Eight steps to migrate IBM Agent Builder solutions

With the introduction of IBM Monitoring V8 a complete new user interface has been introduced and the agents also changed in the way how communicating with the server.

That implies, that all existing Agent Builder solutions you created have also to change. Agents created for your ITM V6 deployment have also to be adopted.

In this article I want to give a sample for one of my agent builder solutions, I’ve created for OpenVPN. I extend the solution in that way, that I can use it under ITM V6 as well as under APM V8.

The work to be performed is pretty limited. The documentation describes a few major prerequisites, to successfully deploy your agent to a IBM Performance Management infrastructure:

  • A minimum of one attribute group to be used generate an agent status overview dashboard.

  • These single row attribute group (or groups) will be used to provide the four other required fields for this overview

    • A status indicator for the overall service quality

    • A port number, where the service is provided

    • A host name where the agent service located

    • An IP address where the monitored service resides

For more details, please consult the documentation.

In my situation, these attributes were not provided with my ITM V6 agent builder solution. So I expanded my existing solution:

  1. Changing the version number of my agent builder solution (optional)

  2. Create a new data source “openvpnstatus_sh”, which is a script data provider delivering one single line with all attributes defined to it.
    Frame1

  3. The attribute “ReturnCode” will be used later on to describe the overall status of my OpenVPN server. So I have to define the good value and the bad value (see documentation for more details)

  4. Make sure, that the check box under Self Describing Agent is activated.

  5. Run the Dashboard Setup Wizard to produce the dashboard views

    Please make sure, that you checked “Show agent components in the dashboard”! Otherwise…

  6. Now you can select the different values:

    1. Status

    2. Additional attributes for the summary widget

    3. Select the attribute groups to be displayed in the details view for the agent

    4. Define the name of the monitored component

    5. Define the hostname where the software is running

    6. Define the IP address

    7. Define the Port Number

    8. Click “Finish”

Now your agent is almost ready. Start the Agent Builder generation process using the icon

You can generate ITM V6 and IPM V8 compatible agent packages in one step. If you do so, and you want to support Cognos Reporting, then leave “Cognos reporting” checked. While Cognos Reporting isn’t supported for new V8 agents at this time, leave it unchecked when you don’t want to support ITM V6. The agent package size will be much smaller without Cognos Report support.

After successful generating the agent package you will find the following files and directories:

  • smai-openvpnserver-08.13.00.00.tgz

  • subdirectory k00 containing fixlets for BixFix deployment of the new generated agent

After expanding the archive file you find a well known set of files:

The install routines are a now able to verify which kind of agent deployment (V6 or V8) is required. Depending on the given agent install directory, the routine determines automatically, which agent framework has to be installed.

Remark:

The Agent Builder agent has no info about where the monitoring infrastructure server is running. It will connect to the same server as the previously installed OS agent. In turn, any Agent Builder generated agent cannot run without a previously installed APM V8 agent.

Wrap-Up:

It is pretty simple to create agents for ITM V6 and APM V8 at the same time. The Agent Builder supports the two very different user interfaces in one generation process.

Only for the Dashboard definition new required data must be available, but it is simple to gather.

If you have any further questions regarding the IBM Agent Builder, drop me a message.

WebSphere MQ Monitoring — Comprehensive Workspace Sample Using Navigator Views

This solution presents ITM V6.x enhanced comprehensive workspaces in a custom navigator view for OMEGAMON XE for Messaging V7 (aka ITCAM for Application, MQ Agent).

OMEGAMON XE for Messaging V7 delivers a lot of useful workspaces with very detailed information on a single WebSphere MQ server. This solution presents a complete new approach to navigate to the details of a single MQ resources. The inspection of single objects is more context driven and spans WebSphere MQ server bounds.

The structure of the new navigator is inherited from the original product, so that the user will feel comfortable with the solution. When installed, situations are associated to the new navigation tree.

This solution should highlight the capabilities of the ITM V6 infrastructure and the power of using ITM navigator views in a production environment to identify potential upcoming problems in WebSphere MQ infrastructures.

