Resource Automation in the Cloud: Problems


The next few posts will deal with resource automation in the Cloud. As with other tasks, this is not a thing you solve in one day nor will it be easy. In the first post on resource automation, we will look at the main issues related to resource automation before we dig deeper into resource automation itself.

The major areas of resource automation problems are:

  • Service Level Monitoring
  • Event Lifecycle Management
  • IT Self Service

Service Level Monitoring

SLAs are key metrics in Cloud-based environments (but not only!). A key issue is that Response Times are seldom in SLAs. Metrics that should be contained in SLAs are:  Application availability, Storage availability, CPU use, Network I/O, Events and many more. A key issue is that there are several sources that SLAs have to be built from:
  • Management Tools
  • Monitors
  • Logs
  • Services

There are several reasons why building SLAs manually is not that effective:

  • Higher staff levels and traing costs
  • Higher service desk costs
  • Increased risk of errors and downtime
  • Slower remediation and downtime
  • Slower remediation and missed service levels
  • Increased business pressure on IT service
We will talk about the other two issues – Event Lifecycle Management and IT Self Service – in the next posts.

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Published by

Mario Meir-Huber

I work as Big Data Architect for Microsoft. With this role, I support my customers in applying Big Data technologies - mainly Hadoop/Spark - for their use-cases. I also teach this topic at various universities and frequently speak at various Conferences. In 2010 I wrote a book about Cloud Computing, which is often used at German & Austrian Universities. In my home country (Austria) I am part of several organisations on Big Data.

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