory
Mailfile
Mailin database
Server Mail Box
System database
18. End User Demand at Demo Company
Classified by Demand Level
Document
Writes
Document
Reads
Database
Transactions
Network Traffic
(client to server)
Network Traffic
(server to client)
User Sessions
0%
25%
Extreme (,0)
50%
Intensive (2,0)
75%
Moderate (62,0)
100%
Light (2000,0)
19. User Activity by Top 10 Accounts
(Document Reads)
25.000
20.000
user_810
user_864
user_1831
15.000
user_1364
user_894
user_1950
user_2652
10.000
user_996
user_2243
user_657
8
1
2
1
6
D -O -Y
t 0
k Y
8
1
2
1
6
D -O -Y
t 0
k Y
8
1
2
1
6
D -O -Y
t 0
k Y
8
1
2
1
6
D -O -Y
t 0
k Y
8
1
2
1
6
D -O -Y
t 0
k Y
8
1
2
1
6
2
1
8
1
D -O -Y
t 0
k Y
D -O -Y
t 0
k Y
0
6
5.000
20. Domino Servers at Demo Company
Classified by Maximum Session Concurrency
20
Redistributing the load can reduce nr. of
servers with up to 18,
15
10
5
0
Level
Servers
Very Low
< 50
Low
50 - 249
Average
250 - 749
Normal
750 - 1499
High
> 1500
18
6
0
2
0
26. Network Compression
How Much is Notes Network Compression Used?
Includes Traffic
from Users and
Servers
# Users making use of
Notes Network Compression
Disabled
99%
100%
Enabled
Disabled
75%
% Active Users
Enabled
1%
50%
25%
0%
Persons
Servers
27. Deployment Integrity
Entries appearing in multiple documents
Integrity check
Duplicate Replica On Same Server
Duplicate Template on same Server
# Databases
80
0
Document Type
Full Names
Name Variations
Group
0
2
Mailin / Resource
4
6
Replicas Acting As Different Template
22
Person
0
95
Same Replica but Different Inheritance
0
Other
3
0
Grand Total
7
103
Grand Total
102
86 Group Cycles Detected
28. DB Storage Profile for Demo Company
Distributed by Size
25%
Dbs > 1 GB: 1,323
Dbs > 10 GB: 431
20%
System database
15%
Mailin database
Mailfile
Application
10%
.
0
1
.
0
1
0
.
1
Size (megabytes)
0
1
0%
0
1
5%
1
e
g
r
S
l
a
t
T
f
o
%
Domino Directory
29. Database Storage Consolidation Potential
Number of
Database Type
Domino Directory
Total
Unique
Consolidated
Storage
Databases
Storage
Replicas
Storage
Savings
79
3
30
7,832
20,723
2,768
7,517
64%
Mailin database
261
107
144
53
50%
Server Mail Box
54
8
54
8
0%
QuickPlace
165
1
147
1
0%
BlackBerry
1,425
11
718
6
45%
Application
2,904
1,691
1,200
731
57%
12,720
22,544
5,061
8,316
63%
Mailfile
Grand Total
-
100%
Top 10 databases ranked by size
Database Title
Db Type
Storage (GB)
061cc52a3244e8bad944519170c3ff06
Mailfile
42.2
96140ee5ec036ec69136a74647729a2a
Mailfile
32.2
cad93d0aa156514e70237f1b370cc9c5
Mailfile
31.9
cfef8f68b2847cdd2eea9dda44d94acc
Mailfile
30.5
861455d1d12b1b2d990fc9e7c41ceea7
Mailfile
28.9
5128de940e6d560d37723b6448d48e00
Mailfile
28.7
d6de2ffe48a037e2e93ff13c688df77c
Mailfile
28.3
490eeb54ce626d24d63d1038be4de073
Mailfile
28.0
928c35784596e9487a0db1304573a310
Mailfile
27.8
1ab2006c3ef8fe4ba6e7daca15ee29d6
Application
27.3
30. View Size Distribution for Demo Company
12%
Views > 100 MB: 234,
Views > 1 GB: 8,
Showing 148355, views
10%
System database
8%
Mailin database
Mailfile
Domino Directory
Application
4%
2%
B
G
0
1
B
G
1
B
M
