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