A Decision Support System for Moving Workloads to Public Clouds
The current economic environment is compelling
CxOs to look for better IT resource utilization in order to get more
value from their IT investments and reuse existing infrastructure
to support growing business demands. How to get more from less?
How to reuse the resources? How to minimize the Total Cost of
Ownership (TCO) of underlying IT infrastructure and data center
operation cost? How to improve Return On Investment (ROI) to
remain profitable and transform the IT cost center into a profit
center? All of these questions are now being considered in light of
emerging ‘Public Cloud Computing’ services. Cloud Computing
is a model for enabling resource allocation to dynamic business
workloads in a real time manner from a pool of free resources
in a cost effective manner. Providing resource on demand at
cost effective pricing is not the only criteria when determining
if a business service workload can be moved to a public cloud.
So what else must CxOs consider before they migrate to public
cloud environments? There is a need to validate the business
applications and workloads in terms of technical portability and
business requirements/compliance so that they can be deployed
into a public cloud without considerable customization. This
validation is not a simple task.
In this paper, we will discuss an approach and the analytic
tooling which will help CxOs and their teams to automate the
process of identifying business workloads that should move to
a public cloud environment, as well as understanding its cost
benefits. Using this approach, an organization can identify the
most suitable business service workloads which could be moved
to a public cloud environment from a private data center without
re-architecting the applications or changing their business logic.
This approach helps automate the classification and categorization
of workloads into various categories. For example, Business
Critical (BC) and Non-business Critical (NBC) workloads can
be identified based on the role of business services within the
overall business function. The approach helps in the assessment
of public cloud providers on the basis of features and constraints.
This approach provides consideration for industry compliance
and the price model for hosting workloads on a pay-per-use
basis. Finally, the inbuilt analytics in the tool find the ‘best-fit’
cloud provider for hosting the business service workload. ‘Bestfit’
is based on analysis and outcomes of the previously mentioned
Today, the industry follows a manual time consuming
process for workload identification, workload classification and
cloud provider assessment to find the best-fit for business service
workload hosting. The suggested automated approach enables an
organization to reduce cost and time when deciding to move to
a public cloud environment. The proposed automated approach
accelerates the entire process of leveraging cloud benefits,
through an effective, informed, fact-based decision process.
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