mirror of https://github.com/EdgeVPNio/evio.git
230 lines
10 KiB
Python
230 lines
10 KiB
Python
# EdgeVPNio
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# Copyright 2020, University of Florida
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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# THE SOFTWARE.
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import math
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import random
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from .NetworkGraph import ConnectionEdge
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from .NetworkGraph import ConnEdgeAdjacenctList
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class GraphBuilder():
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"""
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Creates the adjacency list of connections edges from this node that are necessary to
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maintain the Topology
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"""
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def __init__(self, cfg, top=None):
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self.overlay_id = cfg["OverlayId"]
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self._node_id = cfg["NodeId"]
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self._peers = None
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# enforced is a list of peer ids that should always have a direct edge
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self._enforced_edges = cfg.get("EnforcedEdges", [])
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# only create edges from the enforced list
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self._manual_topo = cfg.get("ManualTopology", False)
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self._max_successors = int(cfg["MaxSuccessors"])
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# the number of symphony edges that shoulb be maintained
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self._max_ldl_cnt = int(cfg["MaxLongDistEdges"])
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self._max_ond = int(cfg["MaxOnDemandEdges"])
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# Currently active adjacency list, needed to minimize changes in chord selection
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self._nodes = []
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self._my_idx = 0
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self._top = top
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self._relink = False
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if self._manual_topo and not self._enforced_edges:
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self._top.log("LOG_WARNING", "Ad hoc topology specified but no peers are"
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"provided, config=%s", str(cfg))
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def _build_enforced(self, adj_list):
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for peer_id in self._enforced_edges:
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ce = ConnectionEdge(peer_id, edge_type="CETypeEnforced")
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adj_list.add_conn_edge(ce)
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def _get_successors(self):
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""" Generate a list of successor UIDs from the list of peers """
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successors = []
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num_peers = len(self._peers)
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num_nodes = len(self._nodes)
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successor_index = self._my_idx + 1
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num_succ = self._max_successors if (num_peers >= self._max_successors) else num_peers
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for _ in range(num_succ):
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successor_index %= num_nodes
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successors.append(self._nodes[successor_index])
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successor_index += 1
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return successors
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def _build_successors(self, adj_list, transition_adj_list):
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num_ideal_conn_succ = 0
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successors = self._get_successors()
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suc_ces = transition_adj_list.filter([("CETypeSuccessor", "CEStateConnected")])
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# add the ideal successors to the new adj list
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for peer_id in successors:
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if peer_id not in adj_list:
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adj_list[peer_id] = ConnectionEdge(peer_id, edge_type="CETypeSuccessor")
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if peer_id in suc_ces:
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# this is an ideal succ that was previously connected
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num_ideal_conn_succ += 1
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del suc_ces[peer_id]
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# do not remove the existing successor until the new one is connected
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for peer_id in suc_ces:
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# these are to be replaced when the ideal ones are in connected state
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if num_ideal_conn_succ < self._max_successors:
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# not an ideal successor but keep until better succ is connected
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adj_list[peer_id] = ConnectionEdge(peer_id, edge_type="CETypeSuccessor")
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num_ideal_conn_succ += 1
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else:
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break # consider selecting the best of these
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@staticmethod
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def symphony_prob_distribution(network_sz, samples):
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"""exp (log(n) * (rand() - 1.0))"""
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results = [None]*(samples)
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for i in range(0, samples):
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rnd_val = random.random()
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results[i] = math.exp(math.log10(network_sz) * (rnd_val - 1.0))
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return results
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def _get_long_dist_links(self, num_ldl):
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# Calculates long distance link candidates.
