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Power Management Optimization for Data Centers: A Power Supply Perspective
IEEE Transactions on Sustainable Computing
Year: 2025 | Early Access Article |
With the escalating demand for cloud computing services, data centers (DCs) encounter formidable challenges extending beyond capital investment needs to accommodate increasing computational demands and routine infrastructure maintenance. These challenges include substantial electricity costs due to high energy consumption and the environmental issues caused by high carbon emissions. To reduce cost...Show More
Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A Review
Constructing energy-efficient cloud data centers (CDCs) is an essential path for the further expansion of cloud computing. As one of the core subsystems of a data center, the cooling system provides a reliable thermal environment for the safe operation of IT equipment while posing a huge energy consumption and carbon emission problem. Thus, it is evident that optimizing energy management of coolin...Show More
Virtualized Resource Scheduling in Cloud Computing Environments: An Review
Jianpeng Lin;Delong Cui;Zhiping Peng;Qirui Li;Jieguang He;Mian Guo
2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
This paper summarizes the status and results of current research on cloud computing resource scheduling optimization problems. At the virtual resource layer, it focuses on analyzes and discussion pertaining to quality of service and efficiency-aware resource scheduling strategies, node task load balance and resource utilization-aware resource scheduling strategies, and multi-objective optimization...Show More
With the development of artificial intelligence and the Internet of things, the prospects of cloud computing applications have become broader, and the number of users and cloud data centers (CDCs) has exploded. It is a challenge to realize efficient job scheduling and resource allocation of multiple users and data centers. However, the traditional scheduling model based on heuristic algorithm has ...Show More
Cloud Workflow Task and Virtualized Resource Collaborative Adaptive Scheduling Algorithm Based on Distributed Deep Learning
Delong Cui;Jianpeng Lin;Zhiping Peng;Qirui Li;Jieguang He;Yiheng Yuan;Mian Guo
2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA)
Year: 2020 | Conference Paper |
Cited by: Papers (6)
Cloud workflow task scheduling and resource allocation are core issues in cloud computing environment. Balancing the quality of user service and the revenue of service providers is a challenge in cloud workflow task scheduling and resource allocation. Therefore, we propose a cloud workflow task and virtualized resource collaborative adaptive scheduling algorithm based on heterogeneous distributed ...Show More
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