<jupyter_start><jupyter_text>Forwarding LatencyIt is being run from a desktop computer on the UC Berkeley network w/ avg ping latency of 5.03ms to a single broker running on EC2 running in standalone mode.  Pairs10 pairs of pub/sub that share a query. Publishers at a rate of 10 msg/sec, runtime of 20 minutes. Each query has a unique key and value<jupyter_code>FILENAME="data/10_pub_sub_pairs.csv"
df = parse_and_plot(FILENAME)
df.describe()<jupyter_output><empty_output><jupyter_text>---100 pairs of pub/sub that share a query. Publishers at a rate of 10 msg/sec, runtime of 20 minutes. Each query has a unique key and value<jupyter_code>filename="data/forwarding_latency_100pub_to_1sub.csv"
df = pd.read_csv(filename)
df /= float(1e6)
df.plot(kind='line', figsize=(15,8))
filename="data/1_client_10_pub.csv"
df1 = pd.read_csv(filename, header=None)
df1 /= float(1e6)
df1.plot(style='-',figsize=(15,8))
print df1.quantile(q=.99)
df1.describe()<jupyter_output>0    4.98007
dtype: float64<jupyter_text>We wil run `forwrdingLatencyNto1` for clients = 1, 10, 100. In the N > 1 case, we average the N clients together to get a single timeseries for that case, then we will plot them all together<jupyter_code>filename="data/10_client_100_pub.csv"
df2 = pd.read_csv(filename, header=None)
df2 /= float(1e6)
df2[0] = df2.mean(axis=1)
print df2[0].quantile(q=.99)
df2[0].plot(style='-',figsize=(15,8))
#df2.describe()

df1[1] = df2[0]
filename="data/100_client_1000_pub.csv"
df3 = pd.read_csv(filename, header=None)
df3 /= float(1e6)
df3[0] = df3.mean(axis=1)
df3.plot(style='-',figsize=(15,8), legend=False)
print df3[0].quantile(q=.99)
df1[2] = df3[0]
snp = df1[df1 > 3.5].copy()
snp.columns = ['1:10 sub:pub','10:100 sub:pub', '100:1000 sub:pub']
axs = snp.plot(figsize=(15,10), subplots=True)
for ax in axs:
    ax.set_ylabel("Latency (ms)")
    ax.set_xlabel("Sample (5msg/sec)")
    ax.set_yscale('log')
df1<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14352090862157976, 'fraction_numerical': 0.07176045431078988, 'mean_word_length': 5.8, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 14, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '207423', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 835, 'n_tokens_neox': 764, 'n_words': 189, 'path': 'evaluation/single_node/Single Node Benchmark.ipynb', 'repo_name': 'gtfierro/cs262-project', 'size': '1937'}