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CLUM: A CLUSTERING-CUM-MARKOV MODEL FOR RESOURCE PREDICTION IN A DATA CENTER.

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Názov: CLUM: A CLUSTERING-CUM-MARKOV MODEL FOR RESOURCE PREDICTION IN A DATA CENTER.
Autori: GOVINDARAJAN, MADHUPRIYA, SELVARAJ, MERCY SHALINIE, RAVI, NAGARATHNA
Zdroj: International Journal of Applied Mathematics & Computer Science; 2025, Vol. 35 Issue 3, p535-545, 11p
Predmety: CAPACITY management (Computers), DEMAND forecasting, SCALABILITY, SERVER farms (Computer network management), ECOLOGICAL impact, PREDICTION models
Abstrakt: 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 clustering-cum-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. [ABSTRACT FROM AUTHOR]
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Databáza: Complementary Index
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Abstrakt: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 clustering-cum-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. [ABSTRACT FROM AUTHOR]
ISSN:1641876X
DOI:10.61822/amcs-2025-0038