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.
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