System Center Operations Manager 2012: Update Rollup 12

System Center Operations Manager 2012: Update Rollup 12

Recently Microsoft released the update rollup 12 for System Center Operations Manager 2012 R2 users, including installation guidelines.

Issues that are fixed in System Center Operations Manager

  • When you try to upgrade System Center Operations Manager 2012 R2 Reporting Server to System Center 2016 Operations Manager reporting server, the upgrade fails for the following configuration:
  • Server A is configured as System Center 2012 R2 Operations Manager including Management Server.
  • Server B is configured as System Center 2012 R2 Operations Manager, including Operations Manager Database (OpsMgrDB), Operations Manager Data Warehouse (OpsMgrDW) and Operations Manager Reporting Server.

( X ) Management Server Upgraded Check

The management server to which this component reports has not been upgraded.

  • Recovery tasks on “Computer Not Reachable” messages in the System Center Operations Manager Monitor generate failed logons for System Center Operations Manager Agents that are not part of the same domain as the Management Groups.
  • When a Management Server is removed from the All Management Servers Resource Pool, the monitoring host process do not update the Type Space Cache.
  • SHA1 is deprecated for the System Center Operations Manager 2012 R2 Agent and SHA2 is now supported.
  • Because of incorrect computations of configuration and overrides, some managed entities go into an unmonitored state. This behavior is accompanied by event 1215 errors that are logged in the Operations Manager log.
  • IntelliTrace Profiling workflows fail on certain Windows operating system versions. The workflow cannot resolve Shell32 interface issues correctly.
  • There is a character limitation of 50 characters on the custom fields in the notification subscription criteria. This update increases the size of the limitation to 255 characters.
  • You cannot add Windows Client computers for Operational Insights (OMS) monitoring. This update fixes the OMS Managed Computers wizard in the System Center Operations Manager Administration pane to let you search or add Windows Client computers.
  • When you use the Unix Process Monitoring Template wizard to add a new template to the monitor processes on UNIX servers, the monitored data is not inserted into the database. This issue occurs until the Monitoring Host is restarted. Additionally, the following is logged in the Operations Manager log file:

Log entry:
Log Name:      Operations Manager
Source:        Health Service Modules
Date:
Event ID:      10801
Task Category: None
Level:         Error
Keywords:      Classic
User:          N/A
Computer:
Description:    Discovery data couldn’t be inserted to the database. This could have happened because of one of the following reasons:
– Discovery data is stale. The discovery data is generated by an MP recently deleted.
– Database connectivity problems or database running out of space.
– Discovery data received is not valid.

Additionally, you may receive the following exception, which causes this issue to occur:

Exception:
Exception type:   Microsoft.EnterpriseManagement.Common.DataItemDoesNotExistException
Message:          ManagedTypeId = ccf81b2f-4b92-bbaf-f53e-d42cd9591c1c
InnerException:   <none>
StackTrace (generated):            SP               IP               Function            000000000EE4EF10 00007FF8789773D5 Microsoft_EnterpriseManagement_DataAccessLayer!Microsoft.EnterpriseManagement.DataAccessLayer.TypeSpaceData.IsDerivedFrom(System.Guid, System.Guid)+0x385

Read the full article here.

 

Relevant Articles:

Watch: Best Practices for Designing your SCOM 2016 Environment with MVP Maarten Goet

Watch what’s new in SCOM 2016 & Microsoft OMS with Maarten Goet

https://www.opslogix.com/12895-2/
Watch: Microsoft Azure Dev Test Labs, What’s New?

Watch: Microsoft Azure Dev Test Labs, What’s New?

Azure DevTest Labs is a new service for Microsoft Azure

Azure DevTest Labs is a new service for Microsoft Azure allowing you to quickly create environments in Azure, while minimizing waste and controlling costs.

Recently, Claude Remillard (Group Program Manager for Microsoft) showcased the new features of Microsoft Azure DevTest Labs. In short Azure DevTest Labs is a Microsoft Azure service that lets you quickly create environments in Azure while minimizing waste and controlling costs.

