New Update: OpsLogix VMware Management Pack V1.3.8.0

New Update: OpsLogix VMware Management Pack V1.3.8.0

We’re happy to announce a new update release of our VMware Management Pack for SCOM 2012/2016. This latest release is upgradable starting from V1.3.0.0 or later.

The update is downloadable from the customer download area.

VMware Management Pack V1.3.8.0

The new VMware Management Pack has been optimized on multiple fronts, such as:

  • The Snapshot Age monitor does not fail anymore due to incorrect regional settings (if not set as US).
  • The License monitor no longer shows a critical unhealthy state when 100% of the licenses are used.
  • After running a connection test in the UI, the first healthy server pool is selected instead of the first server pool.
  • When a data-store is set inactive in vCenter the data-store monitor workflows does not fail.

For more additions, changes, and fixes please refer to release notes.

 

Update Instructions

  1. Import the updated management packs.
  2. When imported wait +/- 10 minutes to get the updated MPs distributed in your SCOM environment.
  3. In the SCOM console, go to the “Administration” folder -> “Resource Pools” and select the resource pool(s) that is responsible for the VMware monitoring.
  4. Select the “view resource pool members..”. For every member, access to the server and follow the step below:
  5. Restart the SCOM agent

Team OpsLogix

 

Join Our Webcast With Approved: AI For IT-Operations: How To Classify, Train & Escalate Alerts From SCOM

Join Our Webcast With Approved: AI For IT-Operations: How To Classify, Train & Escalate Alerts From SCOM

WHAT’S IT ALL ABOUT?

The evergrowing amount of devices to be monitored in combination with high availability requirements makes it more urgent to review internal processes.

Introducing machine learned automation involves short-handed removal of manual processes that can be performed by a machine according to predetermined consistent routines.

In this webinar you will get an introduction and real world scenario how to:

  • Use pre-actions to classify and enrich your alert data
  • Train a machine learning model
  • Escalate to different channels depending on the predicted destination
  • Integration to ServiceNow with a bi-directional connector
  • Tag and analyze your escalated alerts

WHEN?

 

WEDNESDAY 4TH OF APRIL 2018

 

1st session

  • Amsterdam (Netherlands) 10:00 CEST
  • New York (USA – New York) 04:00 EDT
  • London (United Kingdom – England) 09:00 BST
  • Melbourne (Australia – Victoria) 18:00 AEST

2nd session

  • Amsterdam (Netherlands) 19:00 CEST
  • New York (USA – New York) 13:00 EDT
  • London (United Kingdom – England) 18:00 BST

Melbourne (Australia – Victoria) 03:00 AEST

PLEASE NOTE, ONLY 25 SPOTS PER SESSION. FIRST COME FIRST SERVE!

CONQUER YOUR SPOT NOW! CLICK HERE

 

3 Reasons To Implement Automation & Machine Learning For IT-Operations

3 Reasons To Implement Automation & Machine Learning For IT-Operations

A guest blog by Jonas Lenntun from Approved Sweden.

Clearly, we’ll automate!

Automation and efficiency go hand in hand and is something that has been mentioned in IT since the 70’s. Nevertheless, 40 years on, and the majority of companies still have to internalize and embrace automated processes.

The growing amount of devices to be monitored in combination with higher availability requirements makes it more urgent to review their internal processes. Especially when digitization is introduced with more and more critical e-services that are expected to be available 24 hours a day.

Introducing automation involves short-handed removal of manual processes that can easily be performed by a machine according to predetermined routines – in a shorter and the same way, each time.

Some processes have already come a long way in this. Among other things, orders of equipment, user setup or server update, along with a lot of administrative work.

At the IT department, there are three interesting areas with high potential to automate manual processes to become more efficient, reduce shorter lead times and reduce repetitive work.

What can machine learning add?

Machine learning has previously been perceived as not directly relevant to traditional monitoring and incident management. But more and more people realize that it is a matter of highest relevance to simplify everyday life, in every aspect.

Instead of manually escalating incidents or sending out notifications to readiness through complex and blunt regulations, machine learning can be applied.

We can relatively easily train a machine to automatically identify patterns and then perform the actions we want in a very short time.

We have already begun with automation.

Most likely, you have already begun implementing automation in several areas. Since automation is such a wide-ranging area, this article focuses on activities that increase the value of what the monitoring delivers and is more relevant to you in IT operations.

Three important automation areas

Escalation

At first sight, escalation is considered a rather simple process to automate. However, the more complex the rules are for different types of alarms to be distributed to different groups, depending on certain criteria, the more difficult it will be to easily control these rights through a static regulatory framework.

Instead of building complex script or programs, you can instead look at an alarm and train where to send. How it then comes to the conclusion is where machine learning comes in its right place. It finds patterns we did not know.

Large time savings can be made by shortening the processing time due to the fact that the cases are sent to the correct grouping without having to wait for a manual decision.

Recovery

Many errors that occur at the operating system level or around inadvertently stopped services can be easily reset.

Even though it is possible to configure it on a Windows service to start up if it is stopped, it is better to allow a monitoring system to capture the error. Since a monitoring system can both restore and maintain statistics, it will be easier to monitor any recurring interference. These statistics also provide a good basis for the problem process with the supplier – the dialogue is based on data instead of rumors and empathy.

Many restorations need to be clearly defined, but there is also the possibility to train a model that learns which rescues are to run in order to minimize complexity through machine learning.

Diagnostics

Many errors that occur may be difficult to automatically reset, but this does not mean we should exclude automation.

If a disc indicates that it is running out of space, then the human factor may be needed to determine what can be cleaned. But that does not prevent us from collecting diagnostic information of the person who will be performing the task.

Automation of diagnostics can be to look at which of the largest directories contain the largest files, or to insert a graph of disk usage into the analysis process.

Here too we can use Machine Learning to determine what to run or not.

How do we show results?

Introducing automation and machine learning in IT operations has many advantages. Since many things happen without anyone even discovering it, follow-up is one of the most important parts to improve results after the introduction.

There are many important key figures to look for before and after the introduction, but the most important thing is of course “Mean Time To Repair”, shortened MTTR. In short, the time it takes for the alarm to be resolved and closed.

Because we can divide  automation into three different categories, we can measure:

  • Recovery time overall on the alarms that are automated compared to those that are not
  • Automation degree overall – What is the percentage of alarms automated
  • Automation rate per queue – What is the percentage of alarms automated per destination
  • Recovery time of automatically escalated alarms compared to those done manually
  • Recovery time per escalated destination
  • Recovery time of automatic reset compared to manual handling
  • Recovery time of automated diagnostics compared to manual handling

These are just a few key figures that have a great effect in detecting the results of automation and machine learning.

Below you will find an example of the Approved operational analysis tool “IT Service Analytics” (in Swedish) which, with data from the Microsoft System Center Operations Manager, can show results after the introduction of automation.

 

Summary

Automation of IT operations is a topic that can not be ignored if you don’t want to risk getting lost. The challenge at first is to decide how and where to start. Building down and up and analyzing where to put the effort is a common tactic. With automation, basically, you suddenly get action that runs 24/7 on all your deliveries, reducing the need for emergency preparedness.

We hope you had a good introduction to why you just need to look at automation and machine learning in your organization.`

For more information, have a look at Approved’s concept of Digital Operations or email us at info@opslogix.com.