mirror of https://github.com/vacp2p/research.git
473 lines
19 KiB
Python
473 lines
19 KiB
Python
# !!! THIS IS WIP (analyze the code structure at your own risk ^.^')
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# the scope of this is still undefined; we want to avoid premature generalization
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# - todo: separate the part on latency
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# based on ../whisper_scalability/whisper.py
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import matplotlib.pyplot as plt
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import numpy as np
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import math
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import typer
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from enum import Enum, EnumMeta
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class networkType(Enum):
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NEWMANWATTSSTROGATZ = "newmanwattsstrogatz" # mesh, smallworld
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REGULAR = "regular" # libp2p
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GENNET="gennet"
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GENLOAD="wls"
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CONFIG="config"
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# Util and format functions
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#-----------------------------------------------------------
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class bcolors:
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HEADER = '\033[95m'
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OKBLUE = '\033[94m'
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OKGREEN = '\033[92m'
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WARNING = '\033[93m'
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FAIL = '\033[91m'
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ENDC = '\033[0m'
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BOLD = '\033[1m'
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UNDERLINE = '\033[4m'
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def sizeof_fmt(num):
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return "%.1f%s" % (num, "MB")
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def sizeof_fmt_kb(num):
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return "%.2f%s" % (num*1024, "KB")
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def magnitude_fmt(num):
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for x in ['','k','m']:
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if num < 1000:
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return "%2d%s" % (num, x)
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num /= 1000
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# Color format based on daily bandwidth usage
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# <10mb/d = good, <30mb/d ok, <100mb/d bad, 100mb/d+ fail.
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def load_color_prefix(load):
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if load < (10):
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color_level = bcolors.OKBLUE
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elif load < (30):
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color_level = bcolors.OKGREEN
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elif load < (100):
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color_level = bcolors.WARNING
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else:
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color_level = bcolors.FAIL
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return color_level
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def load_color_fmt(load, string):
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return load_color_prefix(load) + string + bcolors.ENDC
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def print_header(string):
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print(bcolors.HEADER + string + bcolors.ENDC + "\n")
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def print_assumptions(xs):
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print("Assumptions/Simplifications:")
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for x in xs:
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print(x)
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print("")
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def usage_str(load_users_fn, n_users):
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load = load_users_fn(n_users)
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return load_color_fmt(load, "For " + magnitude_fmt(n_users) + " users, receiving bandwidth is " + sizeof_fmt(load_users_fn(n_users)) + "/hour")
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def print_usage(load_users):
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print(usage_str(load_users, 100))
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print(usage_str(load_users, 100 * 100))
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print(usage_str(load_users, 100 * 100 * 100))
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def latency_str(latency_users_fn, n_users, degree):
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latency = latency_users_fn(n_users, degree)
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return load_color_fmt(latency, "For " + magnitude_fmt(n_users) + " the average latency is " + ("%.3f" % latency_users_fn(n_users, degree)) + " s")
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def print_latency(latency_users):
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print(latency_str(latency_users, 100, average_node_degree))
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print(latency_str(latency_users, 100 * 100, average_node_degree))
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print(latency_str(latency_users, 100 * 100 * 100, average_node_degree))
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def num_edges_dregular(num_nodes, degree):
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# we assume and even d; d-regular graphs with both where both n and d are odd don't exist
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return num_nodes * (degree/2)
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def avg_node_distance_upper_bound(n_users, degree):
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return math.log(n_users, degree)
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# Assumptions
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#-----------------------------------------------------------
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# Users sent messages at a constant rate
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# The network topology is a d-regular graph (gossipsub aims at achieving this).
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# general / topology
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average_node_degree = 6 # has to be even
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message_size = 0.002 # in MB (Mega Bytes)
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messages_sent_per_hour = 5 # ona a single pubsub topic / shard
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# gossip
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gossip_message_size = 0.00005 # 50Bytes in MB (see https://github.com/libp2p/specs/pull/413#discussion_r1018821589 )
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d_lazy = 6 # gossip out degree
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mcache_gossip = 3 # Number of history windows to use when emitting gossip (see https://github.com/libp2p/specs/blob/master/pubsub/gossipsub/gossipsub-v1.0.md)
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avg_ratio_gossip_replys = 0.01 # -> this is a wild guess! (todo: investigate)
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# multi shard
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avg_nodes_per_shard = 10000 # average number of nodes that a part of a single shard
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avg_shards_per_node = 3 # average number of shards a given node is part of
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# latency
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average_delay_per_hop = 0.1 #s
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# TODO: load case for status control messages (note: this also introduces messages by currently online, but not active users.)
