AI-Powered Green Cloud and Data Center

Abstract

As the scale of cloud computing expands, its impact on energy and the environment is becoming more and more prominent. According to statistics, data centers' energy consumption has accounted for 50% of operating costs of the data centers. The rising energy consumption not only needs energy in large quantity but also imposes heavy pressure on the environment. The high energy consumption of cloud data center has become an issue, people pay close attention to, in the information technology field. It is also a problem to be solved urgently. At present, high energy consumption is caused by two reasons. First, a resource scheduling mechanism with the priority of completion time causes low server use ratio, and it is a pervasive phenomenon that small tasks take high consumption. Second, the current refrigerating system of the data center is based on peak value strategy, which causes excessive cooling supply, increases operation cost, and leads to huge waste of energy. In this paper, considering the reason of high energy consumption of the data center, a new framework of green cloud data center is put forward. Using the techniques relevant to artificial intelligence, we put forward a scheduling control engine and an intelligent refrigerating engine aiming at reduction of energy consumption. In addition, we build a green cloud data center platform, realize the scheduling control engine, and verify the feasibility of the framework. It indicates that the framework can realize a cloud platform with low power consumption and a high-energy-efficient data center operation. INDEX TERMS Green cloud and data center, energy optimizing, deep learning, particle swarm optimization.

[Retracted] Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

“…P i,n+1 � P i,n (15) else ( 16) P i,n+1 � X i,n+1 (17) Calculate the data center energy consumption by e i (18) for k � 1 to N 1 do (19) for l � 1 to N 2 do Calculate U j i,n+1 according to…”

“…Renewable energy sources such as solar energy and wind energy have been considered for data center, and two improvements to small and medium-sized data center based on opportunistic scheduling and reliance on energy storage devices had been considered [18]. It was believed that the low server utilization rate caused by the resource scheduling mechanism with the completion time as the priority and the excessive cooling supply caused by the data center cooling system based on the peak strategy were the main reasons for the increase in the data center energy consumption, and artificial intelligence could be used to construct an energy consumption control scheduling framework aiming at reducing energy consumption [19]. Kumar proposed to use the dynamic voltage frequency scheduling (DVFS) scheme to assign task to virtual machines and extended data center energy efficient network aware scheduling through point-to-point load balancers to reduce network energy consumption [20].…”

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

“…Workload placement strategies considered are based on zones discretization, minimize the heat recirculation, and prioritize the servers for task allocation by observing hot airflow within the DC [ 47 , 48 , 51 ]. Scheduling methodologies common in DCs such as first-come-first-serve or backfilling do not usually consider the thermal perspective [ 52 , 53 ]. Machine learning-based models are proposed to infer scheduling policies with thermal features.…”

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders

“…This can lead to a considerable decrease in energy consumption in data centers. This can ease not only the problem of high energy costs in data centers but also the critical challenges imposed on the environment (Yang et al, 2018). As outlined by the High-Level Expert Group on Artificial Intelligence (AI) (AI High-level Expert Group, 2020), environmental well-being is one of the requirements of a trust-worthy AI system.…”