Added Project

README.md

-# Web Server Simulation
+# Source Code File Structure
 
+Main Folder
+    
+    ├── Assignment 2 data - 1000 RR.csv
+    ├── Assignment 2 data - FCFS 1000 data.csv
+    ├── Assignment 2 data - FCFS comparision.csv
+    ├── Assignment 2 data - FCFS Comparison Data.csv
+    ├── Assignment 2 data - Goodput Badput B.csv
+    ├── Assignment 2 data - Measurement Closed.csv
+    ├── Assignment 2 data - Round Robin Comparison.csv
+    ├── Assignment 2 data - RR 0.01.csv
+    ├── Assignment 2 data - rr1.csv
+    ├── Assignment 2 data - RR comparision.csv
+    ├── Assignment 2 Simulation  Slides.pdf
+    ├── graph_plot_script.py
+    ├── infile.txt
+    ├── README.md
+    ├── Report.csv
+    ├── Simulation.
+    ├── Simulation.cpp
+    └── Trace.txt
+
+# Instructions of Compilation and Execution of Code
+
+Compile the code using
+
+    g++ Simulation.cpp -o Simulation
+
+Execute the code using
+
+    ./Simulation
+
+# Execution Instructions
+
+The program accepts following inputs
+
+        - Action
+            - 1: Single Simulation Run
+            - 2: Metric Calculation
+        - Mean Service Time (Non-Negative Real Number)
+        - Mean TimeOut Time (Non-Negative Real Number)
+        - Service Time Distribution (Integer)
+            - 1: Exponential
+            - 2: Uniform
+            - 3: Constant
+        - TimeOut Time Distribution (Integer)
+            - 1: Exponential
+            - 2: Uniform
+            - 3: Constant
+        - Number of Users (Positive Integer)
+        - Maximum Requests Per User (Positive Integer)
+
+There are two ways to give input
+    
+    - From Terminal: Execute the above given commands and give inputs wherever asked.
+    - From File: Put the inputs in a file names "infile.txt" in the same order mentioned above and every input on different line.
+
+# Trace and Output
+
+The program produces the trace whenever global time reaches some next event time. The trace is generated only when program is executed in single simulation mode. The trace is outputed on terminal as well as in a file called Trace.txt. The trace contains following data in every row in same order.
+    
+    - Simulation Timer
+    - All Core Status: The status of all the 4 cores whether they are idle or doing something
+    - Server Buffer Top Element: The top element in the server queue
+    - Next Event Type: The next event that is going to be processed
+    - Next Event Time: The time when next event is going to be processed.
+
+# Metrics Calculation
+
+To calculate the metric, select Metirc Calculation mode. Give maximum number of users till till which you want to calculate metrics. The program will generate the metrics while incrementing number of users by 10 every time. The metric data is stored in a file named "Report.csv".
+To generate the required Graphs run graph_plot_script.py.
+To generate the metric results from given csv's uncomment line 238 and run the run graph_plot_script.py script.
\ No newline at end of file

data/Assignment 2 data - 1000 RR.csv

+noOfUsers,minRes,maxRes,meanRes,core1,core2,core3,core4,requestDrop,badput,goodput,throughput,cpu
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\ No newline at end of file

data/Assignment 2 data - FCFS Comparison Data.csv

+noOfUsers,minRes,maxRes,meanRes,core1,core2,core3,core4,requestDrop,badput,goodput,throughput,cpu
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\ No newline at end of file

data/Assignment 2 data - FCFS comparision.csv

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\ No newline at end of file

data/Assignment 2 data - Goodput Badput B.csv

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\ No newline at end of file

data/Assignment 2 data - Measurement Closed.csv

+file,total,cpu,meanRes,noOfUsers,load Duration,,throughput,,error-file,error_abort,error_abort_max_conn_retries,error_connect_emfile,error_unknown_msg,fraction of packets dropped,users,success
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\ No newline at end of file

data/Assignment 2 data - RR 0.01.csv

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data/Assignment 2 data - RR comparision.csv

