부산대학교 도서관

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers are containerized to facilitate this growing demand. Most of the existing resource allocation methods for data centers used efficient scheduling algorithms to place the containers using static computing resources. These static resource allocation techniques are not energy efficient and do not help maximize data center utilization. Dynamic resource allocation and a migration-enabled placement method can reduce energy utilization while improving the utilization of the available computing infrastructure. This article presents and evaluates a novel dynamic resource management system that uses active migrations to minimize energy utilization to serve containerized workloads and improve data center utilization. Our approach uses a deep learning method to estimate the job execution time and then employs an unsupervised learning method to identify similar jobs. Similar jobs are placed and migrated to achieve energy efficiency and better utilization of the available data center infrastructure. Our proposed system is evaluated and compared with the existing state-of-the-art baseline methods. The proposed solution reduces the energy consumption from $\times 1.18$ to $\times 2.35$ compared to the baseline methods while maintaining similar performance.