データセンタトラフィックの推移

• Networking, Cisco Visual."Forecast and methodology, 2010-2015." White paper. (2011).
• Networking, Cisco Visual. "Cisco global cloud index: Forecast and methodology, 2016–2021." White paper. Cisco Public, San Jose 1 (2016).

を元に可視化。

table データ

import pandas as pd
import matplotlib.pyplot as plt

# Data (2010-2015)
data_2010_2015 = {
    "Year": [2010, 2011, 2012, 2013, 2014, 2015],
    "Data Center to User": [179, 262, 363, 489, 635, 815],
    "Data Center to Data Center": [75, 110, 150, 198, 252, 322],
    "Within Data Center": [887, 1279, 1727, 2261, 2857, 3618]
}

# Data (2016-2021)
data_2016_2021 = {
    "Year": [2016, 2017, 2018, 2019, 2020, 2021],
    "Data Center to User": [998, 1280, 1609, 2017, 2500, 3064],
    "Data Center to Data Center": [679, 976, 1347, 1746, 2245, 2796],
    "Within Data Center": [5143, 6831, 8601, 10362, 12371, 14695]
}

# Convert to DataFrame
df_2016_2021 = pd.DataFrame(data_2016_2021)
df_2010_2015 = pd.DataFrame(data_2010_2015)

# Merge the dataframes to have a complete dataset from 2010 to 2021
df = pd.concat([df_2010_2015, df_2016_2021]).reset_index(drop=True)

年毎の(1) データセンタとユーザー間、(2) データセンタ間、(3) データセンタ内（ラック内通信は除く）の推移。単位はEB。

Year	Data Center to User	Data Center to Data Center	Within Data Center
2010	179	75	887
2011	262	110	1279
2012	363	150	1727
2013	489	198	2261
2014	635	252	2857
2015	815	322	3618
2016	998	679	5143
2017	1280	976	6831
2018	1609	1347	8601
2019	2017	1746	10362
2020	2500	2245	12371
2021	3064	2796	14695

可視化

scale = 0.8
plt.figure(figsize=(7 * scale, 5 * scale))

# Plotting stacked bar chart
plt.bar(df['Year'], df['Within Data Center'], label='Within Data Center', color='skyblue')
# plt.bar(df['Year'], df['Within Data Center'], label='データセンタ内', color='skyblue')
plt.bar(df['Year'], df['Data Center to Data Center'], bottom=df['Within Data Center'],
        label='Data Center to Data Center', color='lightgreen')
plt.bar(df['Year'], df['Data Center to User'],
        bottom=df['Within Data Center'] + df['Data Center to Data Center'],
        label='Data Center to User', color='lightcoral')

# Adding titles and labels
plt.title('Global Data Center Traffic (2016-2021)', fontsize=18)
plt.xlabel('Year', fontsize=16)
plt.ylabel('Traffic (EB/Year)', fontsize=16)
plt.xticks(df['Year'])
plt.legend(fontsize=12)
plt.grid("--")

# Show the plot
plt.tight_layout()
plt.show()