mirror of https://github.com/vacp2p/research.git
557 lines
24 KiB
Python
557 lines
24 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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from pathlib import Path
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import sys
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import json
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import typer
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import logging as log
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from enum import Enum, EnumMeta
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# we do not currently use these - for future extensions
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class networkType(Enum):
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NEWMANWATTSSTROGATZ = "newmanwattsstrogatz" # mesh, smallworld
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REGULAR = "regular" # d_lazy
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#JSON/YAML keys: for consistency and avoid stupid bugs
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class Keys:
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GENNET = "gennet"
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GENLOAD = "wls"
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JSON = "json"
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YAML = "yaml"
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BATCH = "batch"
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RUNS = "runs"
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# Config holds the data for the individual runs. Every analysis instance is a Config instance
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class Config:
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'''
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def __init__(self): # the defaults
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self.num_nodes = 4 # number of wakunodes = 4
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self.fanout = 6 # 'average' node degree = 6
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self.network_type = networkType.REGULAR.value # regular nw: avg node degree is 'exact'
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self.msg_size = 0.002 # msg size in MBytes
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self.msgpsec = 0.00139 # msgs per sec in single pubsub topic/shard = 5 msgs/hr
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self.gossip_msg_size = 0.05 # gossip message size in KBytes = 50 bytes
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self.gossip_window_size = 3 # the history window for gossips = 3
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self.gossip2reply_ratio = 0.01 # fraction of gossips that elicit a reply = 0.01 (guess)
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self.nodes_per_shard = 10000 # avg number of nodes online and part of single shard
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self.shards_per_node = 3 # avg number of shards a wakunode participates
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self.per_hop_delay = 100 # avg delay per hop = 0.1 sec / 100 msec
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self.d_lazy = self.fanout # gossip degree = 6
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'''
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# We need 12 params to correctly instantiate Config. Set the defaults for the missing
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def __init__(self,
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num_nodes=4, fanout=6,
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network_type=networkType.REGULAR.value,
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msg_size=0.002, msgpsec=0.00139, per_hop_delay=100,
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gossip_msg_size=0.002, gossip_window_size=3, gossip2reply_ratio=0.01,
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nodes_per_shard=10000, shards_per_node=3):
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# set the current Config values
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self.num_nodes = num_nodes # number of nodes
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self.fanout = fanout # avg degree
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self.network_type = network_type # regular, small world etc
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self.msg_size = msg_size # avg message size in MBytes
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self.msgpsec = msgpsec # avg # of messages per sec
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self.per_hop_delay = per_hop_delay # per-hop delay = 0.01 sec
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self.gossip_msg_size = gossip_msg_size # avg gossip msg size in MBytes
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self.gossip_window_size = gossip_window_size # max gossip history window size
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self.gossip2reply_ratio = gossip2reply_ratio # fraction of replies/hits to a gossip msg
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self.nodes_per_shard = nodes_per_shard # max number of nodes per shard
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self.shards_per_node = shards_per_node # avg number of shards a node is part of
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# secondary parameters, derived from primary
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self.msgphr = msgpsec*60*60 # msgs per hour derived from msgpsec
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self.d_lazy = self.fanout # gossip degree = 6
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self.base_assumptions = ["a1", "a2", "a3", "a4"]
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# Assumption strings (general/topology)
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self.Assumptions = {
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"a1" : "- A01. Message size (static): " + sizeof_fmt_kb(self.msg_size),
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"a2" : "- A02. Messages sent per node per hour (static) (assuming no spam; but also no rate limiting.): " + str(self.msgphr),
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"a3" : "- A03. The network topology is a d-regular graph of degree (static): " + str(self.fanout),
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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(self.nodes_per_shard),
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"a10" : "- A10. Number of shards a given node is part of (static) " + str(self.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(self.shards_per_node) + " shards.\n\
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Once all of these shards have " + str(self.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(self.msgphr), # 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(self.gossip_msg_size),
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"a33" : "- A33. Ratio of IHAVEs followed-up by an IWANT (incl. the actual requested message):" + str(self.gossip2reply_ratio),
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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(self.per_hop_delay) + "s."
