File size: 9,879 Bytes
5fed0fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 |
from typing import List
from pprint import pprint
import networkx as nx
import json
import colorama
from colorama import Fore, Style
from utils import networkx_to_graphviz
from broadcast import *
from utils import *
class BCSimulator:
# Default variables
data_vol: float = 4.0 # size of data to be sent to multiple dsts
num_partitions: int = 1
partition_data_vol: int = data_vol / num_partitions
default_vms_per_region: int = 1
cost_per_instance_hr: float = 0.54 # based on m5.8xlarge spot
src: str
dsts: List[str]
algo: str
g = nx.DiGraph
def __init__(self, num_vms, output_dir=None):
# write output to file
self.output_dir = output_dir
self.default_vms_per_region = num_vms
def initialization(self, path, config):
# check if path is dict
if isinstance(path, str):
# Read from json
with open(path, "r") as f:
data = json.loads(f.read())
else:
data = {
"algo": "none",
"source_node": path.src,
"terminal_nodes": path.dsts,
"num_partitions": path.num_partitions,
"generated_path": path.paths,
}
self.src = data["source_node"]
self.dsts = data["terminal_nodes"]
self.algo = data["algo"]
self.paths = data["generated_path"]
self.num_partitions = config["num_partitions"]
self.data_vol = config["data_vol"]
self.partition_data_vol = self.data_vol / self.num_partitions
# Default in/egress limit if not set
providers = ["aws", "gcp", "azure"]
provider_ingress = [10, 16, 16]
provider_egress = [5, 7, 16]
self.ingress_limits = {providers[i]: provider_ingress[i] for i in range(len(providers))}
self.egress_limits = {providers[i]: provider_egress[i] for i in range(len(providers))}
if "ingress_limit" in config:
for p, limit in config["ingress_limit"].items():
self.ingress_limits[p] = self.default_vms_per_region * limit
if "egress_limit" in config:
for p, limit in config["egress_limit"].items():
self.egress_limits[p] = self.default_vms_per_region * limit
# print("Data vol (Gbit): ", self.data_vol * 8)
print("Ingress limits: ", self.ingress_limits)
print("Egress limits: ", self.egress_limits)
def evaluate_path(self, path, config, write_to_file=False):
print(f"\n==============> Evaluation")
self.initialization(path, config)
# construct graph
print(f"\n--------- Algo: {self.algo}")
self.g = self.__construct_g()
print("\n=> Data path to dests")
for path in self.__get_path():
print("--")
print(path)
# NOTE: check
for i in range(len(path) - 1):
print(f"Flow: {self.g[path[i]][path[i+1]]['flow']}")
print(f"Actual throughput: {round(self.g[path[i]][path[i+1]]['throughput'], 4)}")
print(f"Cost: {self.g[path[i]][path[i+1]]['cost']}\n")
# evaluate transfer time and total cost
max_t, avg_t, last_dst = self.__transfer_time()
self.cost = self.__total_cost()
# output to json file
if write_to_file:
open(f"{self.output_dir}/{self.algo}_eval.json", "w").write(
json.dumps(
{
"path": path,
"max_transfer_time": max_t,
"avg_transfer_time": avg_t,
"last_dst": last_dst,
"tot_cost": self.cost,
}
)
)
return max_t, self.cost
def __construct_g(self):
# construct a graph based on the given topology
g = nx.DiGraph()
for dst in self.dsts:
for partition_id in range(self.num_partitions):
print(self.paths)
print("Num of partitions: ", self.num_partitions)
for edge in self.paths[dst][str(partition_id)]:
src, dst, edge_data = edge[0], edge[1], edge[2]
if not g.has_edge(src, dst):
cost = edge_data["cost"]
throughput = edge_data["throughput"] # * self.default_vms_per_region
g.add_edge(src, dst, throughput=throughput, cost=edge_data["cost"], flow=throughput)
g[src][dst]["partitions"] = set()
g[src][dst]["partitions"].add(partition_id)
# h = networkx_to_graphviz(g, self.src, self.dsts, label="throughput")
# h.render(view=True)
print(f"Default vms: {self.default_vms_per_region}")
# Proportionally share if exceed in/egress limit of any node
for node in g.nodes:
provider = node.split(":")[0]
