Cloud computing brings up new cost cutting, improved flexibility and increased elasticity opportunities for enterprises. While these are the main marketing features of the cloud, the evaluation and comparison of the vendors has not been straight forward so far. Thanks to CloudWiz of Gravitant, we are able to quantify the features of vendors, evaluate them and compare them in a practical, analytical and user friendly manner. As the cloud space gets larger, and decision making steps become more complicated, we will need to add more intelligence to our decision making in cloud migration.
The potential optimization problems may arise in several parts of the cloud space, such as cloud sourcing problem, enterprise capacity planning problem, vendor capacity planning and scheduling problem, vendor load balance problem, etc. In today’s blog, I will elaborate on how to view cloud sourcing problem as a conceptual optimization model.
After an enterprise intends to move to the cloud, it first needs to translate its current use and needs into cloud requirements. Some of these requirements are quantifiable while some are not. This task is followed by matching the requirements with multiple cloud vendors for evaluation and comparison.CloudWiz takes care of all these tedious steps in a fast, intelligent and user friendly manner. The optimization of cloud sourcing problem is defined on these steps.
In our problem space, there is one customer against multiple cloud vendors. The decision factor is what portion of a certain computing need to provide from a certain vendor.
What are potential constraints of cloud sourcing problem? Let’s make a list of them.
1- Supply-demand: All demand should be satisfied.
2- Hard capabilities: Selected set of vendors should carry all the unquantifiable capabilities which are core to functioning of the enterprise.
3- Soft capabilities: Selected set of vendors should carry a certain fraction of the unquantifiable capabilities which are secondary to functioning of the enterprise.
4- Quality of service: Each selected vendor should satisfy a certain level of quality of service.
First constraint makes sure there is no lack of supply. Second constraint helps eliminate all infeasible members from the decision set. Third constraint grants some flexibility to the enterprise in decision making. Fourth constraint ensures the consistency of quality of service.
What is the objective? It should definitely be measured in dollars since we kept perhaps the most important aspect, cost, out of scope so far. The proposed objective function is the minimization of total procurement cost. Cloud vendors have varying pricing schemes. Therefore, building such an objective function is a tedious task. From determining the constraints to constructing an objective, CloudWiz provides all the inputs for such an optimization model in a smart and clean way.
Let us speculate about how the optimal solution would look like. Obviously, if there is a unique vendor which serves all the hard capabilities and enough soft capabilities with the minimum cost, there is the winner. Otherwise, the customer goes through the feasible vendors and starting with the lowest priced one, picks the ones with all hard capabilities, certain number of soft capabilities and minimum satisfying quality of service, allocating based on cost. Although the model is defined as generic as possible, it can still be customized for any enterprise in any conditions.
Hang on for the future versions of the CloudWiz powered with enhanced intelligence of optimization provided by Advanced Analytics group at Gravitant. I will share potential optimization problems in our coming blogs.
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