Source code for utils.dc_config_reader

"""
This file is used to read the data center configuration from  user inputs provided inside dc_config.json. It also performs some auxiliary steps to calculate the server power specifications based on the given parameters.
"""
import json
import os
from concurrent.futures import ThreadPoolExecutor, as_completed

[docs] class DC_Config: def __init__(self, dc_config_file='dc_config.json', datacenter_capacity_mw=1): """ Initializes a new instance of the DC_Config class, loading configuration data from the specified JSON configuration file. Args: dc_config_file (str): The path to the data center configuration JSON file. """ # Determine the full path to the configuration file self.config_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), dc_config_file) # Define the maximum compute power capacity of the datacenter on MW self.datacenter_capacity_mw = datacenter_capacity_mw # Load the JSON data from the configuration file self.config_data = self._load_config() # Set up configuration parameters self._setup_config() def _load_config(self): """ Loads the data center configuration from the specified JSON file. Returns: dict: A dictionary containing the loaded configuration data. """ with open(self.config_path, 'r') as file: return json.load(file) def _setup_config(self): """ Sets up various configuration parameters based on the loaded JSON data. """ ################################################################## #################### GEOMETRY DEPENDENT PARAMETERS ############### ################################################################## json_obj = self.config_data # Data Center Geometric configuration self.NUM_ROWS = json_obj['data_center_configuration']['NUM_ROWS'] # number of rows in which data centers are arranged self.NUM_RACKS_PER_ROW = json_obj['data_center_configuration']['NUM_RACKS_PER_ROW'] # number of racks/ITcabinets in each row self.NUM_RACKS = self.NUM_ROWS * self.NUM_RACKS_PER_ROW # calculate total number of racks/ITcabinets in the data center model self.TOTAM_MAX_PWR = self.datacenter_capacity_mw * 1e6 # specify maximum allowed power consumption (W) for the entire data center self.MAX_W_PER_RACK = int(self.TOTAM_MAX_PWR/self.NUM_RACKS) # calculate maximum allowable power consumption for each rack/ITcabinet # CFD may be used to precompute the "supply/return approach temperature" for each rack under given # geometry, containment, CRAC Air flow rate, Load # Supply approach temperature: It is the delta T i.e. the temperature difference between # CRAC_setpoint and the actual inlet temperature to the rack .Its value depends on the geometry # of the data center rack arrangements and can be pre-computed from CFD analysis. The length of # the list should be the same as NUM_RACKS; Default values are populated from paper [3] assuming: # Scenario # 19 from Table 5 self.RACK_SUPPLY_APPROACH_TEMP_LIST = json_obj['data_center_configuration']['RACK_SUPPLY_APPROACH_TEMP_LIST'] # Return approach temperature: It is the delta T i.e. the temperature difference between # CRAC return temperature and the rack Outlet temperature .Its value also depends on the geometry # of the data center rack arrangements and can be pre-computed from CFD analysis. The length of # the list should be the same as NUM_RACKS; Default values are populated from paper [3] assuming: # Scenario # 19 from Table 5 # we add some variation to the default values to highlight change in geometry self.RACK_RETURN_APPROACH_TEMP_LIST = json_obj['data_center_configuration']['RACK_RETURN_APPROACH_TEMP_LIST'] # how many servers are assigned in each rack. The actual number of servers per rack may be limited while self.CPUS_PER_RACK = json_obj['data_center_configuration']['CPUS_PER_RACK'] ################################################################## #################### SERVER CONFIGURATION ######################## ################################################################## # Specify the CPU Config for each cpu/server in each rack # The full load power and the idle power may be populated using spec sheets from common servers in use # This value may be ignored internally if total rack load exceeds MAX_W_PER_RACK # CPU Power Parameters self.DEFAULT_SERVER_POWER_CHARACTERISTICS = json_obj['server_characteristics']['DEFAULT_SERVER_POWER_CHARACTERISTICS'] # This list should be of