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Creating #clients vs update interval relationship
Line chart of #clients vs update interval
2 charts, 1 for with quest, 1 for no quest
A script used for generating these 2 graphs:
all_data = [(#client, update_interval, spread/static, quest/noquest)]
for each run:
spread_or_static_str, with_quest_or_no_quest_str, num_clients = read(label.txt) # 'spread'/'static', 'quest'/'noquest', num_clients
for each csv:
moving_average(request_time)
moving_average(update_time)
for i in each iteration:
update_interval[i] = max([at(i) for each request_time]) + max([at(i) for each update_time])
all_data.append((max(update_interval), spread_or_static_str, with_quest_or_no_quest_str)
for quest_mode in ['spread', 'static']:
chart = chart()
chart.plot(x=[#client], y=[update_interval], line_label='spread')
chart.plot(x=[#client], y=[update_interval], line_label='static')
Use this chart to analyze the scalability, and compare static with spread.
Use the #request, #update, request time, update time trajectory charts for identifying bottleneck.
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generate large amount of data running server 400seconds
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properly label the data in the folder, with the comma separate format saved as label.txt
"spread"/"static", "quest"/"noquest", # clients -
script
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