Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures.While earlier research has emphasized CPU utilization forecasti...Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures.While earlier research has emphasized CPU utilization forecasting,joint prediction of CPU and memory usage under real workload conditions remains underexplored.This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset.The framework combines Extreme Gradient Boosting(XGBoost)with a MultiOutputRegressor(MOR)to capture nonlinear interactions across multiple resource dimensions,supported by a leakage-safe imputation strategy that prevents bias frommissing values.Nested cross-validation was employed to ensure rigorous evaluation and reproducibility.Experiments demonstrated that memory usage can be predicted with higher accuracy and stability than processor usage.Residual error analysis revealed balanced error distributions and very low outlier rates,while regime-based evaluations confirmed robustness across both low and high utilization scenarios.Feature ablation consistently highlighted the central role of page cache memory,which significantly affected predictive performance for both CPU and RAM.Comparisons with baseline models such as linear regression and random forest further underscored the superiority of the proposed approach.To assess adaptability,an online prequential learning pipeline was deployed to simulate continuous operation.The system preserved offline accuracy while dynamically adapting to workload changes.It achieved stable performance with extremely low update latencies,confirming feasibility for deployment in environments where responsiveness and scalability are critical.Overall,the findings demonstrate that simultaneous modeling of CPU and RAM utilization enhances forecasting accuracy and provides actionable insights for cache management,workload scheduling,and dynamic resource allocation.By bridging offline evaluation with online adaptability,the proposed framework offers a practical solution for intelligent and sustainable cloud resource management.展开更多
为提高列车自动监控系统中安全相关控制命令执行结果的可靠性和安全性,满足基于车-车通信的列车自主运行系统项目安全需求,设计一种基于中央处理单元(CPU)和图形处理单元(GPU)双链计算和显示的安全控制执行结果显示方法。操作终端的C P ...为提高列车自动监控系统中安全相关控制命令执行结果的可靠性和安全性,满足基于车-车通信的列车自主运行系统项目安全需求,设计一种基于中央处理单元(CPU)和图形处理单元(GPU)双链计算和显示的安全控制执行结果显示方法。操作终端的C P U计算的执行结果以字符串格式输出到指定位置显示,操作终端的GPU计算的执行结果以图元格式输出到标题栏位置显示。同时采用不同编码方法、相异的算法等方式,避免同一硬件设备在编码语言、算法和硬件平台的共模失效。展开更多
文摘Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures.While earlier research has emphasized CPU utilization forecasting,joint prediction of CPU and memory usage under real workload conditions remains underexplored.This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset.The framework combines Extreme Gradient Boosting(XGBoost)with a MultiOutputRegressor(MOR)to capture nonlinear interactions across multiple resource dimensions,supported by a leakage-safe imputation strategy that prevents bias frommissing values.Nested cross-validation was employed to ensure rigorous evaluation and reproducibility.Experiments demonstrated that memory usage can be predicted with higher accuracy and stability than processor usage.Residual error analysis revealed balanced error distributions and very low outlier rates,while regime-based evaluations confirmed robustness across both low and high utilization scenarios.Feature ablation consistently highlighted the central role of page cache memory,which significantly affected predictive performance for both CPU and RAM.Comparisons with baseline models such as linear regression and random forest further underscored the superiority of the proposed approach.To assess adaptability,an online prequential learning pipeline was deployed to simulate continuous operation.The system preserved offline accuracy while dynamically adapting to workload changes.It achieved stable performance with extremely low update latencies,confirming feasibility for deployment in environments where responsiveness and scalability are critical.Overall,the findings demonstrate that simultaneous modeling of CPU and RAM utilization enhances forecasting accuracy and provides actionable insights for cache management,workload scheduling,and dynamic resource allocation.By bridging offline evaluation with online adaptability,the proposed framework offers a practical solution for intelligent and sustainable cloud resource management.
文摘为提高列车自动监控系统中安全相关控制命令执行结果的可靠性和安全性,满足基于车-车通信的列车自主运行系统项目安全需求,设计一种基于中央处理单元(CPU)和图形处理单元(GPU)双链计算和显示的安全控制执行结果显示方法。操作终端的C P U计算的执行结果以字符串格式输出到指定位置显示,操作终端的GPU计算的执行结果以图元格式输出到标题栏位置显示。同时采用不同编码方法、相异的算法等方式,避免同一硬件设备在编码语言、算法和硬件平台的共模失效。
