Jianpeng Lin

Publicly available datasets are vital to researchers because they permit the testing of new algorithms under a variety of conditions and ensure the verifiability and reproducibility of scientific experiments. In cloud computing research, there is a particular dependence on obtaining load traces and network traces from real cloud computing clusters, which are used for designing energy efficiency prediction, workload analysis, and anomaly detection solutions. To address the current lack of a comprehensive overview and thorough analysis of cloud computing datasets and to gain insight into their current status and future trends, in this article, we provide a comprehensive survey of existing publicly cloud computing datasets. First, we utilize a systematic mapping approach to analyze 968 scientific papers from 6 scientific databases, resulting in the retrieval of 42 datasets related to cloud computing. Second, we categorize these datasets based on 11 characteristics to assist researchers in quickly finding datasets suitable for their specific needs. Third, we provide detailed descriptions of each dataset to assist researchers in gaining a clearer understanding of their characteristics. Fourth, we select 12 mainstream datasets and conduct a comprehensive analysis and comparison of their characteristics. Finally, we discuss the weaknesses of existing datasets, identify challenges, provide recommendations for long-term dataset maintenance and updates, and outline directions for the future creation of new cloud computing datasets. Related resources are available at https://github.com/ACAT-SCUT/Awesome-CloudComputing-Datasets .

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints.

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 cooling systems with considerable energy-saving potential will be essential to realize the green and low-carbon development of CDCs. Therefore, to track the research progress of data center thermal management technologies, this review focuses on two research efforts: thermal modeling and thermal-aware energy saving methods. First, various thermal modeling approaches are reviewed for air-cooled and liquid-cooled data centers. Secondly, a comprehensive review of existing advanced thermal management approaches is conducted from three perspectives: thermal-aware IT load scheduling, cooling system control optimization, and joint optimization of the IT and cooling systems. Finally, we put forward some open issues and future research directions for thermal management that have not been completely solved. This review aims to provide reasonable suggestions to enhance cooling energy efficiency and further promote the transformation of CDCs to lower energy consumption and sustainable direction.