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# TODO: spread in the latency distribution (the highest 10%ish of latencies might be too high)
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# Assumption strings (general/topology)
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a1 = "- A01. Message size (static): " + sizeof_fmt_kb(message_size)
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a2 = "- A02. Messages sent per node per hour (static) (assuming no spam; but also no rate limiting.): " + str(messages_sent_per_hour)
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a3 = "- A03. The network topology is a d-regular graph of degree (static): " + str(average_node_degree)
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a4 = "- A04. Messages outside of Waku Relay are not considered, e.g. store messages."
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a5 = "- A05. Messages are only sent once along an edge. (requires delays before sending)"
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a6 = "- A06. Messages are sent to all d-1 neighbours as soon as receiving a message (current operation)" # Thanks @Mmenduist
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a7 = "- A07. Single shard (i.e. single pubsub mesh)"
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a8 = "- A08. Multiple shards; mapping of content topic (multicast group) to shard is 1 to 1"
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a9 = "- A09. Max number of nodes per shard (static) " + str(avg_nodes_per_shard)
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a10 = "- A10. Number of shards a given node is part of (static) " + str(avg_shards_per_node)
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a11 = "- A11. Number of nodes in the network is variable.\n\
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These nodes are distributed evenly over " + str(avg_shards_per_node) + " shards.\n\
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Once all of these shards have " + str(avg_nodes_per_shard) + " nodes, new shards are spawned.\n\
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These new shards have no influcene on this model, because the nodes we look at are not part of these new shards."
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a12 = "- A12. Including 1:1 chat. Messages sent to a given user are sent into a 1:1 shard associated with that user's node.\n\
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Effectively, 1:1 chat adds a receive load corresponding to one additional shard a given node has to be part of."
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a13 = "- A13. 1:1 chat messages sent per node per hour (static): " + str(messages_sent_per_hour) # could introduce a separate variable here
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a14 = "- A14. 1:1 chat shards are filled one by one (not evenly distributed over the shards).\n\
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This acts as an upper bound and overestimates the 1:1 load for lower node counts."
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a15 = "- A15. Naive light node. Requests all messages in shards that have (large) 1:1 mapped multicast groups the light node is interested in."
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# Assumption strings (store)
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a21 = "- A21. Store nodes do not store duplicate messages."
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# Assumption strings (gossip)
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a31 = "- A21. Gossip is not considered."
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a32 = "- A32. Gossip message size (IHAVE/IWANT) (static):" + sizeof_fmt_kb(gossip_message_size)
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a33 = "- A33. Ratio of IHAVEs followed-up by an IWANT (incl. the actual requested message):" + str(avg_ratio_gossip_replys)
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# Assumption strings (delay)
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a41 = "- A41. Delay is calculated based on an upper bound of the expected distance."
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a42 = "- A42. Average delay per hop (static): " + str(average_delay_per_hop) + "s."