0
1
B
M
0
1
B
K
0
1
B
M
1
0%
B
K
0
1
g
r
S
w
e
i
V
l
a
t
T
f
o
%
6%
31. View Size Distribution for Demo Company
End User Mail Files
25%
Views > 100 MB: 13,0
Views > 1 GB: ,0
Showing 7104, views
20%
($Meetings)
15%
($ThreadsEmbed)
($All)
($Sent)
($Inbox)
g
r
S
w
e
i
V
l
a
t
T
f
o
%
10%
5%
B
M
0
1
B
M
0
1
B
M
1
B
K
0
1
B
K
0
1
0%
32. Basic Security Checks
Internet Password Strength
Databases with Anonymous Access
Variations found
Accounts
Company Name
1
First Name
1
Last Name
1
Depositor
Short Name
7
'password'
8
18
Databases
Templates
0
28
Reader
262
80
Author
35
82
Editor
Grand Total
Access Level
2
0
Manager
0
21
Grand Total
299
211
Grand Total
598
422
42. Mail Transfer Rates
Categorized by Msg Size
7,000
Msgs per Hour, Show ing all servers
6,000
5,000
4,000
3,000
2,000
1,000
under_1kb
1kb_to_10kb
10kb_to_100kb
100kb_to_1mb
1mb_to_10mb
10mb_to_100mb
18
12
6
27-Oct-10 0
18
12
6
26-Oct-10 0
18
12
6
25-Oct-10 0
18
12
6
24-Oct-10 0
18
12
6
23-Oct-10 0
18
12
6
22-Oct-10 0
18
12
6
21-Oct-10 0
0
over_100mb
43. DNA for Root Cause & Performance:
• Service Offering
• Assist Customers with Root Cause Analysis
» Application & Server Performance
» Server Stability and Complex Support Topics
» Network Latency Impact
• Data Collection -> Analysis -> Reporting
• Executed Remotely by panagenda/Trust Factory
Zero Impact: means:
ZERO Impact
Impact
means:
• Prerequisite
• DNA Health Check Performed
Nothing to Install
No Installation
Leaves No Footprint
Leave No Footprint
No Dependancy
Deliver in a Few Days
No Footprint
44. What Results will it Provide?
• Root Cause Analysis Report:
•
•
•
Server & OS Configuration and Settings
Application Performance
Impact of Network Delay on Applications
• Interactive Slide Deck
• Linked to Common Spreadsheets & Check Lists
• Recommendations
• Presented Live
• Along with Explanation & Interpretation of Analysis Results
by Subject Matter Expert
45. What can you do with the results?
• Application Owners:
• Improve User Satisfaction
• Developers:
• Improve Source Code of Applications
• Adminstrators:
• Improve Server Performance
46. DNA Server Consolidation:
• Service Offering
• Multi Dimensional Insight into Network Demand,
Consolidation Potential & Data Center Scenarios
• Detailed Roadmap to Plan & Execute Server Consolidations
• Data Collection -> Analysis -> Reporting
• Executed Remotely by BP Trust Factory
ZEROImpactmeans:
Zero Impact:
Impact means:
No Installation
Nothing to Install
• Prerequisite
Leave No Footprint
Leaves No Footprint
No Dependancy
• DNA Health Check Performed
Deliver in a Few Days
No Footprint
47. What Results will it Provide?
•
•
•
•
•
Calculates Network Bandwidth Requirements
Consolidation Potential & Placement Scenarios
System & User Traffic Reduction & Optimization
Sizing Parameters for Servers, Storage & Network
Data Points for Cloud / Lotus Live
• Interactive Slide Deck
• Linked to Common Spreadsheets & Check Lists
• Recommendations
• Presented Live
• Along with Explanation & Interpretation of Analysis Results by Subject Matter Expert