The linking capability enables the users to follow the path of the message flows across system borders and get a more comprehensive view of the entire object chain making up the communication path in WebSphere MQ. It enables users to quickly identify the root cause of message flow problems.

The PDF document gives you all required details.

The archive file contains the export of the ITM Navigator, which could be used for a quick implementation.

Implementing a 32-bit ITM Agent under Enterprise Linux 64-bit

Implementing the ISM Agent (and other 32-bit ITM Agents) under Linux 64-bit is a little bit tricky, because it requires a 32-bit framework for the ITM base libraries.

The documentation covers all required steps, but is a litlle bit spread across IBM’s website.

In the prerequisites documentation it is stated, that the Tivoli Agent Framework is required in 32-bit architecture.

The installation is a little bit more effort, because it requires the ITM base image to be mounted and the installation of the “Tivoli Enterprise Services User Interface Extensions” has to be performed. The following modules where required on the system I used:

yum install compat-libstdc++-33.i686

yum install libXp

yum install ksh

yum install libstdc++.i686

See the general support matrix for the ISM agent. In the footnote area the required modules are listed.

Because I already installed the OS agent before, the Korn Shell has been installed before.

From the ITM install image (here: ITM_V6.3.0.6_BASE_Linux_x64) I started install.sh:

I accepted the shutdown of any running ITM Agent, and requested to install products to the local host.

After agreeing to the license agreement, the challenge is, to pick the choice to install for another operating system. As you can see from the list of the already installed components, the “Tivoli Enterprise Services User Interface” is already installed. But it is the 64-bit version only. For the ISM agent the 32-bit version is required. So we pick the option 6.

I picked option 3, “Linux x86_64 R2.6, R3.0 (32 bit)” as the OS platform.

In the next step I picked option 2, “Tivoli Enterprise Services User Interface Extensions”.

I used the ITM provided prerequisite checker to verify the software requirements again, and then accepted the installation of the new component.

I accepted all subsequent default values for the setup process and ended with the installation of the 32-bit Tivoli Agent Framework.

After having this successfully installed, the subsequent ISM Agent works really straight forward.

After successfully executing the install.sh from the ISM install image, you simply could accept the default values, if no special requirements have to be met.

After a successful installation the cinfo output should look somehow like this:

If you have further questions, please do not hesitate to drop me a reply below. I will come back to you as soon as possible.

Follow me on Twitter @DetlefWolf to continue the conversation.

Traveling To The Cloud – Predict

As I stated in my previous post, traditional monitoring approaches focusing on named systems do no longer make sense. In an agile cloud environment the system name does not matter, so in turn the performance values from this system don’t.

In such a situation the prediction approach also has to change. The data flowing into IBM Operations Analytics – Predictive Insights should no longer identify a single system nor a single instance of a resource. It should represent the sum of resources or the average usage value. So let us review a few simple examples:

While we monitor the key performance metrics of the system instance with our monitoring agents like

  • Disk I/O per second

  • Memory Usage in Megabytes

  • Network Packages sent and received

  • CPU percentage used

we feed the following values into our prediction tool:

  • SUM(Disk I/O per second) across all used OS images

  • SUM(Memory Usage in Megabytes) across all used OS images

  • SUM(Network Packages sent and received) across all used OS images

  • AVG(CPU percentage used) across all used OS images

IBM Monitoring stores historical data in the Tivoli Data Warehouse. A traditional system setup might directly leverage the data stored in the data warehouse to feed the prediction tool. With the elastic cloud approach we should add some new views to the database, which enable the required summarized data view as described above.

To ensure that a single operating system instance isn’t overloaded a traditional resource monitoring has to be deployed to each cloud participant. Distribution lists from IBM Monitoring will help to do this automatically.

These list of systems are also important to maintain the efficiency of the view’s introduced for the prediction.

The following table is required in the WAREHOUS database:

This table represents the distribution list known from IBM Monitoring.

