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"""
A model is the output of simulator. It contains the data the simulator output, under a certain topology, seed,
workload, datacenter configuration, etc. A model is further used in the analyzer as part of the MultiModel class,
and further in the MetaModel class.
:param sim: the simulation data of the model
"""
import json
from dataclasses import dataclass, field
@dataclass
class Model:
"""
Represents a single simulation output containing various data metrics collected under specific simulation conditions.
A Model object stores raw and processed simulation data and is designed to interact with higher-level structures like
MultiModel and MetaModel for complex data analysis.
Attributes:
raw_sim_data (list): Initial raw data from the simulator output.
processed_sim_data (list): Data derived from raw_sim_data after applying certain processing operations like aggregation or smoothing.
cumulative_time_series_values (list): Stores cumulative data values useful for time series analysis.
id (int): Unique identifier for the model, typically used for tracking and referencing within analysis tools.
path (str): Base path for storing or accessing related data files.
cumulated (float): Cumulative sum of processed data, useful for quick summaries and statistical analysis.
experiment_name (str): A descriptive name for the experiment associated with this model, potentially extracted from external metadata.
margins_of_error (list): Stores error margins associated with the data, useful for uncertainty analysis.
topologies (list): Describes the network or system topologies used during the simulation.
workloads (list): Lists the types of workloads applied during the simulation, affecting the simulation's applicability and scope.
allocation_policies (list): Details the resource allocation policies used, which influence the simulation outcomes.
carbon_trace_paths (list): Paths to data files containing carbon output or usage data, important for environmental impact studies.
Methods:
parse_trackr(): Reads additional configuration and metadata from a JSON file named 'trackr.json', enhancing the model with detailed context information.
Usage:
Model objects are typically instantiated with raw data from simulation outputs and an identifier. After instantiation,
the 'parse_trackr' method can be called to load additional experimental details from a corresponding JSON file.
"""
path: str
raw_sim_data: list
id: int
processed_sim_data: list = field(default_factory=list)
cumulative_time_series_values: list = field(default_factory=list)
cumulated: float = 0.0
experiment_name: str = ""
margins_of_error: list = field(default_factory=list)
topologies: list = field(default_factory=list)
workloads: list = field(default_factory=list)
allocation_policies: list = field(default_factory=list)
carbon_trace_paths: list = field(default_factory=list)
def parse_trackr(self):
"""
Parses the 'trackr.json' file located in the model's base path to extract and store detailed experimental metadata.
This method enhances the model with comprehensive contextual information about the simulation environment.
:return: None
:side effect: Updates model attributes with data from the 'trackr.json' file, such as experiment names, topologies, and policies.
:raises FileNotFoundError: If the 'trackr.json' file does not exist at the specified path.
:raises json.JSONDecodeError: If there is an error parsing the JSON data.
"""
trackr_path = self.path + "/trackr.json"
with open(trackr_path) as f:
trackr = json.load(f)
self.experiment_name = trackr.get(self.id, {}).get('name', "")
self.topologies = trackr.get(self.id, {}).get('topologies', [])
self.workloads = trackr.get(self.id, {}).get('workloads', [])
self.allocation_policies = trackr.get(self.id, {}).get('allocationPolicies', [])
self.carbon_trace_paths = trackr.get(self.id, {}).get('carbonTracePaths', [])
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