The up.time IT Systems Management Blog

As a follow-up to my cost of cloud computing post that had a large response, I decided to do a follow-up cloud cost analysis. This is part 1 of a 3 part series that will be posted over the next few weeks.

The ultimate goal of deploying application or dynamic infrastructure to the cloud is the truly agile and cost-competitive nature of running and managing applications and infrastructure. However, cost can increase exponentially without proper cloud monitoring and cloud cost modeling. It has become crucial for IT to tie cloud success to cost analysis, in addition to overall system performance. This article will provide some common pitfalls and pains around current gaps in cloud costing and deployment, as well as a key set of questions to help IT make smart cloud decisions.

Up to now, the success of applications in cloud, virtual and physical environments have been viewed in only two dimensions – availability and performance. However, perhaps the most important dimension is cost, and it’s cost that will dramatically influence what, when and where IT organizations deploy to the cloud. Presently a major gap is in tooling, where no cloud monitoring tools can help IT and LOBs monitor their cloud costs, predict workload/application cost, notify when costs are escalating, as well as provide standard cloud performance and availability monitoring. However, we do see this tooling issue changing in the near future.

To date, companies have been oblivious to the workload cost of an application running in the cloud, apart from unclear monthly billing. We are entering a new era where performance and availability will be baseline requirements, but workload cost efficiency will be the new key to success. This will be the age of ‘economic compute’ and will be defined by how and where companies can run workloads at the best cost (assuming performance and availability remain constant). It won’t matter if it’s internally run on physical or virtual servers, or in the cloud, as the economics will drive this decision. However, the lynch pin to this costing decision model is missing…

To responsibly manage IT budgets, companies need visibility to the cost and performance data of workloads, applications and dynamic infrastructure services. However, the industry is missing a complete toolset or product suite that can help IT easily see and predict the cost of cloud deployment. Applications and services can be deployed on cloud infrastructure (assuming it returns acceptable performance and availability), but it’s essential for IT to have clear visibility to what the workloads will cost comparatively, across different cloud vendors or even the cost of an internally run workload. How can IT make a cost-conscious decision without the basic cost data of an application, workload or service? Quite simply, it can’t. This is part one of a three part series where the idea of the economic cloud comes into play:

Example #1 – Dynamic Infrastructure Services:

• Ensure IT Doesn’t Overpay: A company may have provisioned a $500 per month system, but if its CPU is only consumed 10 percent of the time, then one is largely over paying. Now scale that scenario out to a company that is running many services, applications and servers in the cloud.

• Companies with Many Separate Cloud Accounts: For IT managers trying to understand the cumulative costs of many developers or departments (LOBs) with cloud accounts, it can be almost impossible, with no clear means of reconciling usage (until it’s too late).

• Manage Cost Across Geographically Dynamic Workloads: For more advanced scenarios, there are now a number of services that allow the creation of cloud instances in specific geographic regions, which enables a new generation of smartphone or mobile applications to exist.  There are millions of smartphone users in the world in non-North American geographies, such as Latin America – imagine if you could dynamically and geographically provision cloud resources that are compute heavy, or can service the requests of these remote smartphone clients, in a cost effective manner.  This reduces bandwidth requirements, increases the response time and can be done on cheaper, temporarily available compute resources. This kind of dynamism is incredibly powerful, yet monitoring the changing costs and performance of these cloud resources is going to be a difficult problem to solve.

Stay tuned over the next few weeks for more examples of where the economic cloud comes into play and please, let me know your feedback/questions by posting a comment.

Until next week…

Alex