Based on this table we can create views like the one below: With this new view we are now able to feed data regarding the disk usage into the IBM Operations Analytics – Predictive Insights tool.

The column “CloudName” is useful to identify records for streams. The “TimeFrame” column works as time dimension.

Five streams are the result from the table above:

  • AllReadRequestPerSecond

  • AllWriteRequestPerSecond

  • AvgWaitTimeSec

  • AllReadBytesPerSec

  • AllWriteBytesPerSec

All streams are generate for each single instance “CloudName”.

 In the Predictive Insights Modeling Tool the view is selectable (as a table), so that the generation of the data model is pretty straight forward.

 The SQL line



makes sure that TimeFrame is a Candle Time Stamp which is known to IBM Operations Analytics – Predictive Insights tool.

This sample shows how a data model for the cloud might look like.

With moving more and more systems to the cloud and becoming more and more agile while serving the IT workload, the monitoring approach has to become more agile as well. Also the point of view which key performance metrics matter have to change. But as you can see, the data is there, we only have to change the perspective a little bit.

So what is your approach? What requirements do you see arising while moving your monitoring and prediction tools to the cloud?

Follow me on Twitter @DetlefWolf, or drop me a discussion point below to continue the conversation.

In my next blog I will share a few ideas how to automate the implementation of IT monitoring in a cloud environment.

IT Service Management – Traveling To The Cloud

More and more customers are moving to cloud architectures to fulfill the alternating resource requirements in the IT. Traditional monitoring approaches with checking the availability of a single system or resource instance does only make limited sense in this new era. Resources are provisioned and removed on dynamic request and have no long term life date.

It no longer matters whether a named system exists or not, it is about the service, and its implementing pieces. The number of systems will vary in accordance to the workload covered by the service. In some cases the service itself may disappear, when it is not permanently required. The key metric is the response time the service consumers achieve. But how can we assure this key performance metric at the highest level, without being hit by an unpredicted slowdown or outage.

We need a common monitoring tool watching the key performance metric keys on a resource level and frequently check the availability of these resources, like:

  • Disk

  • Memory

  • Network

  • CPU

Application containers will also be handled like resources, e.g.:

  • Java Heap

  • Servlet Container

  • Bean Container

  • Messaging Bus

Also resources from database systems, messaging engines and so on are monitored. With IBM Monitoring we have a useful and easy to handle tool, available on-premise and in the cloud.

With this data achieved by the monitoring tool, we can now feed a predictive insight tool. As described in a previous post, monitoring is the enabler for prediction. Prediction is a key success factor in cloud environments. It is essential to understand the behavior of an application in such an environment in a long term.

The promise of the cloud is, that an application has almost unlimited resources. If we are getting short on resources, we simply add additional ones. But how could be detect, that the application is behaving somehow suspicious? Every time we are adding additional resources these are eaten up by the workload. Does this correlate to the number of transaction, to the number of users or other metrics? Or is it a misbehaving application?

We need a correlation between different metrics. But are we able to oversee all possible dependencies? Are we aware of all these correlations?

IBM Operations Analytics Predictive Insights will help you in this area. Based on statistical models, it discovers mathematical relationships between metrics. A human intervention is not needed to achieve this result. The only thing to happen is, that the metrics are provided as streams in a frequent interval.

After the learning process is finished, the tool will send events on unexpected behavior, covering uni-variate and multivariate threshold violations.

For example, you have three metrics:

  • Number of request

  • Response time

  • Number of OS images handling the workload

Raising number of OS Images wouldn’t be detected by a simple threshold on a single resource, covered by the traditional monitoring solution.

Either the response time shows no anomaly nor the number of users does. Also the correlation between these to data streams remains inconspicuous. However, adding the number of OS images shows an anomaly in the relation to the other values. This could lead to a situation, where all available (even the cloud resources are limited, because we can’t afford it) resources are eaten up. In this situation our resource monitor would send out an alarm at a much later point of time.