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# Cases Load Per Node
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#-----------------------------------------------------------
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# Case 1 :: singe shard, unique messages, store
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def load_case1(n_users):
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return message_size * messages_sent_per_hour * n_users
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def print_load_case1():
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print("")
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print_header("Load case 1 (store load; corresponds to received load per naive light node)")
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print_assumptions([a1, a2, a3, a4, a7, a21])
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print_usage(load_case1)
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print("")
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print("------------------------------------------------------------")
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# Case 2 :: single shard, (n*d)/2 messages
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def load_case2(n_users):
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return message_size * messages_sent_per_hour * num_edges_dregular(n_users, average_node_degree)
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def print_load_case2():
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print("")
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print_header("Load case 2 (received load per node)")
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print_assumptions([a1, a2, a3, a4, a5, a7, a31])
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print_usage(load_case2)
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print("")
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print("------------------------------------------------------------")
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# Case 3 :: single shard n*(d-1) messages
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def load_case3(n_users):
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return message_size * messages_sent_per_hour * n_users * (average_node_degree-1)
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def print_load_case3():
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print("")
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print_header("Load case 3 (received load per node)")
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print_assumptions([a1, a2, a3, a4, a6, a7, a31])
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print_usage(load_case3)
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print("")
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print("------------------------------------------------------------")
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# Case 4:single shard n*(d-1) messages, gossip
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def load_case4(n_users):
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messages_received_per_hour = messages_sent_per_hour * n_users * (average_node_degree-1) # see case 3
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messages_load = message_size * messages_received_per_hour
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num_ihave = messages_received_per_hour * d_lazy * mcache_gossip
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ihave_load = num_ihave * gossip_message_size
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gossip_response_load = (num_ihave * (gossip_message_size + message_size)) * avg_ratio_gossip_replys # reply load contains both an IWANT (from requester to sender), and the actual wanted message (from sender to requester)
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gossip_total = ihave_load + gossip_response_load
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return messages_load + gossip_total
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def print_load_case4():
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print("")
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print_header("Load case 4 (received load per node incl. gossip)")
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print_assumptions([a1, a2, a3, a4, a6, a7, a32, a33])
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print_usage(load_case4)
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print("")
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print("------------------------------------------------------------")
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# sharding case 1: multi shard, n*(d-1) messages, gossip
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def load_sharding_case1(n_users):
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load_per_node_per_shard = load_case4(np.minimum(n_users/3, avg_nodes_per_shard))
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return avg_shards_per_node * load_per_node_per_shard
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def print_load_sharding_case1():
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print("")
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print_header("load sharding case 1 (received load per node incl. gossip)")
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print_assumptions([a1, a2, a3, a4, a6, a8, a9, a10, a11, a32, a33])
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print_usage(load_sharding_case1)
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print("")
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print("------------------------------------------------------------")
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# sharding case 2: multi shard, n*(d-1) messages, gossip, 1:1 chat
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def load_sharding_case2(n_users):
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load_per_node_per_shard = load_case4(np.minimum(n_users/3, avg_nodes_per_shard))
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load_per_node_1to1_shard = load_case4(np.minimum(n_users, avg_nodes_per_shard))
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return (avg_shards_per_node * load_per_node_per_shard) + load_per_node_1to1_shard
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def print_load_sharding_case2():
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print("")
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print_header("load sharding case 2 (received load per node incl. gossip and 1:1 chat)")
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print_assumptions([a1, a2, a3, a4, a6, a8, a9, a10, a11, a12, a13, a14, a32, a33])
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print_usage(load_sharding_case2)
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print("")
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print("------------------------------------------------------------")
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# sharding case 3: multi shard, naive light node
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def load_sharding_case3(n_users):
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load_per_node_per_shard = load_case1(np.minimum(n_users/3, avg_nodes_per_shard))
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return avg_shards_per_node * load_per_node_per_shard
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def print_load_sharding_case3():
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print("")
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print_header("load sharding case 3 (received load naive light node.)")
