The rapid expansion of cloud computing has made precise performance evaluation a critical necessity. However, conventional cloud benchmarks often face significant limitations in simulation environments—necessary for scalable and cost-effective testing—due to the complexity of technology stacks and substantial runtime overheads. Proxy benchmarking has thus emerged as a practical alternative. Existing methods primarily focus on the global similarity of micro-architectural metrics between proxy benchmarks and real workloads but neglect their temporal similarity, leading to inaccurate performance evaluations, flawed cache behavior simulations, and misguided architectural optimization decisions. To address this, we present PaTGen , a phase-aware method for generating proxy benchmarks that accurately reflect both global and temporal similarity. By partitioning workloads into phases and formulating proxy generation as nonlinear optimization problems, PaTGen further refines intra-phase execution patterns via the delay-based temporal similarity optimization (DTSO) technique. Evaluations on 15 real-world workloads show PaTGen achieves over 97% global similarity in key metrics while significantly outperforming state-of-the-art methods in temporal similarity. Ablation studies confirm the efficacy of phase division and DTSO. Further experiments confirm its scalability and generalizability across architectures. Moreover, the effectiveness observed in downstream tasks provides empirical evidence that preserving temporal similarity is a fundamental requirement for proxy benchmarks to faithfully capture real workload behavior.

Data centers often deploy servers that exceed the capacity of the power infrastructure to increase power utilization, i.e., power over-subscription. To address potential power overloads, power capping mechanisms are implemented for protection. However, the power efficiency disparities among heterogeneous servers, along with the adjustment intervals required for power capping at the cluster level, pose challenges to capping decisions, especially for latency-sensitive applications with higher quality of service requirements. To tackle this challenge, we propose a prediction-based, power efficiency-aware dynamic power capping framework (PPE-DPC), comprising two stages. First, we design a lightweight online prediction to capture requests generated within the power adjustment time intervals. Then, leveraging prior knowledge of the power efficiency of heterogeneous servers, we design a greedy strategy to fine-tune the capping power for better capping decisions. Extensive simulations and conducted in Testbed using Alibaba traces demonstrate that PPE-DPC outperforms existing solutions, optimizing request latency, power utilization, and mitigating the negative impact of power adjustment intervals. Finally, we also explore the effect of prediction error on PPE-DPC and find that only 3x the true prediction error is weaker than the existing optimal capping algorithm.

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 costs and mitigate environmental impacts, modern DCs not only use energy-efficient technologies to improve the efficiency of common IT and cooling systems, but also actively optimize the direct, indirect and environmental costs of the power supply side, posing significant challenges for DCs power management. Therefore, this paper presents a comprehensive survey of cost-aware optimization from the power supply perspective. First, it reviews the structures and key metrics of the power supply system, modeling methods and supporting techniques for main power and IT system components, establishing a foundation for optimization. Second, traditional (brown) and green energy sources are categorized to survey and compare existing critical works, analyzing the application of power management methods to tackle cost-related challenges. Finally, future research trends in the power supply perspective for DCs are discussed. This survey aims to provide recommendations for power supply side cost optimization to further advance the sustainable development of DCs.

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.