Traditional data center usually conducts asset management manually, which is costly and error-prone. For large data centers, intelligent asset management is of great significance. Intelligent inspection robots, as a substitute for manual inspection methods, have been widely used in various industries. This article first studies a real-time positioning and mapping technology that integrates multi-sensor data. The robot achieves high-precision autonomous indoor navigation and positioning with positioning errors less than 2 cm. Then, an image recognition technology that can remove occlusion was studied. It reads the detailed information of the equipment inside the cabinet through the cabinet grid door. The experimental results show that the recognition accuracy of the QR code on the equipment inside cabinet can reach97.2%. Finally, an intelligent inspection robot system was built to conduct 24-hour intelligent inspection of the data center.
The data center uses virtualization and isolation technologies to provide flexible and efficient services for multi-tenants. One of the most challenging aspect of resource sharing is task scheduling. During the scheduling process, it is crucial to ensure fairness in user resource usage and achieve high cluster utilization and energy efficiency. However, the heterogeneity of resources and the variations in user demands make it extremely difficult to provide an effective scheduling solution. In this paper, we propose an efficient heuristic scheduling algorithm called SAUFEE, which trades off the resource requirement of multi-tenants and cluster power consumption. First, we introduce a user fairness model, which prioritizes the tasks of users with the least resource allocation in each scheduling round, ensuring fairness among them. Next, we propose a resource utilization model to schedule user tasks to reduce resource waste. Additionally, idle machines are shut down to save overall cluster energy consumption. The simulation experiment results show that our algorithm increases the number of running tasks by 3.3% and the CPU utilization by 3.4% while ensuring fairness. Our algorithm plays an important role in improving cluster energy efficiency and user fairness.
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