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https://github.com/logos-storage/logos-storage-research.git
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50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
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import numpy as np
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import matplotlib.pyplot as plt
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# Parameters
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N_values = [1e4, 1e5]
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# P buckets (members)
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P_buckets = {
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"small (P=10)": 10,
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"mid (P=250)": 250,
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"large (P=1000)": 1000
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}
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# Query: (bytes per query)
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def query_payload_bytes(N, P):
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return 14460.0 * np.log2(N) + 16.0 * (33.0 + 305.0 * P)
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# Query rate Q = (P / (7*86400)) * (0.05 + 0.20 / C)
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def Q_per_content(C, P):
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W = 7 * 86400.0
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return (P / W) * (0.05 + 0.20 / C)
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# Query bandwidth BW_query(C) = C * Q(C) * query_payload_bytes
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def BW_query_bytes_per_sec(C, P, N):
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Q = Q_per_content(C, P)
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return C * Q * query_payload_bytes(N, P)
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# Community age (H_weeks) and content counts (C)
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H_weeks = np.arange(1, 513)
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C_vals = H_weeks + 1
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# Plotting 1x2 grid
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fig, axs = plt.subplots(1, 2, figsize=(14, 5), sharex=True, sharey=True)
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axs = axs.flatten()
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for i, N in enumerate(N_values):
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ax = axs[i]
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for label, P in P_buckets.items():
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y = BW_query_bytes_per_sec(C_vals, P, N)
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ax.plot(H_weeks, y, label=label, linewidth=2)
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ax.set_xscale("log", base=2)
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ax.set_yscale("log", base=2)
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ax.set_xlabel("Community age H (weeks)")
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ax.set_ylabel("Query Bandwidth (bytes/sec)")
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ax.set_title(f"N={int(N):,}")
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ax.grid(True, linestyle="--", alpha=0.5)
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ax.legend()
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plt.tight_layout()
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plt.show()
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