An optimization approach to capacity evaluation and investment decision of hybrid cloud: a corporate customer’s perspective

Abstract

While the rapid growth of cloud computing is driven by the surge of big data, the Internet of Things, and social media applications, an evaluation and investment decision for cloud computing has been challenging for corporate managers due to a lack of proper decision models. This paper attempts to identify critical variables for making a cloud capacity decision from a corporate customer's perspective and develops a base mathematical model to aid in a hybrid cloud investment decision under probabilistic computing demands. The identification of the critical variables provides a means by which a corporate customer can effectively evaluate various cloud capacity investment opportunities. Critical variables included in this model are an actual computing demand, the amount of private cloud capacity purchased, the purchase cost of the private cloud capacity, the price of the public cloud, and the default downtime loss/penalty cost. Extending the base model developed, this paper also takes into consideration the interoperability cost incurred in cloud bursting to the public cloud and derives the optimal investment. The interoperable cloud systems require time and investment by the users and/or cloud providers and there exists a diminishing return on the investment. Hence, the relationship between the interoperable cloud investment and return on investment is also investigated.

Strategic Joining and Optimal Pricing in the Hybrid Cloud System with Risk-sensitive Service Requests

“…Taking into account customers' strategic behavior, Zhu et al [19] derived the conditional equilibrium joining probability of entering the private cloud and determined the cooperatively optimal retrial rate and the non-cooperatively optimal retrial rate in a hybrid cloud system. From the perspective of corporate customers, Lee et al [20] obtained customers' behavior strategies for maximizing their benefits in hybrid cloud. Liu et al [21] considered the relationship among multiple cloud customers as an evolutionary game from the perspective of game theory, and obtained demand strategies for customers to maximize their own profits.…”