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data/Assignment 2 data - Round Robin Comparison.csv

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+270,9.74056,9.91975,9.83805,0.932769,0.914583,0.912362,0.911598,0,0,15.5126,15.5126,0.917828
+280,10.1504,10.5092,10.3649,0.933103,0.914584,0.913064,0.911764,0,0,15.5999,15.5999,0.91812875
+290,10.8949,11.0321,10.969,0.933212,0.914886,0.912982,0.912071,0,0,15.6247,15.6247,0.91828775
+300,11.438,11.6559,11.5238,0.933188,0.91637,0.914104,0.913352,0,0,15.6885,15.6885,0.9192535
\ No newline at end of file

data/Assignment 2 data - rr1.csv

+noOfUsers,minRes,maxRes,meanRes,core1,core2,core3,core4,requestDrop,badput,goodput,throughput,cpu
+10,0.331164,0.349904,0.341353,0.8294,0.108816,0.0203268,0.00220854,0,0,1.21197,1.21197,0.240187835
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+70,0.339595,0.348471,0.343082,0.82491,0.698093,0.590441,0.478914,0,0,8.99482,8.99482,0.6480895
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+120,0.339446,0.345595,0.342848,0.882901,0.823495,0.789425,0.741354,0,0,15.3336,15.3336,0.80929375
+130,0.338536,0.344353,0.342194,0.889673,0.837154,0.810443,0.772459,0,0,16.6593,16.6593,0.82743225
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+150,0.339766,0.345959,0.342974,0.892353,0.8487,0.830748,0.805982,0,0,19.166,19.166,0.84444575
+160,0.339187,0.346082,0.342243,0.896581,0.851777,0.839339,0.822997,0,0,20.4821,20.4821,0.8526735
+170,0.342526,0.346561,0.343727,0.897119,0.855472,0.841982,0.830271,0,0,21.6988,21.6988,0.856211
+180,0.340844,0.345022,0.343087,0.892534,0.858295,0.848889,0.84062,0,0,22.9893,22.9893,0.8600845
+190,0.337827,0.345771,0.343055,0.893742,0.855762,0.851146,0.841829,0,0,24.2732,24.2732,0.86061975
+200,0.345355,0.348146,0.346728,0.886899,0.853701,0.850069,0.845125,0,0,25.4689,25.4689,0.8589485
+210,0.344173,0.345722,0.344931,0.887218,0.853319,0.846856,0.843129,0,0,26.7416,26.7416,0.8576305
+220,0.343422,0.348218,0.345193,0.886513,0.854233,0.851153,0.850003,0,0,28.0426,28.0426,0.8604755
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+240,0.343844,0.349983,0.346442,0.880755,0.848573,0.848873,0.848001,0,0,30.5853,30.5853,0.8565505
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+290,0.377501,0.383402,0.380712,0.866379,0.836126,0.828919,0.828966,0,0,36.7785,36.7785,0.8400975
+300,0.388591,0.396877,0.392157,0.864481,0.829968,0.826163,0.825118,0,0,37.8338,37.8338,0.8364325
+310,0.406685,0.422262,0.412446,0.857719,0.83147,0.822266,0.822842,0,0,38.9851,38.9851,0.83357425
+320,0.439833,0.449136,0.444656,0.856996,0.822249,0.81837,0.818847,0,0,40.1307,40.1307,0.8291155
+330,0.468986,0.489971,0.480941,0.855182,0.820864,0.817072,0.815335,0,0,41.2252,41.2252,0.82711325
+340,0.540266,0.565217,0.555674,0.852015,0.818024,0.816181,0.809744,0,0,42.0088,42.0088,0.823991
+350,0.628869,0.659124,0.642609,0.852438,0.817946,0.810052,0.808141,0,0,42.7151,42.7151,0.82214425
+360,0.716375,0.791033,0.755661,0.852501,0.816059,0.814527,0.809956,0,0,43.3638,43.3638,0.82326075
+370,0.891385,1.00135,0.944378,0.852983,0.817817,0.81163,0.807202,0,0,43.5797,43.5797,0.822408
+380,1.08332,1.14854,1.12542,0.850216,0.818385,0.813095,0.809751,0,0,43.7485,43.7485,0.82286175
+390,1.31254,1.37885,1.35861,0.849594,0.819571,0.816517,0.813334,0,0,43.9171,43.9171,0.824754
+400,1.5285,1.58084,1.54199,0.848962,0.817839,0.813345,0.809891,0,0,43.9323,43.9323,0.82250925
+410,1.76663,1.79576,1.7778,0.848885,0.817244,0.812589,0.812055,0,0,43.9168,43.9168,0.82269325
