Affiliations: Vanguard Group Inc., Malvern, PA, USA | System Engineering, George Washington University,
Washington, DC, USA | Engineering Management and Systems Engineering of
Decision Sciences, George Washington University, Washington, DC, USA | Engineering Management and Systems Engineering, George
Washington University, Washington, DC, USA
Note: [] Corresponding author: Jayneel Patel, 46 East Bristol Road,
Washington, D.C., Warminster, PA 18974, USA. Tel.: +1 215 431 8077; E-mail:
[email protected]
Abstract: Studies on data center capacity planning, maintenance, and
reorganization have been of interest to all its stakeholders since the data
centers were first instituted. Recent study shows that data center costs
contribute to nearly 25% of all information technology budgets in a company.
Several methodologies have been adopted for strategic data center capacity
reduction such as dynamic shutdown, virtualization, and logical partitions. The
greatest challenge around data center capacity reduction is an approach that
captures all data center variables and allows for strategic reduction in
capacity while minimizing risks. This paper uses causal Bayesian Belief Network to represent data
center capacity planning decision process. It encapsulates three areas that
influence the data center demand. These areas include market conditions,
development process, and internal business decisions. The approach uses
sensitivity analysis to narrow down the factors that influence the decision
process the most while providing an opportunity, if one exists, to also reduce
unused data center capacity. An iterative approach was applied to develop a
causal Bayesian Belief Network, to carry out decisions at each stage, and to
collect sensitivity values. Training data was simulated using Geometric
Brownian motion generated through Monte-Carlo simulation. The Bayesian belief
network itself was designed using Netica.
Keywords: Data center capacity reduction, data center operations, causal Bayesian belief network, data center knowledge domain