For example, first, the OS agent would report a high CPU usage. Second, the response time delivered to the end users would reach a predefined limit. The time between the first resource event and the point in time where the user’s service level agreement metric (response time) is violated is too short to react.

With IBM Operations Analytics Predictive Insights we earn time to react.

So what is your impression? Did you also identify correlations to watch out for after analyzing the reason for a major outage and the way to avoid this outage?

Follow me on Twitter @DetlefWolf, or drop me a discussion point below to continue the conversation.

In my next blog I will start a discussion which values make sense to be fed into a prediction tool.

Sybase Monitoring

While we see big changes in the way we deliver monitoring services, the functionality of the tool is still a proof point when talking with subject matter experts.

The IBM Monitoring suite offers a huge coverage for a lot of systems, applications and containers. Find a list of supported agents on the IBM Service Engage website.

The Sybase ASE Agent enables the monitoring of SAP Sybase database servers. The agent fully integrates into the IBM Monitoring infrastructure.

The installation and configuration guide gives you insight how things fit together and how the product will be installed.

This agent is bundled with the IBM Application Performance Management suite.

The IBM Tivoli Composite Application Manager Agent for Sybase ASE Reference Guidegives you detailed information about:

  • Attribute groups and attributes
  • Workspaces
  • Situations
  • Take Action commands
  • Event mapping
  • Workspaces workgroups mapped to tasks
  • Upgrading for warehouse summarization
  • Sybase agent data collection

The agent is currently supported on-premise only and still uses the IBM Tivoli Monitoring V6 infrastructure.

SCAPI: Preparing your system — Software Packages

Before you can install SmartCloud Analytics Insight (SCAPI) you have to meet the software prerequisites on the RedHat Enterprise Linux Server system you are using to host the SCAPI Data Server Components. Currently only RHEL 6 64-bit is supported.

The documentation names the requirements in several locations of the installation brochure.

I’m using the following command stack to make sure that all software packages are installed:

  • yum -y install libstdc++.i686
  • yum -y install *libstdc++-33*.i*86
  • yum -y install openmotif22*.*86
  • yum -y install pam.i686
  • yum -y install libXpm.i686
  • yum -y install libXtst.i686
  • yum -y install freetype.i686
  • yum -y install libmcpp.i686
  • yum -y install libXdmcp.i686
  • yum -y install libxkbfile.i686
  • yum -y install libpciaccess.i686
  • yum -y install libXxf86misc
  • yum -y install libXm.so.4
  • yum -y install ksh*
  • yum -y install libstdc++.*
  • yum -y install *libstdc++-33*
  • yum -y install openmotif22*
  • yum -y install compat-glibc
  • yum -y install pam
  • yum -y install libXpm
  • yum -y install libXtst
  • yum -y install freetype
  • yum -y install xorg-x11-xinit
  • yum -y install Xorg
  • yum -y install firefox
  • yum -y install openmotif
  • yum -y install atlas
  • yum -y install compat-libgfortran-41
  • yum -y install blas
  • yum -y install lapack
  • yum -y install dapl
  • yum -y install sg3_utils
  • yum -y install libstdc++.so.6
  • yum -y install libstdc++.so.5
  • yum -y install java-1.7*-openjdk.x86_64
    Java is required to get the prerequisite checker executed delivered with the IBM InfoSphere Streams software.
    The packages below are installed because the InfoSphere Streams checker.
  • yum -y install libcurl-devel.i686
  • yum -y install libcurl-devel.x86_64
  • yum -y install fuse-curlftpfs.x86_64
  • yum -y install libcurl.i686
  • yum -y install libcurl.x86_64
  • yum -y install perl-Time-HiRes
  • yum -y install perl-XML-Simple*
  • yum -y install gcc-c++

This command stack includes only those packages, which are provided by RedHat Satellite server.

After you installed all the packages above you have to add the provided RPM package as documented in the installation manual.

I’ve used the following command:

#
# Install provided InfoSphere RPM Prerequisite
rpm -Uvh <streams_unpack_folder>/rpm/*.rpm

Having all this packages installed, allows you to install all SCAPI software components.