in_edges, out_edges = g.in_edges(node), g.out_edges(node)
in_flow_sum = sum([g[i[0]][i[1]]["flow"] for i in in_edges])
out_flow_sum = sum([g[o[0]][o[1]]["flow"] for o in out_edges])
if in_flow_sum > self.ingress_limits[provider]:
# print("\nExceed ingress limit")
for edge in in_edges:
src, dst = edge[0], edge[1]
# assign based on flow proportion
# flow_proportion = g[src][dst]['throughput'] / in_flow_sum
# or assign based on num of incoming flows
flow_proportion = 1 / len(list(in_edges))
g[src][dst]["flow"] = min(g[src][dst]["flow"], self.ingress_limits[provider] * flow_proportion)
if out_flow_sum > self.egress_limits[provider]:
# print("\nExceed egress limit")
for edge in out_edges:
src, dst = edge[0], edge[1]
# assign based on flow proportion
# flow_proportion = g[src][dst]['throughput'] / out_flow_sum
# or assign based on num of incoming flows
flow_proportion = 1 / len(list(out_edges))
print(f"src: {src}, dst: {dst}, flow proportion: {flow_proportion}")
g[src][dst]["flow"] = min(g[src][dst]["flow"], self.egress_limits[provider] * flow_proportion)
return g
def __get_path(self):
all_paths = [path for node in self.dsts for path in nx.all_simple_paths(self.g, self.src, node)]
return all_paths
def __slowest_capacity_link(self):
min_tput = min([edge[-1]["throughput"] for edge in self.g.edges().data()])
return min_tput
def __transfer_time(self, log=True):
# time for each (src, dst) pair
t_dict = dict()
for dst in self.dsts:
partition_time = float("-inf")
for i in range(self.num_partitions):
# NOTE: how to calculate this? is it correct for both baseline and brute-force?
for edge in self.paths[dst][str(i)]:
edge_data = self.g[edge[0]][edge[1]]
partition_time = max(partition_time, len(edge_data["partitions"]) * self.partition_data_vol * 8 / edge_data["flow"])
t_dict[dst] = partition_time
max_t = max(t_dict.values())
last_dst = [k for k, v in t_dict.items() if v == max_t] # last dst receiving obj
avg_t = sum(t_dict.values()) / len(t_dict.values())
# assert(max_t == self.data_vol / self.__slowest_capacity_link()) # checking for single data copy case
if log:
print(f"\n{Fore.BLUE}Algo: {Fore.YELLOW}{self.algo}{Style.RESET_ALL}")
print(
f"{Fore.BLUE}Data vol = {Fore.YELLOW}{self.data_vol} GB {Fore.BLUE}or {Fore.YELLOW}{self.data_vol * 8} Gbit{Style.RESET_ALL}"
)
print(f"\n{Fore.BLUE}Transfer time (s) for each destination: {Style.RESET_ALL}")
pprint({key: round(value, 5) for key, value in t_dict.items()})
print(f"{Fore.BLUE}Throughput (Gbps) for each destination: {Style.RESET_ALL}")
pprint({key: round(self.data_vol * 8 / value, 5) for key, value in t_dict.items()})
print(f"\n{Fore.BLUE}Max transfer time = {Fore.YELLOW}{round(max_t, 4)} s {Style.RESET_ALL}")
print(
f"{Fore.BLUE}Overall throughput = {Fore.YELLOW}{round(self.data_vol * 8 / max_t, 4)} Gbps{Style.RESET_ALL}"
) # data size / max transfer time
print(f"{Fore.BLUE}Last dst receiving data = {Fore.YELLOW}{last_dst}{Style.RESET_ALL}")
# print(f"The avg transfer time is: {round(avg_t, 3)}")
return max_t, avg_t, last_dst
def __total_cost(self):
sum_egress_cost = 0
for edge in self.g.edges.data():
edge_data = edge[-1]
sum_egress_cost += (
len(edge_data["partitions"]) * self.partition_data_vol * edge_data["cost"]
) ## TODO: is this calculation correct?
runtime_s, _, _ = self.__transfer_time(log=False)
runtime_s = round(runtime_s, 2)
sum_instance_cost = 0
for node in self.g.nodes():
# print("Default vm per region: ", self.default_vms_per_region)
# print("Cost per instance hr: ", (self.cost_per_instance_hr / 3600) * runtime_s)
sum_instance_cost += self.default_vms_per_region * (self.cost_per_instance_hr / 3600) * runtime_s
sum_cost = sum_egress_cost + sum_instance_cost
print(
f"{Fore.BLUE}Sum of total cost = egress cost {Fore.YELLOW}(${round(sum_egress_cost, 4)}) {Fore.BLUE}+ instance cost {Fore.YELLOW}(${round(sum_instance_cost, 4)}) {Fore.BLUE}= {Fore.YELLOW}${round(sum_cost, 3)}{Style.RESET_ALL}"
)
return sum_cost
|