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print_assumptions([a1, a2, a3, a4, a6, a8, a9, a10, a15, a32, a33])
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print_usage(load_sharding_case3)
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print("")
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print("------------------------------------------------------------")
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# Cases average latency
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#-----------------------------------------------------------
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def latency_case1(n_users, degree):
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return avg_node_distance_upper_bound(n_users, degree) * average_delay_per_hop
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def print_latency_case1():
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print("")
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print_header("Latency case 1 :: Topology: 6-regular graph. No gossip (note: gossip would help here)")
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print_assumptions([a3, a41, a42])
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print_latency(latency_case1)
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print("")
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print("------------------------------------------------------------")
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# Run cases
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#-----------------------------------------------------------
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# Print goals
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print("")
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print(bcolors.HEADER + "Waku relay theoretical model results (single shard and multi shard scenarios)." + bcolors.ENDC)
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print_load_case1()
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print_load_case2()
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print_load_case3()
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print_load_case4()
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print_load_sharding_case1()
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print_load_sharding_case2()
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print_load_sharding_case3()
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print_latency_case1()
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# Plot
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#-----------------------------------------------------------
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def plot_load():
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plt.clf() # clear current plot
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n_users = np.logspace(2, 6, num=5)
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print(n_users)
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plt.xlim(100, 10**4)
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plt.ylim(1, 10**4)
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plt.plot(n_users, load_case1(n_users), label='case 1', linewidth=4, linestyle='dashed')
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plt.plot(n_users, load_case2(n_users), label='case 2', linewidth=4, linestyle='dashed')
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plt.plot(n_users, load_case3(n_users), label='case 3', linewidth=4, linestyle='dashed')
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plt.plot(n_users, load_case4(n_users), label='case 4', linewidth=4, linestyle='dashed')
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case1 = "Case 1. top: 6-regular; store load (also: naive light node)"
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case2 = "Case 2. top: 6-regular; receive load per node, send delay to reduce duplicates"
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case3 = "Case 3. top: 6-regular; receive load per node, current operation"
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case4 = "Case 4. top: 6-regular; receive load per node, current operation, incl. gossip"
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plt.xlabel('number of users (log)')
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plt.ylabel('mb/hour (log)')
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plt.legend([case1, case2, case3, case4], loc='upper left')
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plt.xscale('log')
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plt.yscale('log')
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plt.axhspan(0, 10, facecolor='0.2', alpha=0.2, color='blue')
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plt.axhspan(10, 100, facecolor='0.2', alpha=0.2, color='green')
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plt.axhspan(100, 3000, facecolor='0.2', alpha=0.2, color='orange') # desktop nodes can handle this; load comparable to streaming (but both upload and download, and with spikes)
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plt.axhspan(3000, 10**6, facecolor='0.2', alpha=0.2, color='red')
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caption = "Plot 1: single shard."
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plt.figtext(0.5, 0.01, caption, wrap=True, horizontalalignment='center', fontsize=12)
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# plt.show()
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figure = plt.gcf() # get current figure
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figure.set_size_inches(16, 9)
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# plt.savefig("waku_scaling_plot.svg")
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plt.savefig("waku_scaling_single_shard_plot.png", dpi=300, orientation="landscape")
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def plot_load_sharding():
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plt.clf() # clear current plot
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n_users = np.logspace(2, 6, num=5)
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print(n_users)
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plt.xlim(100, 10**6)
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plt.ylim(1, 10**5)
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plt.plot(n_users, load_case1(n_users), label='sharding store', linewidth=4, linestyle='dashed') # same as without shardinig, has to store *all* messages
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plt.plot(n_users, load_sharding_case1(n_users), label='case 1', linewidth=4, linestyle='dashed')
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plt.plot(n_users, load_sharding_case2(n_users), label='case 2', linewidth=4, linestyle='dashed')
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plt.plot(n_users, load_sharding_case3(n_users), label='case 3', linewidth=4, linestyle='dashed')
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case_store = "Sharding store load; participate in all shards; top: 6-regular"
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case1 = "Sharding case 1. sharding: top: 6-regular; receive load per node, incl gossip"
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case2 = "Sharding case 2. sharding: top: 6-regular; receive load per node, incl gossip and 1:1 chat"
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case3 = "Sharding case 3. sharding: top: 6-regular; regular load for naive light node"
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plt.xlabel('number of users (log)')
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plt.ylabel('mb/hour (log)')
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plt.legend([case_store, case1, case2, case3], loc='upper left')
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plt.xscale('log')
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plt.yscale('log')
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plt.axhspan(0, 10, facecolor='0.2', alpha=0.2, color='blue')
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plt.axhspan(10, 100, facecolor='0.2', alpha=0.2, color='green')
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plt.axhspan(100, 3000, facecolor='0.2', alpha=0.2, color='orange') # desktop nodes can handle this; load comparable to streaming (but both upload and download, and with spikes)
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plt.axhspan(3000, 10**6, facecolor='0.2', alpha=0.2, color='red')
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caption = "Plot 2: multi shard."
