CDSFA Stochastic Frontier Analysis Approach to Revenue Modeling in Large Cloud Data Centers

TL;DR

This paper introduces a stochastic frontier analysis approach using a quasi Cobb Douglas model to optimize revenue and costs in large cloud data centers, incorporating technological progress and uncertainty.

Contribution

It presents a novel CDSFA method for revenue modeling and cost optimization specifically tailored for large cloud data centers, integrating stochastic and technological factors.

Findings

Effective revenue and cost optimization model developed

Incorporates technological progress via Harrod and Solow neutrality

Provides insights into stochastic uncertainty in data center operations

Abstract

Enterprises are investing heavily in cloud data centers to meet the ever surging business demand. Data Center is a facility, which houses computer systems and associated components, such as telecommunications and storage systems. It generally includes power supply equipment, communication connections and cooling equipment. A large data center can use as much electricity as a small town. Due to the emergence of data center based computing services, it has become necessary to examine how the costs associated with data centers evolve over time, mainly in view of efficiency issues. We have presented a quasi form of Cobb Douglas model, which addresses revenue and profit issues in running large data centers. The stochastic form has been introduced and explored along with the quasi Cobb Douglas model to understand the behavior of the model in depth. Harrod neutrality and Solow neutrality are incorporated in the model to identify the technological progress in cloud data centers. This allows us to shed light on the stochastic uncertainty of cloud data center operations. A general approach to optimizing the revenue cost of data centers using Cobb Douglas Stochastic Frontier Analysis,CDSFA is presented. Next, we develop the optimization model for large data centers. The mathematical basis of CDSFA has been utilized for cost optimization and profit maximization in data centers. The results are found to be quite useful in view of production reorganization in large data centers around the world.