For start-ups and greenfi eld applications, it is common that entire applica-tions are built in the cloud. For established enterprises, it is more realistic that only certain components within an architecture are deployed in the cloud.
Here are some common use cases where today ’s enterprises are leveraging the cloud to supplement their existing architectures.
Cloud Bursting
Many organizations choose to leverage the cloud to handle peaks in traffi c.
They may have applications running in their data centers and choose to send excess capacity out to a cloud service provider instead of investing in physical infrastructure to accommodate peaks. Retailers that deal with seasonal spikes around the holidays or companies that process tax returns that have low traf-fi c for most of the year but experience huge peaks during the tax season are examples of companies that might take advantage of cloud bursting.
Archiving/Storage
Some organizations are fi nding innovative ways to reduce archiving and stor-age costs by leveraging storstor-age in the cloud. Traditional archiving strategies involve several pieces of infrastructure and software such as backup tape and disk devices, various types of storage media, transportation services, and much more. Now companies can eliminate all of those physical components and leverage cloud storage services that can be totally automated through scripts.
The cost of storage in the cloud is drastically cheaper than storage on physical storage media and the processes for data retrieval can be much less complex.
Data Mining and Analytics
The cloud is a great place for processing large amounts of data on-demand.
As disks gets cheaper, organizations are storing more data now than ever before. It is not uncommon for companies to be storing many terabytes or even petabytes of information. Analyzing large amounts of data like this can become very challenging on-premises because an extraordinary amount of infrastructure is required to process all of that data. To make matters worse, the analytics of these large data sets are usually ad hoc in nature, which means often the infrastructure is sitting idle until someone initiates a request.
Moving these types of big data workloads to a public cloud is much more economical. In the public cloud, resources can be provisioned only when a request is initiated. There is a huge cost savings both in physical infrastruc-ture and in the management of the systems by deploying an on-demand cloud model.
Test Environments
Many companies are looking to the cloud for provisioning test and develop-ment environdevelop-ments and other nonproduction environdevelop-ments. In the past, IT has had to maintain numerous test and development environments on-premises, which required constant patching and maintenance. In many cases, those environments sit idle outside of normal working hours when workers are not working. Another issue is that a limited number of environments are usually available to testers and developers, and they often have to share environments with other teams and environments, which can make testing and development a challenge.
To solve that problem, many companies are creating processes for testers and developers to self-provision testing and development environments on-demand in the cloud. This method requires less work for the administrators, provides speed to market for the testers and developers, and can reduce costs if the environments are shut down when not in use. Better performance testing can be accomplished in the cloud because testers can provision a large amount of resources to simulate large peaks in traffi c, where in the on-premises model they were restricted to the amount of physical hardware that was in the data center.
There are many more use cases for cloud computing. The point here is that building services in the cloud is not an all-or-nothing proposition. It is perfectly acceptable and very common for enterprises to have a mixture of solutions within their architectures deployed within their data centers and in one-to-many clouds.
Summary
Choosing cloud service models and deployment models are critical tasks in any cloud computing initiative. The decisions should be based on business drivers, constraints, and customer impacts. Before making these decisions it is highly recommended that the six architecture questions discussed in Chapter 4 are answered. It is also important that all components of the business archi-tecture are considered before making these decisions. An understanding of the future state is also important. As we saw from Jamie ’s decision, he built a roadmap that arrives at a long-term future state of a hybrid cloud, which is much different from the initial deliverable, which is a public cloud option.
Since he knows that his future state is a hybrid cloud solution, he knows that a hybrid PaaS makes sense in his fi rst deliverable. If he did not look out to the future, he likely would have chosen a public PaaS. When the time came to move to a hybrid solution he would have been constrained by the public PaaS
decision. The moral of this story is to take time up front to understand the context of the entire business problem over time, not just the immediate need.
References
Kaplan, J. (2005). Strategic IT Portfolio Management: Governing Enterprise Transformation . PRTM, Inc.
Handler, R., and B. Maizlish (2005). IT Portfolio Management: Unlocking the Business Value of Technology . Hoboken, NJ: John Wiley & Sons.
Hurwitz, J., M. Kaufman, F. Halper, and D. Kirsch (2012). Hybrid Cloud for Dummies . Hobo-ken, NJ: John Wiley & Sons.
Lee, J. (2013, March 5). “Amazon Web Services Drops Prices Again to Compete with Micro-soft, Google.” Retrieved from http://www.thewhir.com/web-hosting-news/amazon-web-services-drops-price-of-ec2-again-to-compete-with-microsoft-google .
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