+420,1.96212,1.99698,1.98289,0.85083,0.818138,0.815677,0.811839,0,0,43.9716,43.9716,0.824121
+430,2.12967,2.24223,2.19238,0.851493,0.820371,0.817364,0.812649,0,0,44.1138,44.1138,0.82546925
+440,2.37678,2.45781,2.40383,0.852015,0.82064,0.820403,0.813868,0,0,44.1881,44.1881,0.8267315
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+480,3.1702,3.26708,3.21937,0.85135,0.825847,0.821963,0.817645,0,0,44.5159,44.5159,0.82920125
+490,3.42577,3.45963,3.43992,0.849883,0.82379,0.82028,0.819981,0,0,44.4873,44.4873,0.8284835
+500,3.61425,3.70775,3.65185,0.850673,0.820996,0.822104,0.820308,0,0,44.4841,44.4841,0.82852025
+510,3.85026,3.91563,3.87855,0.847096,0.825682,0.823898,0.819009,0,0,44.5273,44.5273,0.82892125
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+550,4.69821,4.76522,4.72223,0.850984,0.829298,0.823543,0.821968,0,0,44.7183,44.7183,0.83144825
+560,4.92432,4.96603,4.94664,0.850097,0.827008,0.825809,0.822471,0,0,44.686,44.686,0.83134625
+570,5.06744,5.20269,5.1357,0.848434,0.828909,0.825656,0.824562,0,0,44.8402,44.8402,0.83189025
+580,5.34275,5.37154,5.35685,0.851708,0.826936,0.826911,0.82462,0,0,44.8293,44.8293,0.83254375
+590,5.53235,5.608,5.56993,0.851054,0.828584,0.824579,0.823736,0,0,44.805,44.805,0.83198825
+600,5.76909,5.78265,5.77526,0.850576,0.829012,0.825794,0.825796,0,0,44.8511,44.8511,0.8327945
+610,5.93307,6.01501,5.96784,0.8485,0.829005,0.825828,0.824577,0,0,44.9115,44.9115,0.8319775
+620,6.14874,6.2777,6.2092,0.848696,0.829721,0.825368,0.824404,0,0,44.9025,44.9025,0.83204725
+630,6.35958,6.39222,6.38262,0.850573,0.831985,0.828645,0.825605,0,0,45.0336,45.0336,0.834202
+640,6.51612,6.69198,6.60985,0.850657,0.831188,0.827445,0.827042,0,0,45.0303,45.0303,0.834083
+650,6.80224,6.88721,6.85434,0.849429,0.83025,0.829359,0.825487,0,0,44.9667,44.9667,0.83363125
+660,6.96121,7.06196,7.02251,0.850571,0.831286,0.829823,0.827145,0,0,45.101,45.101,0.83470625
+670,7.19611,7.29304,7.25317,0.848628,0.832202,0.829153,0.827353,0,0,45.067,45.067,0.834334
+680,7.43914,7.51009,7.476,0.850737,0.833243,0.827299,0.827526,0,0,45.0552,45.0552,0.83470125
+690,7.62116,7.73564,7.69217,0.850242,0.830963,0.830109,0.825145,0,0,45.0611,45.0611,0.83411475
+700,7.83906,7.96615,7.89499,0.852903,0.833123,0.829022,0.827633,0,0,45.0956,45.0956,0.83567025
+710,8.06023,8.12791,8.0893,0.850609,0.832302,0.830258,0.830486,0,0,45.1687,45.1687,0.83591375
+720,8.28495,8.38652,8.32418,0.848541,0.832249,0.831046,0.829942,0,0,45.1726,45.1726,0.8354445
+730,8.44445,8.56713,8.51277,0.850902,0.831878,0.829177,0.82873,0,0,45.1683,45.1683,0.83517175
+740,8.68436,8.75913,8.72369,0.849093,0.832208,0.829952,0.828524,0,0,45.1702,45.1702,0.83494425
+750,8.90066,9.00277,8.93522,0.851065,0.833227,0.829772,0.829943,0,0,45.2195,45.2195,0.83600175
+760,9.11006,9.25279,9.17384,0.850443,0.833797,0.830054,0.829234,0,0,45.1909,45.1909,0.835882
+770,9.31794,9.42266,9.36007,0.851151,0.833739,0.83157,0.832147,0,0,45.31,45.31,0.83715175
+780,9.54661,9.62364,9.58836,0.850404,0.832919,0.830917,0.830543,0,0,45.3039,45.3039,0.83619575
+790,9.6577,9.84534,9.78069,0.848216,0.834452,0.832427,0.8336,3.6,0,45.2992,45.2992,0.83717375
+800,9.76928,9.94159,9.84459,0.852565,0.835374,0.833668,0.831906,9.8,0,45.4895,45.4895,0.83837825
+810,9.71959,10.0356,9.93597,0.852767,0.834862,0.833243,0.830671,14.6,0,45.4546,45.4546,0.83788575
+820,9.62781,10.0572,9.8612,0.850158,0.835415,0.832402,0.831033,34.2,0,45.3208,45.3208,0.837252
+830,9.78288,9.96986,9.85838,0.849175,0.835798,0.833704,0.831155,44.4,0,45.3895,45.3895,0.837458
\ No newline at end of file

