research/problems/cloudcast/resources/evaluate.py

from logging import BASIC_FORMAT
from utils import *
from simulator import *
from networkx.algorithms.tree.branchings import Edmonds
from broadcast import BroadCastTopology
from pathlib import Path
import graphviz as gv
import networkx as nx
import subprocess
import argparse
import json
import sys
import os


def networkx_to_graphviz(g, src, dsts, label="partitions"):
    """Convert `networkx` graph `g` to `graphviz.Digraph`.

    @type g: `networkx.Graph` or `networkx.DiGraph`
    @rtype: `graphviz.Digraph`
    """
    if g.is_directed():
        h = gv.Digraph()
    else:
        h = gv.Graph()
    for u, d in g.nodes(data=True):
        # u = u.split(",")[0]
        if u.split(",")[0] == src:
            h.node(str(u.replace(":", " ")), fillcolor="red", style="filled")
        elif u.split(",")[0] in dsts:
            h.node(str(u.replace(":", " ")), fillcolor="green", style="filled")
        h.node(str(u.replace(":", " ")))
    for u, v, d in g.edges(data=True):
        # print('edge', u, v, d)
        h.edge(str(u.replace(":", " ")), str(v.replace(":", " ")), label=str(d[label]))
    return h


def N_dijkstra(src, dsts, G, num_partitions):
    h = G.copy()
    h.remove_edges_from(list(h.in_edges(source_node)) + list(nx.selfloop_edges(h)))
    bc_topology = BroadCastTopology(src, dsts, num_partitions)

    for dst in dsts:
        path = nx.dijkstra_path(h, src, dst, weight="cost")
        for i in range(0, len(path) - 1):
            s, t = path[i], path[i + 1]
            for j in range(bc_topology.num_partitions):
                bc_topology.append_dst_partition_path(dst, j, [s, t, G[s][t]])

    return bc_topology


def N_direct(src, dsts, G, num_partitions):
    bc_topology = BroadCastTopology(src, dsts, num_partitions)

    for dst in dsts:
        edge = G[src][dst]
        for j in range(bc_topology.num_partitions):
            bc_topology.set_dst_partition_paths(dst, j, [[src, dst, edge]])

    return bc_topology


def MDST(src, dsts, G, num_partitions):
    # Construct MDST path
    h = G.copy()
    h.remove_edges_from(list(h.in_edges(src)) + list(nx.selfloop_edges(h)))
    DST_graph = Edmonds(h.subgraph([src] + dsts))
    opt_DST = DST_graph.find_optimum(attr="cost", kind="min", preserve_attrs=True, style="arborescence")
    bc_topology = BroadCastTopology(src, dsts, num_partitions)

    # Construct MDST graph
    MDST_graph = nx.DiGraph()
    for edge in list(opt_DST.edges()):
        s, d = edge[0], edge[1]
        MDST_graph.add_edge(s, d, **G[s][d])

    return append_src_dst_paths(src, dsts, MDST_graph, bc_topology), MDST_graph


def MULTI_MDST(src, dsts, G, num_partitions):
    # Construct MDST path based on original graph
    h = G.copy()
    MDST_graphs = []
    while len(list(h.edges())) > 0:
        _, MDST_graph = MDST(src, dsts, h, 1)
        print("MDST graph: ", MDST_graph.edges.data())
        MDST_graphs.append(MDST_graph)
        h.remove_edges_from(list(MDST_graph.edges()))

    print("Number of MDSTs: ", len(MDST_graphs))


def Min_Steiner_Tree(src, dsts, G, num_partitions, hop_limit=3000):
    # TODO: need to download the dependencies first
    source_v, dest_v = src, dsts

    h = G.copy()
    h.remove_edges_from(list(h.in_edges(source_v)) + list(nx.selfloop_edges(h)))

    nodes, edges = list(h.nodes), list(h.edges)
    num_nodes, num_edges = len(nodes), len(edges)
    id_to_name = {nodes.index(n) + 1: n for n in nodes}

    config_loc = "write.set"
    write_loc = "test.stplog"
    param_loc = "test.stp"

    with open(config_loc, "w") as f:
        f.write('stp/logfile = "use_probname"')
        f.close()

    command = " ~/Documents/Packages/scipoptsuite-8.0.2/build/bin/applications/scipstp "  # TODO: then replace this with where scipstp is installed
    command += f"-f {param_loc} -s {config_loc} -l {write_loc}"

    def construct_stp():
        section_begin = '33D32945 STP File, STP Format Version 1.0\n\nSECTION Comment\nName "Relay: cloud regions"\nCreator "S. Liu"\n'
        section_begin += f'Remark "Cloud region problem adapted from relay"\nEND\n\nSECTION Graph\n'
        section_begin += f"Nodes {num_nodes}\nEdges {num_edges}\nHopLimit {hop_limit}\n"