length NUM_RACKS; Here DEFAULT_SERVER_POWER_CHARACTERISTICS is of same length as NUM_RACKS assert len(self.DEFAULT_SERVER_POWER_CHARACTERISTICS) == self.NUM_RACKS, "DEFAULT_SERVER_POWER_CHARACTERISTICS should be of length as NUM_RACKS" # self.RACK_CPU_CONFIG = [[{'full_load_pwr' : j[0], # 'idle_pwr': j[-1]} for _ in range(int(self.CPUS_PER_RACK))] for j in self.DEFAULT_SERVER_POWER_CHARACTERISTICS] # Parallelize the construction of RACK_CPU_CONFIG def construct_cpu_config(server_power_characteristics): """Function to construct CPU configuration for a single server.""" return [{'full_load_pwr': j[0], 'idle_pwr': j[-1]} for _ in range(int(self.CPUS_PER_RACK)) for j in server_power_characteristics] # Use ThreadPoolExecutor to parallelize the operation with ThreadPoolExecutor() as executor: # Submit tasks to the executor futures = [executor.submit(construct_cpu_config, [j]) for j in self.DEFAULT_SERVER_POWER_CHARACTERISTICS] # Wait for the futures to complete and collect the results self.RACK_CPU_CONFIG = [future.result() for future in as_completed(futures)] # A default value of HP_PROLIANT server for standalone testing self.HP_PROLIANT = json_obj["server_characteristics"]['HP_PROLIANT'] # Serve/cpu parameters; Obtained from [3] self.CPU_POWER_RATIO_LB = json_obj['server_characteristics']['CPU_POWER_RATIO_LB'] self.CPU_POWER_RATIO_UB = json_obj['server_characteristics']['CPU_POWER_RATIO_UB'] self.IT_FAN_AIRFLOW_RATIO_LB = json_obj['server_characteristics']['IT_FAN_AIRFLOW_RATIO_LB'] self.IT_FAN_AIRFLOW_RATIO_UB = json_obj['server_characteristics']['IT_FAN_AIRFLOW_RATIO_UB'] self.IT_FAN_FULL_LOAD_V = json_obj['server_characteristics']['IT_FAN_FULL_LOAD_V'] self.ITFAN_REF_V_RATIO, self.ITFAN_REF_P = json_obj['server_characteristics']['ITFAN_REF_V_RATIO'], json_obj['server_characteristics']['ITFAN_REF_P'] self.INLET_TEMP_RANGE = json_obj['server_characteristics']['INLET_TEMP_RANGE'] ################################################################## #################### HVAC CONFIGURATION ########################## ################################################################## # Air parameters self.C_AIR = json_obj['hvac_configuration']['C_AIR'] # J/kg.K self.RHO_AIR = json_obj['hvac_configuration']['RHO_AIR'] # kg/m3 # CRAC Unit paramters self.CRAC_SUPPLY_AIR_FLOW_RATE_pu = json_obj['hvac_configuration']['CRAC_SUPPLY_AIR_FLOW_RATE_pu'] self.CRAC_REFRENCE_AIR_FLOW_RATE_pu = json_obj['hvac_configuration']['CRAC_REFRENCE_AIR_FLOW_RATE_pu'] self.CRAC_FAN_REF_P = json_obj['hvac_configuration']['CRAC_FAN_REF_P'] # Chiller Stats self.CHILLER_COP = json_obj['hvac_configuration']['CHILLER_COP_BASE'] self.CW_PRESSURE_DROP = json_obj['hvac_configuration']['CW_PRESSURE_DROP'] #Pa self.CW_WATER_FLOW_RATE = json_obj['hvac_configuration']['CW_WATER_FLOW_RATE'] #m3/s self.CW_PUMP_EFFICIENCY = json_obj['hvac_configuration']['CW_PUMP_EFFICIENCY'] #% self.CHILLER_COP_K = json_obj['hvac_configuration']['CHILLER_COP_K'] self.CHILLER_COP_T_NOMINAL = json_obj['hvac_configuration']['CHILLER_COP_T_NOMINAL'] # Cooling Tower parameters self.CT_FAN_REF_P = json_obj['hvac_configuration']['CT_FAN_REF_P'] self.CT_REFRENCE_AIR_FLOW_RATE = json_obj['hvac_configuration']['CT_REFRENCE_AIR_FLOW_RATE'] self.CT_PRESSURE_DROP = json_obj['hvac_configuration']['CT_PRESSURE_DROP'] #Pa self.CT_WATER_FLOW_RATE = json_obj['hvac_configuration']['CT_WATER_FLOW_RATE']#m3/s self.CT_PUMP_EFFICIENCY = json_obj['hvac_configuration']['CT_PUMP_EFFICIENCY'] #%
#References: #[1]: Postema, Björn Frits. "Energy-efficient data centres: model-based analysis of power-performance trade-offs." (2018). #[2]: Raghunathan, S., & Vk, M. (2014). Power management using dynamic power state transitions and dynamic voltage frequency scaling controls in virtualized server clusters. Turkish Journal of Electrical Engineering and Computer Sciences, 24(4). doi: 10.3906/elk-1403-264 #[3]: Sun, Kaiyu, et al. "Prototype energy models for data centers." Energy and Buildings 231 (2021): 110603. #[4]: Breen, Thomas J., et al. "From chip to cooling tower data center modeling: Part I influence of server inlet temperature and temperature rise across cabinet." 2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems. IEEE, 2010. #[5]: https://h2ocooling.com/blog/look-cooling-tower-fan-efficiences/#:~:text=The%20tower%20has%20been%20designed,of%200.42%20inches%20of%20water.