Temperature monitoring and regulation is a critical aspect of data center administration. Currently, conventional discrete transistor-based thermal sensing systems are widely used for this purpose, which requires a discrete device for each temperature measurement in the special domain. This leads to an increase in both complexity and cost as the data center grows in scale. This manuscript describes a real-time multiplexed optical fiber thermal sensing system for data center applications which simultaneously measures thousands of discrete points along the length of the fiber under test. This system allows for real-time thermal monitoring of several hundred servers with a spatial resolution of 1 cm, a temperature resolution of <1 °C, and a system update rate of 1 Hz. Temperature inside of individual servers and the ambient room temperature outside the racks can be simultaneously monitored in real time using a single optical fiber probe. To investigate this concept, a pilot experiment is presented which monitored the dynamic server temperature distribution using the proposed fiber sensing system. Temperature data recorded using built-in thermal sensors within the CPU of the server under test were simultaneously recorded and compared to measurements made. In order to induce a temperature change within the server, a computationally intensive task was undertaken during temperature testing. Both methods of temperature measurement demonstrated similar trends, indicating that the proposed multiplexed optical fiber-based system has substantial potential as a scalable method of distributed data center temperature monitoring.
In this paper, a modified particle swarm optimization (PSO) approach, particle swarm optimization with ε- greedy exploration εPSO), is used to tackle the object tracking. In the modified εPSO algorithm, the cooperative learning mechanism among individuals has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best individuals according to certain probability. This kind of biologically-inspired mutual-learning behavior can help to find the global optimum solution with better convergence speed and accuracy. The εPSO algorithm has been tested on benchmark function and demonstrated its effectiveness in high-dimension multi-modal optimization. In addition to the standard benchmark study, we also combined our new εPSO approach with the traditional particle filter (PF) algorithm on the object tracking task, such as car tracking in complex environment. Comparative studies between our εPSO combined PF algorithm with those of existing techniques, such as the particle filter (PF) and classic PSO combined PF will be used to verify and validate the performance of our approach.