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convert_data_format.py
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162 lines (142 loc) · 4.5 KB
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from utilities import load_base_instance, load_requests_traces
from copy import deepcopy
from typing import Tuple
import numpy as np
import yaml
import os
def build_arrivals_list(
requests: dict, mt: int, Mt: int, ts: int
) -> list:
Nf = len(requests)
Nn = len(requests[0])
# loop over time
arrivals = []
for t in range(mt, Mt, ts):
# loop over nodes
loadt_list = []
for n in range(Nn):
# loop over functions
for f in range(Nf):
loadt = {
"node": f"n{n+1}",
"function": f"f{f+1}",
"rate": float(requests[f][n][t])
}
loadt_list.append(loadt)
arrivals.append(loadt_list)
return arrivals
def build_base_spec_dict() -> dict:
spec_dict = {
"classes": [{
"name": "critical",
"max_resp_time": None,
"utility": 1.0,
"arrival_weight": 1.0
}],
"nodes": [],
"functions": [],
"arrivals": []
}
return spec_dict
def compute_avg_demand(demand: dict, Nn: int, Nf: int) -> Tuple[float, float]:
demand_avg = {}
demand_std = {}
for f in range(Nf):
# compute mean
demand_avg[f] = 0.0
for n in range(Nn):
demand_avg[f] += demand[(n+1, f+1)]
demand_avg[f] /= Nn
# compute standard deviation
demand_std[f] = 0.0
for n in range(Nn):
demand_avg[f] += (demand[(n+1, f+1)] - demand_avg[f])**2
demand_avg[f] = np.sqrt(demand_avg[f] / (Nn - 1))
return demand_avg, demand_std
def update_arrivals_info(base_spec_dict: dict, requests: dict, t: int) -> dict:
Nf = len(requests)
Nn = len(requests[0])
# loop over nodes
spec_dict = deepcopy(base_spec_dict)
for n in range(Nn):
# loop over functions
for f in range(Nf):
loadt = {
"node": f"n{n+1}",
"function": f"f{f+1}",
"rate": float(requests[f][n][t])
}
spec_dict["arrivals"].append(loadt)
return spec_dict
def update_functions_info(base_spec_dict: dict, instance_data: dict) -> dict:
# get functions information from instance data
Nn = instance_data[None]["Nn"][None]
Nf = instance_data[None]["Nf"][None]
memory = instance_data[None]["memory_requirement"]
demand = instance_data[None]["demand"]
# compute demand average and standard deviation
demand_avg, demand_std = compute_avg_demand(demand, Nn, Nf)
# loop over functions
spec_dict = deepcopy(base_spec_dict)
for f in range(Nf):
funct = {
"name": f"f{f+1}",
"memory": int(memory[f+1]),
"duration_mean": float(demand_avg[f]),
"duration_scv": float(max(0.00001, demand_std[f]))
}
spec_dict["functions"].append(funct)
# compute threshold
da = sum(demand_avg.values()) / len(demand_avg)
spec_dict["classes"][0]["max_resp_time"] = float(10 * da)
return spec_dict
def update_nodes_info(base_spec_dict: dict, instance_data: dict) -> dict:
# get node information from instance data
Nn = instance_data[None]["Nn"][None]
memory = instance_data[None]["memory_capacity"]
# loop over nodes
spec_dict = deepcopy(base_spec_dict)
for n in range(Nn):
node = {
"name": f"n{n+1}",
"region": "edge",
"memory": int(memory[n+1])
}
spec_dict["nodes"].append(node)
# add one cloud "node"
cloud_node = {
"name": "cloud",
"region": "cloud",
"cost": 0.00001,
"speedup": 1,
"memory": int(sum(list(memory.values()) * 100))
}
spec_dict["nodes"].append(cloud_node)
return spec_dict
def load_and_convert(instance_folder: str, dest_folder: str) -> dict:
os.makedirs(dest_folder, exist_ok = True)
# load instance data
base_instance, _ = load_base_instance(instance_folder)
# build base dictionary (no info on the arrival rates)
spec_dict = build_base_spec_dict()
spec_dict = update_nodes_info(spec_dict, base_instance)
spec_dict = update_functions_info(spec_dict, base_instance)
# save
with open(os.path.join(dest_folder, "base_spec.yaml"), "w") as ostream:
yaml.dump(spec_dict, ostream)
return spec_dict
def main(instance_folder: str, dest_folder: str):
# build base dictionary
base_spec_dict = load_and_convert(instance_folder, "spec")
# load requests traces
requests, mt, Mt, ts = load_requests_traces(instance_folder)
# loop over time
for t in range(mt, Mt, ts):
# add arrivals info
spec_dict = update_arrivals_info(base_spec_dict, requests, t)
# save
with open(os.path.join(dest_folder, f"spec_t{t}.yaml"), "w") as ostream:
yaml.dump(spec_dict, ostream)
if __name__ == "__main__":
instance_folder = "solutions/2024_RussoRusso_2/2025-09-22_16-37-07.629719"
main(instance_folder, "spec")