Implementing the OMNIbus WebGUI in silent mode

After installing the OMNIbus WebGUI you have to implement the properties to direct the WebGUI server to the correct OMNIbus server and the correct authorization source.

To run the configuration wizard in silent mode, which is essential for repeated installations, use the ws_ant.sh shell script as documented in the manual.

The documentation suggests that you can locate the OMNIbusWebGUI.properties file in any location. This is not really true.

Inside this file other files are referenced which are provided in the same directory as the properties file.

The following approach worked for me:

I’ve installed the OMNIbus WebGUI server with default values, so it ended up in /opt/IBM/netcool/omnibus_webgui/ directory. In the subdirectory bin I’ve found the OMNIbusWebGUI.properties file.

Change the file accordingly to your installation and leave it in this directory.  As documented in the manual execute the ws_ant.sh script from this directory, adding the required activity.

The ws_ant.sh requires the build.xml in the working directory. build.xml file describes the different callable activities. These are

  • configureOS
  • resetVMM
  • configureVMM
  • restartServer

Through this build.xml file the WebSphere script execution is controlled.

In this xml file, the OMNIbusWebGUI.properties file is referenced with hard coded path names. Additionally, other XML files are referenced which are expected in the same directory.

So, edit the properties file in the location where you find it. And then execute the ws_ant shell script from this directory…

IBM Monitoring goes SaaS

Big changes in the IT market are taking place. We see cloud services all around changing the delivery model of software from product sale to a software as a service model.

IBM also delivers more and more parts of its portfolio in a software as a service model. One of the very first offerings is IBM Monitoring. Based on the IBM Service Engage platform the monitoring infrastructure is delivered to the customers.

But how does it work?

IBM delivers the server components in a Softlayer® data center. The infrastructure is hidden behind a firewall in combination with a reverse proxy. All customer agents and client devices are connected by using the HTTPS port (Port 443) on the announced service address.

How are the different customers separated from each other?

The user clients are connected to the correct customer specific monitoring environment based on the user credentials given on the login page.

The agents have customer specific credentials in their setup and are generated for each customer exclusively. These agents are provided upon registration for the service and can be downloaded on customer request.

How many agents should a customer have?

Well, there is no minimum number of agents a customer has to request to become eligible for IBM’s monitoring offering. However, there is a maximum number of agents a single instance of this monitoring infrastructure can serve. Depending on the complexity of the monitoring rules you apply we expect a maximum of about 1000 agents per infrastructure instance.

What kind of agents are available?

The following agents are currently available for the SaaS offering:

  • Operating Systems

    • Windows OS

    • Linux OS (RHEL, SLES)

    • AIX

  • Databases

    • DB2 UDB

    • Oracle DB

    • Microsoft SQL Server

    • Mongo DB

    • MySQL

    • PostGreSQL

  • Response Time Monitoring

  • Microsoft Active Directory

  • Virtualization Engines

    • KVM

    • System P AIX

  • JEE Container

    • WebSphere Application Server

    • WebSphere Liberty

    • Apache Tomcat

  • Languages and Frameworks

    • Ruby on Rails

    • Node.js

    • PHP

    • Python

There are several other agents planned to be released within the next few weeks or months, but I’m not authorized to write about in detail in this blog. If you want to more details, or if you have specific requirements, drop me a message, and I’ll come back to you with more specific information.

How are these agents installed?

The installation procedure is now pretty simple. The following videos show the installation on Linux and Windows. After downloading the appropriate packages for the target OS platform, the installation process can be initiated. The redesigned installation process on Linux follows now the standard installation rules for the OS platform (here now RPM).

The new IBM Monitoring is different from the previous one. The new lightweight infrastructure is available within a few minutes. The agents are easy to install and are simple to configure. The monitoring solution comes with a new user interface based on HTML without the need of any Java Runtime Environment. Because of that, the user interface is now also available on touch pads and smart phones.