The video hosted by Claude Remillard teaches you what problems DevTest Labs can solve, what scenarios and values it targets, and how its exciting new features can help make your life better, as well as your work in your Dev/Test environments.

Watch: What’s new in Microsoft Azure Dev Test Labs

 

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Microsoft Azure Marketplace Update: OpsLogix Oracle OMS Solution

New release: VMware Management Pack V1.3.5.28

New release: VMware Management Pack V1.3.5.28

We have released a new update of the VMware Management Pack for System Center Operations Manager.

If you’ve already purchased the VMware Management Pack and have a valid support contract, you can log in to the customer download area and download this version.

VMware version 1.3.5.28 updates

For the new VMware management pack update, we’ve adjusted the snapshot age monitor, so that it can’t fail due to a possible region comma separator conversion. Furthermore, a few display descriptions of some monitor modules have been adjusted for this update.

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

Team OpsLogix

 

Relevant article(s)

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Is your company scared of Artificial Intelligence?

Is your company scared of Artificial Intelligence?

In the last year Machine Learning, AI (Artificial Intelligence), Deep Learning and Data Science have become the new buzz words in information technology. Tech companies are now investing billions in the development of these new technologies and are racing to be able to boast the latest advance in image recognition or machines that can operate autonomously.

More and more consumer products are making use of Artificial Intelligence.

These products range from for your latest smartphone to your home thermostat or electric toothbrush. Consumers adopt these new technologies and products quickly, but companies seem far more hesitant and slower adopting products which contain AI.

Why are some companies hesitant adopting products containing Artificial Intelligence?

One of the reasons why companies are slower adopting these products is a gap in knowledge. Typically staff of an IT department have heard of Artificial Intelligence, and are more knowledgeable about it than people who are not working in IT, but they don’t know it as well as the tried and tested technologies they are currently working with. There is also a fear that products, which use AI, are going to make unexpected decisions, and that there is no way of finding out why a particular decision was made.

Making sense of AI, Machine Learning and other buzz words

Artificial Intelligence is an umbrella term for the field of Computer Science which deals with providing machines the ability to perform rational tasks such as Language Recognition, Automation, Image Recognition, and many others. The field of Artificial Intelligence includes:

  • Machine Learning is a subset of AI which is data oriented and deals with prediction. If you hear about techniques like SVM, Bayes and Decision Trees you can be confident that you are talking about machine learning.
  • Neural Networks are also a subset of AI which has been extremely popular over the last years. Neural Network algorithms mimic the Neurons in a human brain and are most commonly used in voice and image processing. The technique of applying Neural Networks is sometimes referred to as Deep Learning.
  • Data Science is often used in conjunction with Artificial Intelligence, however, it is not an algorithm, but a scientific field for extracting knowledge or gaining insights in data in various forms, either structured or unstructured.

What type of AI do we use for what application?

In general most Machine Learning techniques are used in a predictive capacity. Will the stock market rise or fall? Who will develop diabetes? What will the outcome be for a football match? Machine Learning is largely based on well-known statistic techniques and therefore the the results can be validated.

Neural Networks are typically used for non-predictive tasks such a face and voice recognition or autonomous driving cars. Large Neural Networks can be extremely complex, and unlike Machine Learning, it can be very challenging or nearly impossible to find out why a Neural Network made a particular decision. Currently, a lot of research is done in an effort to making the decision-making process of neural networks more transparent so that improvements can be made on current techniques.

Mind shift for adopting AI

Is it a bad thing that we don’t always know why a decision was made by a type of AI algorithm? The knee jerk reflex is the need to have control over a system and that we know exactly why a system did what it did. For the past decade, this way of thinking has been dominant in IT but it’s slowly changing to an approach where we don’t control the output of a system directly, but rather “train” the system like we do with a pet or apprentice. In the consumer market we can already see that we train our home thermostat to choose the best time to start heating your house, so for example, would it be so strange to let AI decide when it would be an optimal time to do maintenance on your IT environment?

If we are confident in the AI’s ability to be trained to a point where we trust its decision making, then it doesn’t really matter if we are not exactly sure why the AI made a choice, just that we know it made the right one.