Low-Energy Resource Classification Algorithm for Cross-Regional Cloud Data Centers Based on K-Means Clustering Algorithm
Bin Liang, Junqing Bai · IEEE Transactions on Industrial Informatics · 2024
As the division of labor in the industry becomes more refined, an increasing number of companies are abandoning infrastructure construction and instead moving their operations to cloud data centers (CBDCs). Cloud service providers are responding to the surge in demand by deploying their own CBDCs worldwide. However, the energy consumption and operation costs of these CBDCs vary depending on the region's environment and policies. To mitigate these costs, cloud service providers often employ resource management algorithms. This article conducts a comprehensive analysis of the cross-regional CBDC model, including establishing virtual machine classification rules based on clustering results. Ultimately, this article proposes a low-energy resource classification algorithm for cross-regional CBDCs based on the K-means clustering algorithm (LCKC). The effectiveness of the LCKC algorithm is compared to that of other algorithms, and the results indicate that it reduces energy consumption in cross-regional CBDCs.