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}
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self.display()
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# display the Config
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def display(self):
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print( "Config = ", self.num_nodes, self.fanout, self.network_type,
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self.msg_size, self.msgpsec, self.msgphr,
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self.gossip_msg_size, self.gossip_window_size, self.gossip2reply_ratio,
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self.nodes_per_shard, self.shards_per_node, self.per_hop_delay, self.d_lazy)
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# Print assumptions : with a base set
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def print_assumptions1(self, xs):
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print("Assumptions/Simplifications:")
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alist = self.base_assumptions + xs
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for a in alist:
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if a in self.Assumptions:
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print(self.Assumptions[a])
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else:
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log.error(f'Unknown assumption: ' + a)
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sys.exit(0)
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print("")
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# Print assumptions: all
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def print_assumptions(self, xs):
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print("Assumptions/Simplifications:")
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for a in xs:
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if a in self.Assumptions:
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print(self.Assumptions[a])
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else:
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log.error(f'Unknown assumption: ' + a)
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sys.exit(0)
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print("")
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# Analysis performs the runs. It creates a Config object and runs the analysis on it
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class Analysis(Config):
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# accept variable number of parameters with missing values set to defaults
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def __init__(self, **kwargs):
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Config.__init__(self, **kwargs)
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# Case 1 :: singe shard, unique messages, store
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# sharding case 1: multi shard, n*(d-1) messages, gossip
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def load_sharding_case1(self, n_users):
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load_per_node_per_shard = self.load_case4(np.minimum(n_users/3, self.nodes_per_shard))
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return self.shards_per_node * load_per_node_per_shard
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def load_case1(self, n_users):
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return self.msg_size * self.msgphr * n_users
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def print_load_case1(self):
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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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self.print_assumptions1(["a7", "a21"])
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print_usage(self.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(self, n_users):
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return self.msg_size * self.msgphr * num_edges_dregular(n_users, self.fanout)
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def print_load_case2(self):
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print("")
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print_header("Load case 2 (received load per node)")
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self.print_assumptions1(["a5", "a7", "a31"])
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print_usage(self.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(self, n_users):
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return self.msg_size * self.msgphr * n_users * (self.fanout-1)
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def print_load_case3(self):
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print("")
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print_header("Load case 3 (received load per node)")
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self.print_assumptions1(["a6", "a7", "a31"])
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print_usage(self.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(self, n_users):
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messages_received_per_hour = self.msgphr * n_users * (self.fanout-1) # see case 3
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messages_load = self.msg_size * messages_received_per_hour
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num_ihave = messages_received_per_hour * self.d_lazy * self.gossip_window_size
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ihave_load = num_ihave * self.gossip_msg_size
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gossip_response_load = (num_ihave * (self.gossip_msg_size + self.msg_size)) * self.gossip2reply_ratio # 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(self):
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print("")
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print_header("Load case 4 (received load per node incl. gossip)")
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self.print_assumptions1(["a6", "a7", "a32", "a33"])
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print_usage(self.load_case4)
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print("")
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print("------------------------------------------------------------")
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# latency cases
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def latency_case1(self, n_users, degree):
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return avg_node_distance_upper_bound(n_users, degree) * self.per_hop_delay
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def print_latency_case1(self):
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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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self.print_assumptions(["a3", "a41", "a42"])
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print_latency(self.latency_case1, self.fanout)
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print("")
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print("------------------------------------------------------------")
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def print_load_sharding_case1(self):
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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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self.print_assumptions1(["a6", "a8", "a9", "a10", "a11", "a32", "a33"])
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print_usage(self.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(self, n_users):
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load_per_node_per_shard = self.load_case4(np.minimum(n_users/3, self.nodes_per_shard))
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load_per_node_1to1_shard = self.load_case4(np.minimum(n_users, self.nodes_per_shard))
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return (self.shards_per_node * load_per_node_per_shard) + load_per_node_1to1_shard