48. What can you do with the results?
• IT Director / CIO:
• Executive Decision-Making Support
• Validation of Business Cases & Vendor Proposals
• Effective Realization of Cost Reduction
• Project Manager & Teams:
•
•
•
•
Input for Business Case & Project Proposal
DNA Facts & Findings Help Focus & Prioritize
Define Service Levels
Use Sizing Parameters for RFP’s
49. DNA Source Code Analysis:
• Service Offering
• Multi Dimensional Insight into Entire Application Landscape
» All Design Elements
» Source Code
• Real Impact of Application Landscape on Platform Migration
» Factual Quantification of Migration Effort for Redevelopment
» Identify Applications that Depend on Notes Mail
• Data Collection -> Analysis -> Reporting
• Executed Remotely by Trust Factory
ZEROImpactmeans:
Zero Impact:
Impact means:
No Installation
Nothing to Install
Leave No Footprint
Leaves No Footprint
No Dependancy
• Prerequisite
• DNA Health Check Performed
Deliver in a Few Days
No Footprint
50. What Results will it Provide?
• Complete Inventory of De-Duplicated Design & Source Code
• List of Applications that will Break after Migrating Notes Mail away
from Domino
• True Migration Effort based on Cocomo II
• Interactive Slide Deck
• Linked to Common Spreadsheets & Check Lists
• Recommendations
• Presented Live
• Along with Explanation & Interpretation of Analysis Results by Subject Matter
Expert
51. What can you do with the results?
• IT Director / CIO:
• Executive Decision-Making Support
• Validation of Business Cases & Vendor Proposals
• Develop Innovation Strategy
• Project Manager & Teams:
•
•
•
•
Input for Business Case & Project Proposal
Remediate Apps & Code Interacting with Notes Mail
DNA Facts & Findings Help Focus & Prioritize
Consolidate Source Code
52. Upcoming: panagenda iDNA
The In-house Version of Trust Factory‘s DNA Service
http://www.panagenda.com/en_uk/idna
53. Instant and Ongoing Analytics for Servers, Clients, Apps & More
• Unique Insights and Instant Value
• Executive Decision-Making Support
• Validation of Business Cases & Vendor Proposals
• Develop Innovation Strategy
• Turn Data into Knowledge
•
•
•
•
Hassle-free data collection from many different data sources
Instantly turns your data into meaningful reports
Move from reactive to proactive operations
Helps to fix, foresee and prevent problems with root cause
identification
• Gain answers to questions you never knew you could ask
54. Instant and Ongoing Analytics for Servers, Clients, Apps & More
• Facts and Architecture
• Up and running in half an hour - turnkey virtual softwareappliance
(Linux/VMWare)
• Non-intrusive, agent-less software - no installations required on
analyzed systems
• Data ware house access possible for data use with existing
reporting solutions
• HTML5 and PDF export for offline reports
67. How to Engage
• Master Agreement is in Place with IBM since 2003:
• Global Master Agreement Number:
• Trust Factory Supplier Number:
• panagenda:
•
•
•
•
4902NL0239
1000216663
Schreyvogelgasse 3/10 :: 1010 Vienna :: Austria
Web: http://www.panagenda.com
Email: office@panagenda.com
Fax: +43 1 89 012 89 – 15
• Our business partners in Italy:
68. Grazie agli sponsor per aver reso possibile i
Dominopoint Days 2013!
Main Sponsor
Vad sponsor
Platinum sponsor
Gold sponsor
Notas do Editor
When performing the data collection, the DNA collector will populate itself by taking server documents from the Domino Directories. By default the directory is taken from the homeserver of the user-id that performs the collector, but the customer is free to add more server documents from additional directories.
With a populated collector database, each server will be verified for connectivity and sufficient access to log files. In case the Notes client cannot reach certain servers, the customer may decide to place them out of scope.
Another reason for placing servers out of scope may be to discard activity on servers in the development and test environment.
