Name: Emrah

Email:

Bio: Dr. Emrah Zarifoglu works as the Operations Research Scientist of Advanced Analytics at Gravitant. He has contributed Gravitant with developing algorithms and models in IT Supply Chain Optimization, IT Capacity Planning, Business Forecasting and Analytics. He has been a frontier in Cloud Transformation efforts and builds Optimization and Analytics frameworks for Cloud Analytics. Dr. Zarifoglu has worked with many IT professionals closely helping in the analysis and solution of their IT supply chain problems. The customers he has involved with range from Texas state agencies and federal agencies to cloud providers such as Terremark, Savvis, GoGrid, Rackspace. He has helped more than 15 customers in their efforts of transition and transformation to cloud space. He holds a patent in Cloud Analytics. His work is being published in IEEE and has been presented in IERC and INFORMS. Dr. Zarifoglu holds B.S. and M.S. degrees in Industrial Engineering from Bilkent University, Turkey. He earned his Ph.D. in Operations Research and Industrial Engineering from University of Texas at Austin. He has taken part in developing Advanced Analytics department at Gravitant since 2010. Prior to Gravitant, he worked for University of Texas at Austin/ISMI, and AMD.

Posts by ezarifoglu:

What Do We Mean by Cloud?

December 19th, 2011

“In all the ambiguity of what adds value to the Cloud or what facilitates the Cloud, Gravitant sits at the intersection of both, which makes it a pure Cloud company with all the experience, expertise, and solutions built around the Cloud.”

Recently, I’ve been writing mostly about what we’ve been developing for and around the Cloud at Gravitant. Now I’d like to elaborate a little bit about what’s being said and done about the Cloud outside of Gravitant. Rather than analyzing specific articles, I want to present my overall impression of what is out there and where Gravitant stands in this picture.

Due to the increasing hype surrounding the Cloud, its effects of determining the next generation of IT and what the Internet constitutes of, Cloud is getting a whole lot of attention from the actors of the sector and beyond. Initially, Cloud was defined with a bottom-to-top approach. Now, however, the new actors of the Cloud are redefining the Cloud with a top-to-bottom view.

The concept of IT resource sharing can be dated back as far as the use of mainframes, the Internet, VMware, or EC2 – depending on your perception. However, the name “Cloud” -which is cleverly set by the way- comes definitely after commoditization of IT resources, which is very recent. Before Cloud became “the Cloud”, standards of traditional IT had given direction to all innovative efforts towards Cloud. These efforts have been very technical and mostly motivated by infrastructure oriented improvements. Later on, the commoditization of IT resources has required the business model to be well defined. Although there are a lot of technical and infrastructural advancements noted, most of the focus is probably in defining the business of the Cloud.

After reading many blog articles, white papers, research papers and web content produced by a plethora of cloud companies, one thing common amongst all these articles is the lack of clarity as to what exactly can be labeled as Cloud. I meet the same kind of confusion among my colleagues as an Analytics professional as well. In general, boundaries in the field of Analytics are not very clear. It makes sense in both cases because business definitions are still in progress. However, certain examples could draw a more indicative line of what could be called as a pure Cloud effort.

Most of the work branded as Cloud efforts are actually the conversion of existing desktop software to SaaS. If you search keywords such as “Cloud” and “Analytics”, the results will show you many analytics tools as SaaS. Although I believe every type of Cloud effort is a brick in the wall while constructing a whole Cloud environment, Cloud efforts should be distinguished by what is made “for” cloud and what is made by “facilitating” Cloud. For example, if you convert management software to a SaaS application, then you are “facilitating” Cloud. If this management software is used to manage your Cloud resources, then this is an effort made “for” Cloud. Although there is a considerable gray area in the intersection of the both, I hope this example highlights a clear but subtle distinction.



So where does Gravitant stand? First of all, Gravitant is an established Cloud brokerage company listed on Gartner’s recent report on Cloud brokerage companies. According to NIST a cloud broker is “…an entity that manages the use, performance and delivery of cloud services and negotiates relationships between cloud providers and cloud consumers.” Gravitant’s cloudMatrix and cloudWiz tools manage all traditional IT resources and Cloud resources end-to-end from sourcing to provisioning and even monitoring. These tools include powerful and intelligent capacity planning, advanced monitoring and analytics tools which enable enterprises to strategically and tactically plan the capacity of their IT resources on the Cloud and in-house. In addition, these tools help enterprises efficiently analyze large amounts of data to propose the most effective Cloud Analytics solution. All these efforts make Cloud a more manageable and less costly environment to meet the IT needs of enterprises.

Furthermore, Gravitant’s major Cloud brokerage and management tools cloudMatrix and cloudWiz are user friendly, fast and smart SaaS applications. They naturally run on the Cloud efficiently, reliably and securely. In fact, Gravitant runs all of its other applications and internal IT resources on the Cloud. As such, Gravitant not only facilitates the Cloud but also has first-hand experience as a Cloud user.

All these Cloud centric activities make Gravitant a true Cloud company. Gravitant’s Cloud network grows very fast day by day and we’re proud of our growing partnerships with companies including Amazon, Terremark, Savvis, Rackspace, GoGrid and IBM. There really is a lot to learn about Gravitant’s cloud experience. If you have any ideas, thoughts or questions to add to this discussion of what is “for” cloud and what is “facilitating” cloud, please respond to this post or contact us so that we can share the intellectual part of the Cloud experience together.

