A Study of Revenue Cost Dynamics in Large Data Centers: A Factorial Design Approach

TL;DR

This paper develops a model using factorial design and Cobb-Douglas functions to analyze revenue and cost dynamics in large data centers, aiming to optimize revenue by identifying key investment factors.

Contribution

It introduces a novel factorial design approach combined with Cobb-Douglas modeling to optimize revenue in large data centers, considering multiple investment parameters.

Findings

Identified key factors influencing data center revenue.

Computed optimal elasticities for maximum revenue.

Validated the model with business scenario interpretations.

Abstract

Revenue optimization of large data centers is an open and challenging problem. The intricacy of the problem is due to the presence of too many parameters posing as costs or investment. This paper proposes a model to optimize the revenue in cloud data center and analyzes the model, revenue and different investment or cost commitments of organizations investing in data centers. The model uses the Cobb-Douglas production function to quantify the boundaries and the most significant factors to generate the revenue. The dynamics between revenue and cost is explored by designing an experiment (DoE) which is an interpretation of revenue as function of cost/investment as factors with different levels/fluctuations. Optimal elasticities associated with these factors of the model for maximum revenue are computed and verified . The model response is interpreted in light of the business scenario of data centers.