DNA will always attempt to collect 7 days worth of log data from the servers in scope. In the event that a server log does not contain 7 days of data, or when the server log has been removed and recreated after e.g. a crash, the collector will be unable to collect data that is missing.
This slide summarizes the optimization potential as it is observed within the customer environment today.
Each topic described here, will be explained in more detail within the remaining slides of this presentation.
In this benchmark, DNA calcules the number of active users as a percentage of the number of person documents registered in the Domino directories of the customer.
User are considered active as soon as the user has had at least one user session towards one of the servers in scope, with a rich client, during the 7 days of analysis. Web users are not included in this DNA analysis.
Purpose of this slide:
If the result is low this could indicate that only a small number of servers have been placed in scope for this analysis. When all servers were included, a low score might indicate that the directories containt person documents that are no longer in use.
This benchmark looks at the amount of time notes clients spent online with the Domino servers. The analysis is split up into two measures:
1. total time online during the 7 day period, expressed in hours;
2. average session duration expressed in minutes;
Average session duration has a negative correlation with network bandwidth consumption. The longer an average session lasts, the lower the network consumption. High session duration may indicate performance issues in the network or at the servers.
Notice that with the customer on the right, each user had sessions open to separate mail and application servers. This is why the customer scored a total online time of more than 40 hours.
Usually, we see that customer who deploy local mail file replicas (where users do not work on server but on their local replica) score significantly lower in session duration, and at higher network bandwidth consumption.
This benchmark shows the total number of documents read and written in the 7 day period that was analyzed.
Note that user activity can be caused in several ways:
user clicking on desktop icon and workin interactively on the server;
user’s workstation starts replicating local databases with a server;
workstation checks for new mail;
Scheduled Notes agent on a desktop starts interacting with server(s).
All these actions result in user sessions that are logged in the log.nsf.
It is not easy to draw conclusions from this analysis, other than that the current customer under investigation is relatively heavier or lighter than the DNA average.
Also, be aware that system-type accounts that are defined in a person document, are considered in this analysis. Especially fax workstations and rdbms connectors may impact the outcome of this analysis significantly.
This benchmark compares the network bandwidth consumption. Two measures are shown for end users connecting to Domino servers. One measure shows the network traffic from desktops towards servers, the other shows the opposite direction. Bandwidth consumption is expressed in kilobits per second.
Low scores may indicate performance issues either in the network, or at the servers. While high scores may prove high performance, it can also indicate excessive use patterns due to misconfiguration of the desktops. As an example, consider desktops that have a local mail file replica, while the end user keeps working online on the server. This results in double the amount of traffic and higher bandwidth levels.
Lotus offers network compression that reduce the network traffic significantly. This is analyzed too by DNA.
Another method to profile end user demand (user segmentation) is by taking a look at the distinct number of working hours per day that users show active on the server park. This analysis does not show the working hours of end users, but observes how many distinct hours the user showed activity, on average per day.
As an example: remote non-office workers (e.g. salesmen visiting customers all day) typically replicate with their home server in the morning (1 hour observed), go on the road all day and replicate again in the evening (another hour observed).
Many system accounts (monitoring workstations, fax machines operating with notes) show activity up to 24x7.
This chart expresses the percentage of the total number of active users in each category.
This analysis presents an overview of the overall user demand characteristics. Total demand is expressed in 6 columns, with each column representing 100% of that type of demand scored during the week of analysis.
Each column is then split up into various types of demand:
Checks for new mail shows Notes clients checking for new mail;
System dbs represents access to system databases;
Mail files: access to end user mail files;
Directories: access to Domino Directories;
Applications: access to application databases*
Application databases are identified as follows:
Of all databases inventoried, DNA substracts mail and mailin files, ‘known’ system databases and domino directories. What remains is a set of databases that are considered applications. Although this is not a 100% accurate method, it does provide a solid understanding of the types of user demand.
This analysis is revealing how end users make use of Notes databases, in terms of network traffic. Every bubble on this chart represents a database.