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long_dist_links = []
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net_sz = len(self._nodes)
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if net_sz > 1:
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num_ldl = min(num_ldl, net_sz)
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node_off = GraphBuilder.symphony_prob_distribution(net_sz, num_ldl)
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for i in node_off:
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idx = math.floor(net_sz*i)
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ldl_idx = (self._my_idx + idx) % net_sz
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long_dist_links.append(self._nodes[ldl_idx])
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return long_dist_links
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def _build_long_dist_links(self, adj_list, transition_adj_list):
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# Preserve existing incoming ldl
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# handled in net builder
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#ldlnks = transition_adj_list.edges_bytype(["CETypeILongDistance"])
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#for peer_id, ce in ldlnks.items():
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# if ce.edge_state in ("CEStateInitialized", "CEStateCreated", "CEStateConnected") and \
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# peer_id not in adj_list:
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# adj_list[peer_id] = ConnectionEdge(peer_id, ce.edge_id, ce.edge_type)
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# evaluate existing ldl
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ldlnks = {}
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if not self._relink:
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ldlnks = transition_adj_list.edges_bytype(["CETypeLongDistance"])
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num_existing_ldl = 0
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for peer_id, ce in ldlnks.items():
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if ce.edge_state in ["CEStateConnected"] and \
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peer_id not in adj_list and not self.is_too_close(ce.peer_id):
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adj_list[peer_id] = ConnectionEdge(peer_id, ce.edge_id, ce.edge_type)
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num_existing_ldl += 1
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if num_existing_ldl >= self._max_ldl_cnt:
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return
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num_ldl = self._max_ldl_cnt - num_existing_ldl
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ldl = self._get_long_dist_links(num_ldl)
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for peer_id in ldl:
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if peer_id not in adj_list:
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ce = ConnectionEdge(peer_id, edge_type="CETypeLongDistance")
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adj_list.add_conn_edge(ce)
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def _build_ondemand_links(self, adj_list, transition_adj_list, request_list):
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ond = {}
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# add existing on demand links
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existing = transition_adj_list.edges_bytype(["CETypeOnDemand"])
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for peer_id, ce in existing.items():
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if ce.edge_state in ("CEStateInitialized", "CEStatePreAuth", "CEStateAuthorized", \
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"CEStateCreated", "CEStateConnected") and peer_id not in adj_list:
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ond[peer_id] = ConnectionEdge(peer_id, ce.edge_id, ce.edge_type)
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task_rmv = []
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for task in request_list:
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peer_id = task["PeerId"]
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op = task["Operation"]
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if op == "ADD":
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task_rmv.append(task)
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if peer_id in self._peers and (peer_id not in adj_list or
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peer_id not in transition_adj_list):
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ce = ConnectionEdge(peer_id, edge_type="CETypeOnDemand")
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ond[peer_id] = ce
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elif op == "REMOVE":
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self._top.log("LOG_DEBUG", "Processing OND Removal, popping %s", peer_id)
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ond.pop(peer_id, None)
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if peer_id not in adj_list:
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# only clear the task after the tunnel has been removed by NetworkBuilder
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task_rmv.append(task)
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for peer_id in ond:
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if peer_id not in adj_list:
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adj_list[peer_id] = ond[peer_id]
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for task in task_rmv:
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request_list.remove(task)
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def build_adj_list(self, peers, transition_adj_list, request_list=None, relink=False):
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self._relink = relink
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self._prep(peers)
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if request_list is None:
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request_list = []
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adj_list = ConnEdgeAdjacenctList(self.overlay_id, self._node_id,
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self._max_successors, self._max_ldl_cnt, self._max_ond)
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self._build_enforced(adj_list)
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if not self._manual_topo:
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self._build_successors(adj_list, transition_adj_list)
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self._build_long_dist_links(adj_list, transition_adj_list)
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self._build_ondemand_links(adj_list, transition_adj_list, request_list)
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for _, ce in adj_list.conn_edges.items():
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assert ce.edge_state == "CEStateInitialized", "Invalid CE edge state, CE={}".format(ce)
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return adj_list
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def build_adj_list_ata(self,):
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"""
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Generates a new adjacency list from the list of available peers
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"""
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adj_list = ConnEdgeAdjacenctList(self.overlay_id, self._node_id,
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self._max_successors, self._max_ldl_cnt, self._max_ond)
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for peer_id in self._peers:
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if self._enforced_edges and peer_id in self._enforced_edges:
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ce = ConnectionEdge(peer_id)
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ce.edge_type = "CETypeEnforced"
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adj_list.add_conn_edge(ce)
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elif not self._manual_topo and self._node_id < peer_id:
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ce = ConnectionEdge(peer_id)
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ce.edge_type = "CETypeSuccessor"
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adj_list.add_conn_edge(ce)
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return adj_list
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def _distance(self, peer_id):
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dst = 0
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nsz = max(1, len(self._nodes))
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try:
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pr_i = self._nodes.index(peer_id)
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dst = (pr_i + nsz - self._my_idx) % nsz
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except ValueError as er:
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self._top.log("LOG_WARNING", "%s, continuing ...", str(er))
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return dst
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def _ideal_closest_distance(self):
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nsz = max(1, len(self._nodes))
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off = math.exp(-1 * math.log10(nsz))
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return math.floor(nsz * off)
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def is_too_close(self, peer_id):
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return self._distance(peer_id) < self._ideal_closest_distance()
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def _prep(self, peers):
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self._peers = peers
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self._nodes = list(self._peers)
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self._nodes.append(self._node_id)
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self._nodes.sort()
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self._my_idx = self._nodes.index(self._node_id)
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