Optimization of Workload Distribution of Data Centers Based on a Self Learning In Situ Adaptive Tabulation Model
Data center cooling typically involves non uniform airflow and temperature distributions, which are affected by the IT workload distribution. It is helpful to simulate the airflow and temperature to optimize the workload distribution. Traditional computational fluid dynamics (CFD) simulation is usually time consuming while conventional reduced order models (ROMs) though computationally fast, may generate inaccurate results even after being fully trained. In Situ Adaptive Tabulation (ISAT) contracting to conventional ROM, can make prediction with error lower than a user specified tolerance. To demonstrate using of ISAT for optimal workload distribution in data center, this paper presents a preliminary study of an ISAT based genetic algorithm optimization platform. The ISAT is trained offline by using the results from CFD simulations using a hypothetical simple data center. The optimal workload distribution determined by the platform leads to approximately 6.8% of energy savings when compared to the benchmark with a uniform workload distribution. We note that the time cost for the entire optimization process, including the training of ISAT is about 4 hours, which is acceptable in the design phase. Introduction ASHRAE (2015) recommends 27 C as the maximum inlet temperature for the IT equipment. In reality, computer room air conditioners (CRACs) in many data centers are operated at a significantly lower supply temperature to avoid hot spots. While providing redundant cooling, this leads to reduced cooling efficiency (Tang et al. 2008) Therefore, it has been a research topic on how to improve the energy efficiency while still providing enough cooling to the data center whitespace, where the IT equipment is placed. One of the approaches is to optimize the workload distribution such that CRACs can operate at a higher supply temperature or lower airflow rate (Banerjee et al. 2010) Moore et al. (2005) proposed several algorithms for workload placement to maximize the supply air temperature while ensuring the server inlet temperature not exceeding the threshold. Tang et al. (2007) investigated several load placement strategies to minimize heat recirculation. Among these research, the CFD simulations were conducted to obtain the thermal map of the data center with regard to workload distribution, which required huge computational efforts to cover a large number of scenarios. To reduce the computational cost, simplified airflow models were proposed for fast prediction of indoor environment. Zhang et al. (2009) developed a lumped model to predict rack inlet temperatures under various supply airflow and rack load scenarios. Since only one effective aggregated rack and cooling unit were considered based on a well mixed assumption, the lumped model may fail to capture local airflow pattern and temperature distribution. Potential flow models were used to predict the airflow and temperature distribution (Toulouse et al. 2009) However, the potential flow model is an approximate and simplified model, which may not be as accurate as CFD, particularly when simulating the jet flows from the perforated tiles. Other than using physic based models to generate simplified models, statistics based approaches, sometimes referred to as reduced order models (ROMs) were also employed. Proper orthogonal decomposition (POD) was used to predict the velocity and temperature distribution in an office (Elhadidi and Khalifa 2005) and rack inlet temperature distribution in a raise floor data center (Demetriou and Khalifa 2013) Artificial neural network (ANN) models were used to predict thermal map of data centers (Moore et al. 2006) perforated tile flowrates and rack inlet temperatures (Song et al. 2011) However, the predictions of such statistics based models may become inaccurate when the queries lie beyond the training domain. To overcome the limitations of these simplified models, an online self learning ROM called in situ adaptive tabulation (ISAT) was proposed. ISAT is a storage andretrieval algorithm that was originally developed by Pope (1997) to accelerate turbulent combustion simulations. Tian et al. (2018) applied it in the indoor environment simulation and presented promising results in terms of its training and prediction. ISAT, which allows user to specify an error tolerance, retrieves the output of a query using linear interpolation if the error of such retrieval is estimated within the error tolerance. Otherwise, as opposed to the conventional ROMs that would continue the retrieving, ISAT will call a full scale simulator (such as CFD) to resolve the query. We will cover the detail of the ISAT in the coming section. In this paper, we present an optimization platform based on ISAT and genetic algorithm to optimize workload ________________________________________________________________________________________________ ________________________________________________________________________________________________ Proceedings of the 16th IBPSA Conference Rome, Italy, Sept. 2 4, 2019 657 https:/doi.org/10.26868/25222708.2019.210589 distribution in a hypothetical data center. To accelerate the training of ISAT, we employ a fast fluid dynamics (FFD) model running on graphics processing unit (GPU) (Tian et al. 2017; Tian, VanGilder, Han, et al. 2019; Tian, VanGilder, Condor, et al. 2019) to simulate the airflow and temperature distributions under different workload distributions. With the optimal workload distribution determined by the platform, CRAC supply air temperature can be increased approximately by 0.8 C, and this leads to 6.8% cooling energy savings.