Memory failures in large-scale production environments pose critical threats to system reliability and service availability. While existing studies have conducted in-depth analyses of the temporal and spatial correlations of memory errors, differences in characteristics across architectures remain largely unexplored. To uncover these overlooked correlations, this paper conducts an extensive analysis of over 130,000 DDR4 DIMMs collected from large-scale heterogeneous production clusters over a nine-month period. Through systematic spatial and temporal analysis across two Intel x86 architectures and four major DRAM vendors, we uncover five new findings and propose a novel twostage training strategy. This strategy addresses sample quality issues by applying temporal weighting to positive samples and adaptive reweighting to negative samples. It also incorporates comprehensive multi-dimensional feature engineering, covering static, spatial, temporal, and micro-level characteristics. Finally, it integrates dual-driven sampling strategies and adaptive prediction timing to balance prediction accuracy and operational efficiency. Extensive evaluation shows that our CatBoost-based model achieves F1-scores of 49.9% on Intel x86v5 and 57.6% on Intel x86v6, substantially outperforming existing methods. This cross-architecture validation demonstrates the robustness and generalization of our approach across different hardware platforms. To the best of our knowledge, our work presents the first large-scale cross-architecture analysis of memory error patterns and provides new insights for production-scale memory failure prediction systems.

Infrastructure as a Service (IaaS) is important in Cloud Computing, which provides on-demand virtual machines (VMs) to users. Proper deployment of the virtual machine onto available hosts plays an important role on the load-balancing of modern data center. At present, many load-balancing methods are based on the load forecasting model, that could predict the resource requirements of the virtual machine. However, the resource requirement of virtual machine in IaaS Cloud is hard to predict because there will be variety of load types in IaaS cloud. Moreover, a variety of heterogeneous hardware environments and virtualization technologies make it hard to predict the requirement of virtual machine based on the workloads. To address the problem, we propose a model based method to predict and calculate the resource requirement of each virtual machine, and using this model to design a load-balancing framework in IaaS Cloud. The contribution of this paper includes: (1) A model that forecast the load and estimate the resource requirement of virtual machines in IaaS Cloud, (2) A scalable framework for load-balancing which uses our resource requirement forecasting model. Experiments show that our method can accurately estimate the resource requirements of virtual machines, and work well in our load-balancing framework.

There are three types of web service QoS criterion weight: subjective weight decided by user's preference of QoS criterion, objective weight affected by QoS criterion value and synthetic weight synthesized with the two. Considering the uncertainty of conversion from qualitative value, which is used to describe user's preference, to quantitative value, the uncertainty of subjective weight is described by cloud model and the weight is calculated using normalization method and least square method. Objective weight is obtained using entropy method. For synthetic weight, Cobb-Douglas method is used to synthesize subjective and objective weight. Lastly, a case is studied and sensibility analysis of weight based on uncertain weight is discussed.