Thursday, July 28, 2011

Cloud Sourcing Optimization: A Conceptual Model Discussion

From Gravitant's blog.

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.

IT Capacity Planning in the Cloud and on the Ground

From Gravitant's blog.

Capacity planning is a hype topic in IT supply chain. It is a key requirement for companies making strategic IT decisions. The main challenge appears to be the lack of a uniform, homogeneous measure of comparison between IT resources. If you take the example of server capacity planning, what makes one server better than the other? CPU power, number of processors and cores are definitely key elements for a comparison. However, benchmarking results does not suggest a straight forward comparison between these elements. SUN has been using a benchmarking approach – what they call as “m-values”- for their servers. SPEC values are the most comprehensive references for benchmarking against competition. However, at the end of the day, all these values are company declared and endorsed values for their own servers. Also, experimental conditions and minor configuration changes may cause significant performance changes as can be seen in the SPECs published.
Recently, the capacity planning problem has another dimension for the companies planning to move to cloud. Either public or private, cloud computing provides a large degree of flexibility for IT operations of companies. However, it is not as easy for the companies who are used to keeping IT resources “in-house” to make a decision to move to the cloud.  Ignoring all the overhead, accessibility, privacy, security and legal issues that come with the cloud, capacity planning becomes a multi-fold complicated problem by itself. While it was not already straight forward to compare performances of existing hardware, capacity planning brings a much bigger challenge due to the nature of the cloud where black boxes of resources somewhere around the world out of control of the company await to be evaluated and configured by a company who is new to this space. 
In reality, the best way to compare performances of the cloud and the in-house hardware would be after the fact. However, almost no company has the luxury and resources to reserve and make such a move to the cloud just to see how it would perform. Therefore, strategic IT capacity planning comes into the picture as the savior of budget, time, and energy. But our prior question still remains unanswered even in a larger scale: “What should be the measure of performance for comparison between the cloud and the hardware?” There are some attempts going on for comparison of cloud providers. provides some good performance indicators for alternative cloud providers. Their performance unit “CCU” has a good perception in the business if you read the reviews. So one link of the chain is missing to have a good starting base for comparison between the hardware and the cloud, which is a relation between SPEC and CCU. I am expecting that it won’t be long before we see some attempt through defining and measuring this relation.
As strategic IT capacity planning is becoming a major attraction, the tools to enable it on a larger scale are also making themselves available. There is a lot to come next on this subject. Optimization and cost minimization will and should follow every capacity planning attempt to make the most benefit out of it. Either on the cloud or on the ground, the key to all these strategic efforts is to have a uniform and homogeneous measure of performance. Gravitant has developed a unique bottom to top approach in which the performance is proportional to expected computational power of the hardware or cloud configuration to resolve this issue. We will talk about this approach and its outcomes in more detail in our coming blogs.

Wednesday, July 27, 2011

Cloud Computing - 58% Average Savings Per Month

Application: CRM
Environment: Production
Capacity: 40 Web/App Servers, 12 DB Servers, 8 VPN Servers, 5TB Storage, 10 Mbps Bandwidth
Demand: 1000 concurrent users, 3.0% growth per year

Scenario results from CloudWiz:

To run additional scenarios (for free), please go to

*Note that these results are simply for comparison and decision support.  All cost and savings results are based on publicly available data, and Gravitant is not responsible for any discrepancies in the numbers shown above.  To increase the accuracy of the results from CloudWiz, please contact us to schedule a calibration meeting with our Professional Services group.