Digital Transformation Driven by Internet Data Center

With the acceleration of the digital transformation and technological upgradation of various industries, in the wake of application of new technologies such as 5G, artificial intelligence, and the internet of things, the demand for data storage, computing, transmission, and applications has greatly increased. Remote working, remote education, and e-commerce on account of the pandemic have led to a drastic increase in data consumption as well. The processing and analysis of massive data requires the construction of an information infrastructure—Internet Data Center (IDC). In the past few years, China's government has been dedicating itself to the task of constructing IDCs in some underdeveloped areas and establishing more detailed regulations. This chapter introduces some basic policies and implications behind this and a mathematical way to quantitatively analyse the investment efficiency of R&D resources in China's different regions. Several recommendations for the government and the society at large have also been outlined in this chapter for improvement in the whole ecosystem for IDCs in China.

Performance of an internet data center refrigeration system using an evaporative cooler

The traditional refrigeration method of internet data center (IDC) is mostly air refrigeration, which has undesired cooling effect and high power consumption. This study addresses this problem and proposes an evaporative air cooler (EAC) suitable for IDC. Given the high specific heat capacity of water, the evaporative condensing coil and spray device are added to the evaporative cooler to enhance the heat transfer effect. Heat and mass transfer mathematical models are established to analyze the heat transfer performance. The mathematical model is used to simulate the profile of the heat and mass transfer coefficient of the EAC with the amount of spray water and air flow. The results show that when the air flow changes from 10 to 20 kg/s, the air equivalent heat transfer coefficient increases by about 41%. When the air flow rate is 20 kg/s and the spray water volume is 0.00124 kg/(mꞏs), the total heat transfer coefficient is increased by about 308% compared with the case without spray water.

An Energy Dynamic Control Algorithm Based on Reinforcement Learning for Data Centers

In recent years, how to use renewable energy to reduce the energy cost of internet data center (IDC) has been an urgent problem to be solved. More and more solutions are beginning to consider machine learning, but many of the existing methods need to take advantage of some future information, which is difficult to obtain in the actual operation process. In this paper, we focus on reducing the energy cost of IDC by controlling the energy flow of renewable energy without any future information. we propose an efficient energy dynamic control algorithm based on the theory of reinforcement learning, which approximates the optimal solution by learning the feedback of historical control decisions. For the purpose of avoiding overestimation, improving the convergence ability of the algorithm, we use the double [Formula: see text]-method to further optimize. The extensive experimental results show that our algorithm can on average save the energy cost by 18.3% and reduce the rate of grid intervention by 26.2% compared with other algorithms, and thus has good application prospects.