Knowledge based data center capacity reduction using sensitivity analysis on causal Bayesian belief network

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.

Capelin: Data-Driven Compute Capacity Procurement for Cloud Datacenters Using Portfolios of Scenarios

“…We find that the majority of studies only consider one resource dimension, and four inputs or less for their capacity planning model. Few are simulationbased [1,14,49,52,53,54], with the rest using primarily analytical models. The notable Janus [1] focuses only on datacenter networks.…”

Capelin: Data-Driven Capacity Procurement for Cloud Datacenters using Portfolios of Scenarios -- Extended Technical Report

“…We find that the majority of studies only consider one resource dimension, and four inputs or less for their capacity planning model. Few are simulation-based [1,13,48,51,52,53], with the rest using primarily analytical models. We highlight three of these works below and position them in relation to this work.…”