Self service IT Requirements: Scalability and elasticity


For Cloud Solutions, Scalability and Elasticity are key requirements. In private Cloud Solutions, this should also be supported, even if scalability and elasticity might has a lower border as we see in the public Cloud. Private Cloud Computing environments basically use dedicated Hardware and resources are not shared. Public Cloud Solutions use resource sharing, which is basically not available in private Cloud Solutions. Scaling applications means that we can add a new instance of Linux or a Windows Server. Elasticity is something “more advanced” to that as described by Reuven Cohen, an opinion leader in Cloud Computing (Cohen, 2010). Reuven describes scalability as the possibility to “grow to the demands of the users on a platform” whereas he states that elasticity is something that reflects real-time conditions. A platform might have millions of users, but if this platform is only available in the United States, there might be significant fewer load on the servers during night. The load will be much higher at peak times and elasticity means that unnecessary instances are shut down if the load is lower or that new instances are started if the load is higher. Elasticity is something very important to self Service Platforms, since they basically exist within an enterprise environment. Demand might change over time as not all enterprises act globally and even if they do, different platforms are used in a different manner. Let’s look back to the marketing department introduced earlier; especially in marketing, different groups of users are targeted and it is about targeting regions individually. What works well for customers in the United Kingdom might not work for customers in France and therefore, the website(s) might be different. That makes elasticity important since unused instances can be shut down to use the now free instances for other applications and services.  (Owens, 2010) defines Elasticity as „the golden nugget of Cloud Computing“ and a key inhibitor to move to Cloud Environments. A very similar definition on what Cohen defined as elasticity is also provided by the National Institute of Standards and Technology  (Mell & Grance , 2011):

 

elastictiy and scalability for self service platforms
elastictiy and scalability for self service platforms (C) Polycart

 

“Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time.” –  (Mell & Grance , 2011)

 

As stated in the beginning of this section, the hardware available to run these services is not infinite and it is much lower as we in public Cloud Solutions. Better utilization of the Datacenter can now enable less critical tasks to be run overnight and use free instances. The IT department could even offer them at lower prices, as we can already see them with spot instances on Amazon Web Services, a popular public Cloud provider  (Amazon Web Services, Inc, 2012). A possible use-case could be the research department, which calculates non time-critical tasks during night and saves money by doing so. Since self-Service platforms should be easy to use, most of the elasticity has to be handled by the platform. The IT-department would only set a quota of how many instances are available for a specific service or application and set the rules and boundaries for elasticity. The consumer (or customer) only starts the new service or application and don’t have to care about scaling and elasticity issues, since it should be easy to use.

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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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