Databases have different colors, indicating the type of database. The size of each bubble is defined by the distinct number of end users that showed activity during the 7 day period that was analyzed. The horizontal and vertical distribution of bubbles reflect the amount of network traffic (bytes read and written towards each database, logarithmic scale).
Databases in the lower left corner are the most light in terms of network consumption, while databases in the upper right hand are the most network intensive.
While this analysis presents up to 10,000 most used databases, the underlaying factsheet does contain all databases that have been touched.
Trust Factory is offering an optional cluster plotter component that enables customers to generate a wide variety of angles in analyzing database utilization.
A significant optimization potential can be found by analyzing user accounts that show excessive demand patterns. Often, we see that very few user accounts consume one third or more of the total network and server capacity.
DNA is able to classify user accounts by means of comparing their individual behavior with the organization average. While the underlaying algorhitm is rather complex, it basically comes down to the following classification:
Light: below or on average with the overall average;
Moderate: causing a load that is 10 - 100 times more than the average;
Intensive: causing a load that is 100 – 1,000 times more heavy than average;
Extreme: causing a load that is more than 1,000 times more heavy.
For each class of user account, this chart shows their impact on the total user demand caused in the 7 days analyzed. This total demand is expressed in 6 measures. The numbers behind the legend indicate the number of users in that class.
Details for the 10 most heavy accounts are given in the next slide.
This analysis shows the demand caused by the 10 most heavy user accounts, during the 7 day period of analysis. The heaviest account is shown on top.
With the DNA factsheet, the customer can identify in detail which servers and databases were touched by these heavy user accounts. This knowledge gives a solid indication if the traffic is really necessary or not.
Mis-configured desktops or functional systems such as e.g. fax or archiving solutions often cause an extreme load on the network and servers.
This slide gives an indication of over capacity in the server park. Each server is classified according to the maximum number of concurrent end user sessions it has served, over the 7 day analysis period.
Load levels on servers in the yellow area are very low and can often be redistributed onto other servers. Functional servers (smtp, hubs, blackberry, sametime) often show very low session levels. Use the factsheet to verify which servers fall in each category.
Customers with a highly centralized server park often show less over capacity than customers with a very decentralized server park.
This analysis topic reveals the total session concurrency caused by end users working on Domino servers, in each of the 168 hours (7 days) that were analyzed.
For time-series charts, the timezone reflected on the horizontal axis is equal to that of the workstation that was used for the data collection.
Network compression is a feature that was introduced with Lotus Notes and Domino release 6. The compression ratio we see at customers is around 40%, so the benefits of this feature are significant.
For network compression to function properly, a setting needs to be in place at both ends of the connection, so both on all servers as well as on every desktop. This is usually not the case. With this analysis, we show how much of the total network traffic was making use of compression (pie chart). In addition, DNA is presenting for all servers and users if compression has been enabled or not.
Customers that make use of other compression solutions in their network, may want to reverse the purpose of this analysis. In these situations, customers may want to disable Notes network compression.
The factsheet reveal which servers and users make use of compression.
Description:
This analysis produces insight into how capacity is being utilized in the database landscape. The left hand table shows how many databases have been deployed, and how many of these have been touched by end users during the analysis period. The right hand table does the analysis on the same databases, but then for storage that these databases occupy.
Interpretation:
This analysis gives a solid indication for the amount of over capacity that exists in the database landscape. Organizations with a highly decentralized server park often show higher levels of over capacity.
Many organizations have seasonal applications that are used once per month, quarter or year. As this DNA analysis covers a 7 day period, these kind of applications may show as unused.
Side note:
Databases that are not recorded in the catalog.nsf are not included in this analysis, therefor the numbers presented in this analysis may slightly differ from those presented in other analysis topics
When the Namelookupcachepool utilization is clipping this could be an indication that the amount of memory assigned to the namelookupcache is insufficient. Default value is 16MB.
Namelookup requests that are not found in the cache, result in the server going to disk to read the data. This slows down server performance.
See:
http://www-10.lotus.com/ldd/stwiki.nsf/dx/Optimizing_Name_Lookup_Sametime_server