66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
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from random import randint
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from constants import *
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def calculate_subnets(node_list, num_subnets, replication_factor):
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"""
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Calculate in which subnet(s) to place each node.
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This PoC does NOT require this to be analyzed,
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nor to find the best solution.
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Hence, we just use a simple model here:
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1. Iterate all nodes and place each node in the subsequent subnet
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2. If the subnet list can not be filled, start again from the top of the list
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3. If each subnet does NOT have at least up to REPLICATION_FACTOR nodes, then
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fill up the list with nodes up to the factor.
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NOTE: This might be incomplete and/or buggy, but should be sufficient for
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the purpose of the PoC.
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If however, you find a bug, please report.
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"""
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# key of dict is the subnet number
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subnets = {}
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for i, n in enumerate(node_list):
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idx = i % num_subnets
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# each key has an array, so multiple nodes can be filter
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# into a subnet
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if idx not in subnets:
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subnets[idx] = []
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subnets[idx].append(n)
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listlen = len(node_list)
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i = listlen
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# if there are less nodes than subnets
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while i < num_subnets:
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subnets[i] = []
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subnets[i].append(node_list[i % listlen])
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i += 1
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# if not each subnet has at least factor number of nodes, fill up
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if listlen < replication_factor * num_subnets:
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for subnet in subnets:
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last = subnets[subnet][len(subnets[subnet]) - 1].get_id()
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idx = -1
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# what is the last filled index of a subnet row
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for j, n in enumerate(node_list):
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if n.get_id() == last:
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idx = j + 1
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# fill up until factor
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while len(subnets[subnet]) < replication_factor:
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# wrap index if at end
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if idx > len(node_list) - 1:
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idx = 0
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# don't add same node multiple times
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if node_list[idx] in subnets[subnet]:
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idx += 1
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continue
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subnets[subnet].append(node_list[idx])
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idx += 1
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return subnets
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