Follow me on Twitter @DetlefWolf, or drop me a discussion point below if you have further question regarding the new IBM Monitoring.

 

Raising IT Monitoring Acceptance

After publishing my blog “IT Monitoring is out of style?” a discussion was initiated by several followers, how IT Monitoring acceptance could be achieved within the system administration groups.

To make that clear, system admins are not preventing monitoring in general, they complain about too often, toounspecific alerts which stops them from doing their daily business.

This leads to the refusal of such monitoring services. So what to do to get a commitment from the system admin team.

What system admins really hate?

  • Alerts, which indicate minor issues that could be also fixed later on within normal business hours, deranging them within their leisure time.

  • Alerts, which flip on and off within intervals (bouncing alerts)

  • Alerts, which are out of their responsibility

Well, I can imagine another bunch of bullet points, what system admins do not like, but remembering my own time as a system programmer, I believe these are the real eye-catchers in this area.

But there are also reasons, why they support a monitoring solution. They want to avoid the following situations:

  • Being hit by an outage of a service without an early warning

  • Upset users are floating the support team with calls, due to poor response times

You can fill this list with tons of other statements, so feel free to drop me your top reasons in the comment section.

 What really changed over the last years in the IT department is the service orientation. Formerly, we watched the system health, rather than the service health. Today we focus on the service health. And this offers a new approach to increase the acceptance of IT Monitoring solutions.

 

End-To-End-Measurement

A business partner, currently implementing a monitoring as a service model for small businesses, stated the requirement to get alerted only, if key business IT functions of its customer are on risk or are already out of service. We used the Internet Service Monitor to check the named services (like email, internet accessibility, phone server, and so on). By using the approach of the End-To-End-Measurement the detection of critical service status is assured. For more sophisticated services like Web Applications or SAP Transactions the Web Response Time Monitor delivers deep insight into transactions. To track down the availability and performance of transactions in business off hours, the Robotic Response Time agent delivers valuable insight and informs about unexpected outages.

All events coming from this discipline are good candidates to be escalated also in business off hours.

Resource Monitoring

Resources, like CPU usage, memory or disk consumption, database buffer pools, JEE heap size or whatever are very important metrics to analyze the health of the operating or application system. A single metric is only an indicator but too often not a good signal to throw a high critical alert. This is exactly the question discussed in “Still configuring thresholds to detect IT problems? Don’t just detect, predict!” But yes, there might be single metrics indicating a hard stop of a system or application, which requires immediate intervention. And this knowledge comes often from resource monitors. Additionally, the resource monitors gather important data for historical projections and capacity planning. Based on this data, predictive insight becomes actionable, and gives us another source of meaningful events. Events detected by Predictive Insights are also good candidates to be escalated even in business off hours, if you are interested in avoiding interruptions in IT services.

Suppressing Events

When I was a system programmer, my team’s main goal was to have as little as possible calls in business off hours. We tried to catch up with the events – also with the less important ones – within our standard office hours. To achieve this goal, we created rules, what kind of events – or what combination of events – are critical enough, to initiate a call in business off hours. In normal business hours we monitored the system with an extended set of rules to get early indications of unhealthy system conditions. This helped us to maintain a pretty tidy IT environment, causing relatively seldom unexpected system behavior. All these extended events were suppressed by the event engine (here OMNIBUS) in business off hours. When we came on-site again, we reviewed the list of open and already closed events, recapped the number of occurrence in the monitoring system to understand the situation we’ve missed while being off-site.

In summary, there are ways to get the commitment from the system administrator team for a monitoring solution. The system administrator’s goal is to have a high available, high performance system environment with fully functioning service running on it. IBM Monitoring tools help them to achieve this goal and offer them the flexibility to get filtered information about the system status as they need it.

For those customers, trying to avoid maintaining a monitoring infrastructure by themselves, the new Monitoring as a Service offering fits perfectly.

So what is your impression? Are you also discussing with system administrators about a powerful monitoring?

Follow me on Twitter @DetlefWolf, or drop me a discussion point below to continue the conversation.