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def print_load_sharding_case2(self):
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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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self.print_assumptions1(["a6", "a8", "a9", "a10", "a11", "a12", "a13", "a14", "a32", "a33"])
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print_usage(self.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(self, n_users):
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load_per_node_per_shard = self.load_case1(np.minimum(n_users/3, self.nodes_per_shard))
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return self.shards_per_node * load_per_node_per_shard
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def print_load_sharding_case3(self):
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print("")
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print_header("load sharding case 3 (received load naive light node.)")
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self.print_assumptions1(["a6", "a8", "a9", "a10", "a15", "a32", "a33"])
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print_usage(self.load_sharding_case3)
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print("")
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print("------------------------------------------------------------")
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def run(self):
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self.print_load_case1()
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self.print_load_case2()
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self.print_load_case3()
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self.print_load_case4()
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self.print_latency_case1()
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self.print_load_sharding_case1()
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self.print_load_sharding_case2()
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self.print_load_sharding_case3()
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def plot_load(self):
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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(self):
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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 plot(self):
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self.plot_load()
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self.plot_load_sharding()
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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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# Util and format functions
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#-----------------------------------------------------------
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class IOFormats:
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self.HEADER = '\033[95m'
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self.OKBLUE = '\033[94m'
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self.OKGREEN = '\033[92m'
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self.WARNING = '\033[93m'
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self.FAIL = '\033[91m'
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self.ENDC = '\033[0m'
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self.BOLD = '\033[1m'
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self.UNDERLINE = '\033[4m'
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def sizeof_fmt(self, num):
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return "%.1f%s" % (num, "MB")
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def sizeof_fmt_kb(self, 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 = self.OKBLUE
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elif load < (30):
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color_level = self.OKGREEN
|
|
elif load < (100):
|
|
color_level = self.WARNING
|
|
else:
|
|
color_level = self.FAIL
|
|
return color_level
|
|
|
|
def load_color_fmt(load, string):
|
|
return load_color_prefix(load) + string + self.ENDC
|
|
|
|
|
|
def print_header(string):
|
|
print(self.HEADER + string + self.ENDC + "\n")
|
|
|
|
|
|
def usage_str(load_users_fn, n_users):
|
|
load = load_users_fn(n_users)
|
|
return load_color_fmt(load, "For " + magnitude_fmt(n_users) + " users, receiving bandwidth is " + sizeof_fmt(load_users_fn(n_users)) + "/hour")
|
|
|
|
|
|
def print_usage(load_users):
|
|
print(usage_str(load_users, 100))
|
|
print(usage_str(load_users, 100 * 100))
|
|
print(usage_str(load_users, 100 * 100 * 100))
|
|
|
|
|
|
def latency_str(latency_users_fn, n_users, degree):
|
|
latency = latency_users_fn(n_users, degree)
|
|
return load_color_fmt(latency, "For " + magnitude_fmt(n_users) + " the average latency is " + ("%.3f" % latency_users_fn(n_users, degree)) + " s")
|
|
|
|
|
|
def print_latency(latency_users, average_node_degree):
|
|
print(latency_str(latency_users, 100, average_node_degree))
|
|
print(latency_str(latency_users, 100 * 100, average_node_degree))
|
|
print(latency_str(latency_users, 100 * 100 * 100, average_node_degree))
|
|
|
|
|
|
# Print goals
|
|
def print_goal():
|
|
print("")
|
|
print(self.HEADER + "Waku relay theoretical model results (single shard and multi shard scenarios)." + self.ENDC)
|
|
|
|
|
|
def _sanity_check(fname, keys, ftype=Keys.JSON):
|
|
print(f'sanity check: {fname}, {keys}, {ftype}')
|
|
if not fname.exists():
|
|
log.error(f'The file "{fname}" does not exist')
|
|
sys.exit(0)
|
|
try:
|
|
with open(fname, 'r') as f: # Load config file
|
|
if ftype == Keys.JSON: # Both batch and kurtosis use json
|
|
json_conf = json.load(f)
|
|
for key in keys:
|
|
if key not in json_conf:
|
|
log.error(f'The json key "{key}" not found in {fname}')
|
|
sys.exit(0)
|
|
return json_conf
|
|
elif ftype == "yaml": # Shadow uses yaml
|
|
log.error(f'YAML is not yet supported : {fname}')
|
|
sys.exit(0)
|
|
#yaml_conf = json.load(f)
|
|
#return yaml_conf
|
|
except Exception as ex:
|
|
raise typer.BadParameter(str(ex))
|
|
log.debug(f'sanity check: All Ok')
|
|
|
|
app = typer.Typer()
|
|
|
|
@app.command()
|
|
def kurtosis(ctx: typer.Context, config_file: Path):
|
|
pretty_print = IOFormat()
|
|
print_goal()
|
|
json = _sanity_check(config_file, [Keys.GENNET, Keys.GENLOAD], Keys.JSON)
|
|
analysis = Analysis(
|
|
json["gennet"]["num_nodes"],
|
|
json["gennet"]["fanout"],
|
|
json["gennet"]["network_type"],
|
|
(json["wls"]["min_packet_size"] + json["wls"]["max_packet_size"])/2,
|
|
json["wls"]["message_rate"],
|
|
per_hop_delay=0.01) # pick up from kurtosis
|
|
analysis.run()
|
|
|
|
print(f'kurtosis: done')
|
|
|
|
@app.command()
|
|
def batch(ctx: typer.Context, batch_file: Path):
|
|
print_goal()
|
|
json = _sanity_check(batch_file, [Keys.BATCH], Keys.JSON)
|
|
runs = json[Keys.BATCH][Keys.RUNS]
|
|
for run in runs:
|
|
print(runs[run])
|
|
analysis = Analysis(**runs[run])
|
|
analysis.run()
|
|
print(f'batch: done')
|
|
|
|
@app.command()
|
|
def shadow(ctx: typer.Context, config_file: Path):
|
|
print_goal()
|
|
yaml = _sanity_check(config_file, [], Keys.YAML)
|
|
print("shadow: done {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"),
|
|
gossip_window_size: int = typer.Option(3,
|
|
help="Set gossip history window size"),
|
|
gossip2reply_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")):
|
|
|
|
analysis = Analysis(num_nodes, fanout, network_type,
|
|
msg_size, msgpsec, per_hop_delay,
|
|
**{"gossip_msg_size" : gossip_msg_size,
|
|
"gossip_window_size":gossip_window_size,
|
|
"gossip2reply_ratio":gossip2reply_ratio,
|
|
"nodes_per_shard":nodes_per_shard,
|
|
"shards_per_node":shards_per_node})
|
|
analysis.run()
|
|
print("cli: done")
|
|
|
|
if __name__ == "__main__":
|
|
app()
|
|
|
|
"""
|
|
# general / topology
|
|
average_node_degree = 6 # has to be even
|
|
message_size = 0.002 # in MB (Mega Bytes)
|
|
self.msgphr = 5 # ona a single pubsub topic / shard
|
|
|
|
# gossip
|
|
self.gossip_msg_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
|
|
"""
|