Tweet This Post

No Comments "

Cloud Capacity Allocation: Reserved vs. On-Demand Capacity or How I Managed to Get over with Black Friday Rush

November 17th, 2011

The shopping season just arrived and who knows how much pressure is on the shoulders of IT administrators of e-commerce companies. Competition is tough so if one has to wait more than a couple of seconds to view a deal, he or she can easily move on to some other website to get them all. So the clock is ticking and all the e-commerce websites are supposed to have the resources to fulfill the oncoming demand. Thanks to the cloud, these problems are behind. And thanks to Advanced Analytics team of Gravitant, the related cost-cutting solutions are provided to enterprises as a part in our cloud domain.

Commoditization of computing via cloud allows IT demand to be fulfilled in time. Ideally, it is possible to acquire the required resources whenever the demand occurs. Obviously, this would be the perfect policy to replenish IT resources regardless of budget constraints. However, putting technical difficulties and lead times aside, supplying demand on time is not very practical and smart when cost and alternative pricing models of the suppliers are considered. Most cloud providers offer lower rates for bulk cloud procurements.

Practical concerns and budget considerations force enterprises to make a three dimensional IT capacity procurement decision in the cloud. Following are the right questions to ask while making these decisions:

1. How much capacity to reserve at the beginning?
2. When to order additional capacity?
3. How much additional capacity should be ordered each time?

Among these three questions, the last two are the easiest to answer as long as we know the answer to the first question. The combined answer to the last two questions is to order the excess demand whenever it occurs. So the first question remains, “what should the reserved capacity be?”

If we assume the preferred cloud provider prices its cloud uniformly, which means it does not implement any bulk pricing and there are no fixed costs per order and no lead times, then it only makes sense to order equivalent to demand quantity whenever there is a demand realization, hence zero reserved capacity. However, the real world does not work exactly this way so we have to keep some reserved capacity to minimize cost and deal with uncertain technical and business problems.

There are a couple of alternative approaches to solving this problem with operations research and advanced analytics techniques. We can either solve the problem with a deterministic optimization approach or implement Markov Decision Process regarding stochasticity. In the next blog article on this topic, I will discuss these alternative approaches in detail and give an idea of what solutions Gravitant offers to enterprises on the issue of reserved vs. on-demand capacity in the cloud.

Tweet This Post

No Comments "

An Analytic Approach to Solving Load Balancing Problem in the Cloud

September 26th, 2011

IT management moves into a new dimension by the Cloud. In traditional IT, most of the cost generation occurs in procurement of resources, provisioning and maintenance. By nature, the cost generation is fairly static in traditional IT. Fixed cost of hardware and data centers and stable variable maintenance and provisioning costs contribute to this static cost structure. Cloud’s dynamic nature affects cost management of enterprises in the Cloud, too. Pricing strategies of cloud providers go along with principle of cloud as a utility. Although many pricing options have a fixed portion for a reserved capacity, the usage based cost is always a significant and varying part of enterprise cloud costs. This dynamic cost structure increases the importance of intelligent provisioning and management.

My previous article, “Cloud Sourcing Optimization: A Conceptual Model Discussion”, in Gravitant’s blog, introduces Gravitant’s efforts in optimization in Cloud analytics. The next of the series is investigating analytic solution approaches to solving load balancing problems.

The underlying problem is simply to determine when to turn off a virtual machine (VM) due to low utilization without allowing utilization of any VM to exceed a certain threshold level by turning on a new VM. The aim is to keep VM utilization within a reasonable band to minimize provisioning cost while satisfying workload demand. The question is what the “optimal” high-mark and low-mark utilization values to turn on and off VMs are.

The obvious decision variables in a corresponding optimization problem are high-mark utilization value, low-mark utilization value, whether an existing VM is turned off due to low utilization, and whether a new VM is created due to high utilization of any VM. Each turned of VM creates an extra load of work on the rest of the VMs. Each new VM shares the load of a high-utilized VM. Objective is to minimize total cost of provisioning. Set of constraints can be summarized in three groups.

1- High-mark utilization: New utilization of the remaining VMs after adding the used capacity of low-utilization VMs should be lower than high-mark utilization value.

2- Low-mark utilization: Any VM should have a utilization more than low-mark utilization value.

3- New VM creation: If a VM has a higher-than-high-mark utilization, then a new VM is created.

Because there are both binary and continuous variables, the optimization model tends to be a mixed integer programming model. However, since the first set of constrains is quadratic, the exact definition of the model is quadratically constrained mixed integer programming model. Some straightforward enumeration over the set of VMs will help linearize the constraint. Therefore, we will have a mixed integer linear programming model.

Although this static model may seem restrictive in a setting with a varying amount of demand for virtual machines to meet under budget limitations, it has ability to roll over time and transform into a dynamic model which would fit very well to the span of provisioning and the nature of the Cloud. The utilization band in which VMs are allowed to operate changes dynamically and provides a flexible space for decision makers.

This article reveals the tip of the iceberg of the analytic solutions which Gravitant offers as a cloud brokerage and management company for the enterprises. Our set of analytic solutions that help enterprises move into and operate in the Cloud will continue to grow and evolve.

Tweet This Post

1 Comment "

Cloud Sourcing Optimization: A Conceptual Model Discussion

July 28th, 2011

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.

Tweet This Post

2 Comments "

IT Capacity Planning in the Cloud and on the Ground

November 4th, 2010

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. Cloudharmony.com 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.




Tweet This Post

No Comments "