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Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13
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Back to the Future: Understand and Optimize your IBM Notes and Domino Infrastructure, #dd13

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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 …

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!

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  • 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
  • Transcript

    • 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
    • 3. Session content: (Some) results of a Domino Network Analysis Health Check
    • 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
    • 12. DNA Bechmark Time Online 60 25 50 20 40 15 30 10 20 5 10 o i t a r u D n s e S ) n o i r e p s m ( ) e p s r u o h ( m T e i l n O 30 - Demo Inc. Lowest Customer DNA Average Highest Customer Session Duration 3 1 4 28 Online Time 37 5 17 44 -
    • 13. DNA Bechmark Document Reads/Writes 1.000 3.000 750 2.000 500 1.000 250 s d a R t n e m u c o D s i r W t n e m u c o D 4.000 - Demo Inc. Lowest Customer DNA Average Highest Customer Document Reads 1.983 304 2.093 3.652 Document Writes 301 152 385 906 -
    • 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 v r o s t n e i l c ) c r e p s t b o l i k ( 10 40 n i l c o t v r e s ) c r e p s t b 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
    • 21. 20 16 12 08 04 2010-10-27 00 20 16 12 08 04 2010-10-26 00 20 16 12 08 04 2010-10-25 00 20 16 12 08 04 2010-10-24 00 20 16 12 08 04 2010-10-23 00 20 16 12 08 04 2010-10-22 00 20 16 12 08 04 2010-10-21 00 Concurrent User Sessions End User Demand Session Concurrency 2,500 Max Observed Maximum : 2,167 2,000 1,500 1,000 500 0
    • 22. 20 16 12 08 04 2010-10-27 00 20 16 12 08 04 2010-10-26 00 20 16 12 08 04 2010-10-25 00 20 16 12 08 04 2010-10-24 00 20 16 12 08 04 2010-10-23 00 20 16 12 08 04 2010-10-22 00 20 16 12 08 04 2010-10-21 00 Avg per Hour End User Demand Document Reads 2,500 Max Observed Average: 1,911 2,000 1,500 1,000 500 0
    • 23. 20 16 12 08 04 2010-10-27 00 20 16 12 08 04 2010-10-26 00 20 16 12 08 04 2010-10-25 00 20 16 12 08 04 2010-10-24 00 20 16 12 08 04 2010-10-23 00 20 16 12 08 04 2010-10-22 00 20 16 12 08 04 2010-10-21 00 Avg per Hour End User Demand Document Writes 800 Max Observed Average: 678 700 600 500 400 300 200 100 0
    • 24. 20 16 12 08 04 2010-10-27 00 20 16 12 08 04 2010-10-26 00 20 16 12 08 04 2010-10-25 00 20 16 12 08 04 2010-10-24 00 20 16 12 08 04 2010-10-23 00 20 16 12 08 04 2010-10-22 00 20 16 12 08 04 2010-10-21 00 Avg per Hour End User Demand Database Transactions 16,000 Max Observed Average: 13,498 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0
    • 25. 20 16 12 08 04 2010-10-27 00 20 16 12 08 04 2010-10-26 00 20 16 12 08 04 2010-10-25 00 20 16 12 08 04 2010-10-24 00 20 16 12 08 04 2010-10-23 00 20 16 12 08 04 2010-10-22 00 20 16 12 08 04 2010-10-21 00 Avg per Hour (kbps, to clients) End User Demand Network Bandwidth Consumption 25,000 Max Observed Average: 23,616 20,000 15,000 10,000 5,000 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
    • 33. Applications Touched by Users
    • 34. Server Health Checks The following Slides show several higlights of the platform checks that were performed
    • 35. Namelookup Cache Utilization on server server_17 Set this cache higher to prevent 100% utilization 2 other server(s) have similar issues 100 % 75 50 25 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
    • 36. NSF Events & Monitor Pool Size Utilization on server server_17 Cache Size is Sufficient No issues detected on other servers 100 % 75 50 25 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
    • 37. Miss Rate on Database BufferPool on server server_24 Longer periods of High Miss Rate may indicate Performance Constraint % 25 5 other server(s) m ay have sim ilar issues 20 15 10 5 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
    • 38. Database File IO in KBytes per Second Read activity on server server_16 Show ing busiest server 3,500 3,000 2,500 2,000 1,500 1,000 500 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
    • 39. Database File IO in KBytes per Second Write activity on server server_26 Show ing busiest server 1,000 900 800 700 600 500 400 300 200 100 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
    • 40. Full Text Index Utilization Search activity on server server_17 Show ing busiest server 0:00:22 0:00:13 0:00:09 0:00:04 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 0:00:00 21-Oct-10 0 Hours:Minutes 0:00:17
    • 41. Mail Delivery Rates Categorized by Msg Size Msgs per Hour, Show ing all servers 30,000 25,000 20,000 15,000 10,000 5,000 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
    • 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
    • 55. Thank you!!!
    • 56. 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:
    • 57. Grazie agli sponsor per aver reso possibile i Dominopoint Days 2013! Main Sponsor Vad sponsor Platinum sponsor Gold sponsor

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