1.Intelligent Detection
Intelligent detection plays a critical role in ensuring the security and reliability of cloud computing platforms. Machine learning-driven fault diagnosis techniques can detect potential issues in advance, preventing system downtime. Intrusion detection systems based on deep learning can identify and block various network attacks, ensuring the security of data and systems. Anomaly prediction and automatic repair technologies can automatically trigger repair procedures upon detecting system anomalies, ensuring the stable operation of the system.
2.Intelligent Scheduling
Intelligent scheduling significantly improves system performance and user experience by efficiently allocating resources and scheduling tasks. Advanced machine learning algorithms are used to optimize resource allocation and load balancing, enhancing system performance and resource utilization. Energy efficiency models and optimization algorithms are introduced to dynamically adjust server operational states, reducing energy consumption and operational costs. Big data and machine learning technologies predict task demands and perform pre-scheduling to ensure efficient operation during peak periods and improve system elasticity.
3.Intelligent Decision-Making
Intelligent decision-making involves macro-level decisions based on the analysis of computing cluster and network communication data. Spatial-temporal data analysis is employed to assess performance metrics in different regions, identifying and pinpointing performance bottlenecks, optimizing cluster layout, and improving network coverage to enhance service quality. Through association analysis and time series forecasting, user access and usage patterns are analyzed to predict future user behaviors and demands, optimizing service stability. By balancing and optimizing multiple objectives such as performance, cost, energy efficiency, and latency, an overall optimal outcome is achieved.
4.AI and Cloud Computing Empowering the Low-Altitude Economy
AI and cloud computing significantly enhance the low-altitude economy, particularly through Unmanned Aerial Vehicles (UAVs). By integrating intelligent algorithms, UAV systems achieve autonomous navigation, obstacle avoidance, and optimized flight paths. Cloud platforms enable efficient data processing, real-time monitoring, and predictive maintenance, while AI models assist in dynamic route planning based on weather, traffic, and operational needs. This synergy optimizes efficiency, reduces costs, and supports the Intelligent Ubiquitous Cloud, enabling the integration of AI and cloud technologies to deliver pervasive, intelligent solutions across industries.
China Telecom Cloud Computing Research Institute Beijing Headquarters
(No. 42, Xueyuan Road, Haidian District, Beijing)
China Telecom Cloud Computing Research Institute Shanghai Branch (No. 211, Century Avenue, Pudong New Area, Shanghai)
Beijing Public Security Registration No. 11040140060 | Beijing ICP No. 12007914
Value-Added Telecommunications Business Operation License A2.B1.B2-20090001
1.Intelligent Detection
Intelligent detection plays a critical role in ensuring the security and reliability of cloud computing platforms. Machine learning-driven fault diagnosis techniques can detect potential issues in advance, preventing system downtime. Intrusion detection systems based on deep learning can identify and block various network attacks, ensuring the security of data and systems. Anomaly prediction and automatic repair technologies can automatically trigger repair procedures upon detecting system anomalies, ensuring the stable operation of the system.
2.Intelligent Scheduling
Intelligent scheduling significantly improves system performance and user experience by efficiently allocating resources and scheduling tasks. Advanced machine learning algorithms are used to optimize resource allocation and load balancing, enhancing system performance and resource utilization. Energy efficiency models and optimization algorithms are introduced to dynamically adjust server operational states, reducing energy consumption and operational costs. Big data and machine learning technologies predict task demands and perform pre-scheduling to ensure efficient operation during peak periods and improve system elasticity.
3.Intelligent Decision-Making
Intelligent decision-making involves macro-level decisions based on the analysis of computing cluster and network communication data. Spatial-temporal data analysis is employed to assess performance metrics in different regions, identifying and pinpointing performance bottlenecks, optimizing cluster layout, and improving network coverage to enhance service quality. Through association analysis and time series forecasting, user access and usage patterns are analyzed to predict future user behaviors and demands, optimizing service stability. By balancing and optimizing multiple objectives such as performance, cost, energy efficiency, and latency, an overall optimal outcome is achieved.
4.AI and Cloud Computing Empowering the Low-Altitude Economy
AI and cloud computing significantly enhance the low-altitude economy, particularly through Unmanned Aerial Vehicles (UAVs). By integrating intelligent algorithms, UAV systems achieve autonomous navigation, obstacle avoidance, and optimized flight paths. Cloud platforms enable efficient data processing, real-time monitoring, and predictive maintenance, while AI models assist in dynamic route planning based on weather, traffic, and operational needs. This synergy optimizes efficiency, reduces costs, and supports the Intelligent Ubiquitous Cloud, enabling the integration of AI and cloud technologies to deliver pervasive, intelligent solutions across industries.