        Edge_info = []
        cnt = 0
        for edge in edges:
            s, d = nodes.index(edge[0]) + 1, nodes.index(edge[1]) + 1
            cost = h[edge[0]][edge[1]]["cost"]
            cnt += 1
            Edge_info.append(f"A {s} {d} {cost}\n")
            if cnt == num_edges:
                Edge_info.append("END\n")

        s = nodes.index(source_v) + 1
        v = [nodes.index(i) + 1 for i in dest_v]
        terminal_info = [f"T {i}\n" for i in v]
        terminal_info.append("END\n\nEOF")
        section_terminal = f"""\nSECTION Terminals\nRoot {s}\nTerminals {len(dest_v)}\n"""

        with open(param_loc, "w") as f:
            f.write(section_begin)
            for edge in Edge_info:
                f.write(edge.lstrip())
            f.write(section_terminal)
            for t in terminal_info:
                f.write(t)
            f.close()
        return

    def read_result(loc):
        di_stree_graph = nx.DiGraph()
        with open(loc, "r") as f:
            lines = f.readlines()
            for line in lines:
                if line.startswith("E") and len(line.split()) == 3:
                    l = line.split()
                    src_r, dst_r = id_to_name[int(l[1])], id_to_name[int(l[2])]
                    di_stree_graph.add_edge(src_r, dst_r, **G[src_r][dst_r])

        # overlays = [node for node in di_stree_graph.nodes if node not in [source_v]+dest_v]
        return di_stree_graph

    construct_stp()  # construct problem to a file
    process = subprocess.Popen(command, shell=True)  # run the steiner tree solver
    process.wait()
    solution_graph = read_result(loc=write_loc)

    print(
        f"Number of overlays added: {len(solution_graph.nodes) - (1 + len(dsts))}, {[node for node in solution_graph.nodes if node not in [src]+dsts]}"
    )
    bc_topology = BroadCastTopology(src, dsts, num_partitions)

    os.remove(config_loc)
    os.remove(write_loc)
    os.remove(param_loc)

    return append_src_dst_paths(src, dsts, solution_graph, bc_topology)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("jsonfile", help="input json file")
    parser.add_argument("-a", "--algo", type=str, nargs="?", const="")
    parser.add_argument("-n", "--num-vms", type=int, nargs="?", const="")
    args = vars(parser.parse_args())
    print("Args:", args)

    print(f"\n==============> Baseline generation")
    with open(args["jsonfile"], "r") as f:
        config_name = args["jsonfile"].split("/")[1].split(".")[0]
        config = json.loads(f.read())

    # generate default graph with node and edge info
    # G = make_nx_graph(throughput_path="profiles/aws_throughput_11_8.csv")
    G = make_nx_graph(num_vms=int(args["num_vms"]))

    # src, dst
    source_node = config["source_node"]
    terminal_nodes = config["dest_nodes"]

    print(f"source_v = '{source_node}'")
    print(f"dest_v = {terminal_nodes}")
    # baseline path generations
    if args["algo"] is None:
        algorithms = [
            "Ndirect",
            "MDST",
            # "HST",
        ]
    else:
        algorithms = [args["algo"]]
    print(f"Algorithms: {algorithms}\n")

    directory = f"paths/{config_name}"
    if not os.path.exists(directory):
        Path(directory).mkdir(parents=True, exist_ok=True)

    num_partitions = config["num_partitions"]
    for algo in algorithms:
        outf = f"{directory}/{algo}.json"
        print(f"Generate {algo} paths into {outf}")
        if algo == "Ndirect":
            bc_t = N_direct(source_node, terminal_nodes, G, num_partitions)
        elif algo == "MDST":
            bc_t, mdgraph = MDST(source_node, terminal_nodes, G, num_partitions)
        elif algo == "MULTI-MDST":
            bc_t = MULTI_MDST(source_node, terminal_nodes, G, num_partitions)
        elif algo == "HST":
            bc_t = Min_Steiner_Tree(source_node, terminal_nodes, G, num_partitions)
        elif algo == "Ndijkstra":
            bc_t = N_dijkstra(source_node, terminal_nodes, G, num_partitions)
        else:
            raise NotImplementedError(algo)

        bc_t.set_num_partitions(config["num_partitions"])  # simple baseline, don't care about partitions, simply set it

        with open(outf, "w") as outfile:
            outfile.write(
                json.dumps(
                    {
                        "algo": algo,
                        "source_node": bc_t.src,
                        "terminal_nodes": bc_t.dsts,
                        "num_partitions": bc_t.num_partitions,
                        "generated_path": bc_t.paths,
                    }
                )
            )

    # put the evaluate logic here
    input_dir = "paths"  # input paths
    output_dir = "evals"  # eval results
    with open(sys.argv[1], "r") as f:
        config_name = sys.argv[1].split("/")[1].split(".")[0]
        config = json.loads(f.read())

    input_dir += f"/{config_name}"
    output_dir += f"/{config_name}"
    if not os.path.exists(output_dir):
        Path(output_dir).mkdir(parents=True, exist_ok=True)

    simulator = BCSimulator(int(args["num_vms"]), output_dir)
    for algo in algorithms:
        path = f"{input_dir}/{algo}.json"
        simulator.evaluate_path(path, config)  # path of algorithm output, basic config to evaluate

    # nx.draw(mdgraph, with_labels=True)
    # plt.show()
    # h = networkx_to_graphviz(mdgraph, source_node, terminal_nodes)
    # h.render(filename="Ndirect")