online read us now
Paper details
Number 3 - September 2025
Volume 35 - 2025
CluM: A clustering-cum-Markov model for resource prediction in a data center
Madhupriya Govindarajan, Mercy Shalinie Selvaraj, Nagarathna Ravi
Abstract
High-end data centers are required to process the user requests and provide them with a better quality of service. The
prominent issues in building a sustainable data center are reduced carbon footprint, dynamic capacity planning to reduce
resource provisioning time and cost, minimized virtual machine migration to prevent higher downtime and enhanced return
on investment and resource utilization. Realizing true elasticity will be a solution for these issues. Better elasticity can
result if the data center is aware of the workload before its entry. Hence, the data center has to have a predictive model to
forecast the resource requirements before the arrival of the workload. We propose a novel methodology called clusteringcum-
Markov to predict the workload resource requirements proactively. It runs in the data center’s controller and collects
the statistics of the incoming workload. It characterizes the workload and predicts the necessary resources two-time slots
ahead. We evaluate the modle in our data center and also with the benchmark Google Workload dataset. The results are
compared with the state-of-the-art solutions based on various metrics, including the environment metrics. The proposed
model achieves a 99.01% precision and exhibits optimal values with respect to the environmental metrics.
Keywords
data center, Markov model, randomized algorithm, resource prediction, workload characterization