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plt.figtext(0.5, 0.01, caption, wrap=True, horizontalalignment='center', fontsize=12)
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# plt.show()
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figure = plt.gcf() # get current figure
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figure.set_size_inches(16, 9)
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# plt.savefig("waku_scaling_plot.svg")
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plt.savefig("waku_scaling_multi_shard_plot.png", dpi=300, orientation="landscape")
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def _config_file_callback(ctx: typer.Context, param: typer.CallbackParam, cfile: str):
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if cfile:
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typer.echo(f"Loading config file: {os.path.basename(cfile)}")
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ctx.default_map = ctx.default_map or {} # Init the default map
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try:
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with open(cfile, 'r') as f: # Load config file
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conf = json.load(f)
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if "config" not in conf:
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print(
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f"Configuration not found in {cfile}. Skipping the analysis.")
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sys.exit(0)
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ctx.default_map.update(conf["network"]) # Merge config and default_map
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except Exception as ex:
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raise typer.BadParameter(str(ex))
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return cfile
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def _sanity_check(fname, keys, ftype="json"):
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if not fname.exists():
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log.error(f'The file "{fname}" does not exist')
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sys.exit(0)
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try:
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with open(fname, 'r') as f: # Load config file
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if ftype == "json": # Both batch and kurtosis use json
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conf = json.load(f)
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for key in keys:
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if key not in conf:
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log.error(f'The json {key} not found in {fname}')
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sys.exit(0)
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elif ftype == "yaml": # Shadow uses yaml
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log.error(f'YAML is not yet supported : {fname}')
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sys.exit(0)
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except Exception as ex:
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raise typer.BadParameter(str(ex))
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@app.command()
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|
def kurtosis(ctx: typer.Context, config_file: Path):
|
|
_sanity_check(fname, "json", [GENNET, GENLOAD])
|
|
|
|
@app.command()
|
|
def batch(ctx: typer.Context, batch_file: Path):
|
|
_sanity_check(fname, "json", [CONFIG])
|
|
|
|
@app.command()
|
|
def shadow(ctx: typer.Context, batch_file: Path):
|
|
_sanity_check(fname, "yaml", [])
|
|
|
|
@app.command()
|
|
def cli(ctx: typer.Context,
|
|
num_nodes: int = typer.Option(4,
|
|
help="Set the number of nodes"),
|
|
fanout: int = typer.Option(6,
|
|
help="Set the arity"),
|
|
network_type: networkType = typer.Option(networkType.REGULAR.value,
|
|
help="Set the network type"),
|
|
msg_size: float = typer.Option(2,
|
|
help="Set message size in KBytes"),
|
|
msgpsec: float = typer.Option(0.083,
|
|
help="Set message rate per second on a shard/topic"),
|
|
gossip_msg_size: float = typer.Option(0.05,
|
|
help="Set gossip message size in KBytes"),
|
|
cache: int = typer.Option(3,
|
|
help="Set gossip window size"),
|
|
gossip__to_reply_ratio: float = typer.Option(0.01,
|
|
help="Set the Gossip to reply ratio"),
|
|
nodes_per_shard: int = typer.Option(10000,
|
|
help="Set the number of nodes per shard/topic"),
|
|
shards_per_node: int = typer.Option(3,
|
|
help="Set the number of shards a node is part of"),
|
|
per_hop_delay: float = typer.Option(0.1,
|
|
help="Set the delay per hop")):
|
|
plot_load()
|
|
plot_load_sharding()
|
|
|
|
"""
|
|
# general / topology
|
|
average_node_degree = 6 # has to be even
|
|
message_size = 0.002 # in MB (Mega Bytes)
|
|
messages_sent_per_hour = 5 # ona a single pubsub topic / shard
|
|
|
|
# gossip
|
|
gossip_message_size = 0.00005 # 50Bytes in MB (see https://github.com/libp2p/specs/pull/413#discussion_r1018821589 )
|
|
d_lazy = 6 # gossip out degree
|
|
mcache_gossip = 3 # Number of history windows to use when emitting gossip (see https://github.com/libp2p/specs/blob/master/pubsub/gossipsub/gossipsub-v1.0.md)
|
|
avg_ratio_gossip_replys = 0.01 # -> this is a wild guess! (todo: investigate)
|
|
|
|
# multi shard
|
|
avg_nodes_per_shard = 10000 # average number of nodes that a part of a single shard
|
|
avg_shards_per_node = 3 # average number of shards a given node is part of
|
|
|
|
# latency
|
|
average_delay_per_hop = 0.1 #s
|
|
|
|
"""
|