The Granularity of On-Demand Cloud - Today vs Tomorrow
Cloud is often characterised by a payment model of resource consumption, pay-as-you-go, only pay for what you use, CPU by the hour.
Over recent weeks I have been doing some thinking around cost and payment models for Cloud and two blog posts really caught my attention (happens when you are mulling over things).
Most of the applications running on virtual machines are just converted as part of physical machines to virtual machines, or installed and run in a way just as before. Essentially they are not much different from counterparts running on physical machines. We call these applications as “Application In the Cloud” (AIC).Its a great summary, Applications In the Cloud compared to Applications For the Cloud. AFC are built to really take on the dynamic characteristics that Cloud can bring such as rapid provisioning, statelessness, JeOS.
Cloud environment brings in new opportunities and challenges for application development. Modern applications can, and should, be designed or re-factored to fully leverage cloud infrastructure. When that happens, we call these applications “Applications For the Cloud” (AFCs), versus AICs as described above.
Amidst all the disconnect at CloudConnect regarding standards and where “cloud” is going was an undercurrent of adoption of what most have come to refer to as a “hybrid cloud computing” model. This model essentially “extends” the data center into “the cloud” and takes advantage of less expensive compute resources on-demand. What’s interesting is that the use of this cheaper compute is the granularity of on-demand. The time interval for which resources are utilized is measured more in project timelines than in minutes or even hours. Organizations need additional compute for lab and quality assurance efforts, for certification testing, for production applications for which budget is limited. These are not snap decisions but rather methodically planned steps along the project management lifecycle. It is on-demand in the sense that it’s “when the organization needs it”, and in the sense that it’s certainly faster than the traditional compute resource acquisition process, which can take weeks or even months.