graph_plot_script.py

+import matplotlib.pyplot as plt
+import pandas as pd
+# from mplfinance.original_flavor import candlestick_ohlc
+# from mplfinance import candlestick_ohlc
+
+figure_count=0
+
+def get_figure_count():
+    global figure_count
+    figure_count=figure_count+1
+    return figure_count
+
+def plot_graphs(data,tag):
+    # Plot 
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    x = data['noOfUsers']
+    plt.plot(x, data['throughput'], label="fcfs")
+    plt.plot(x, data['throughputRR'], label="Round Robin")
+    # plt.xticks(x, data['noOfUsers'])
+    plt.xlabel('Number of Users')
+    plt.ylabel('Throughput (replies/sec)')
+    plt.title('Throughput VS No of users')
+    plt.tight_layout()
+    plt.grid()
+    plt.savefig(tag+'Throughput VS No of users')
+
+    # Plot
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    # x = range(len(data['noOfUsers']))
+    plt.plot(x, data['meanRes'], label="fcfs")
+    plt.plot(x, data['meanResRR'], label="Round Robin")
+    # plt.xticks(x, data['noOfUsers'])
+    plt.xlabel('Number of Users')
+    plt.ylabel('Response Time (sec)')
+    plt.title('Response Time VS No of users')
+    plt.tight_layout()
+    plt.grid()
+    plt.savefig(tag+'Response Time VS No of users')
+
+    # # Plot
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    # x = range(len(data['noOfUsers']))
+    plt.plot(x, data['cpu'], label="fcfs")
+    plt.plot(x, data['cpuRR'], label="Round Robin")
+    # plt.xticks(x, data['noOfUsers'])
+    plt.xlabel('Number of Users')
+    plt.ylabel('cpu utilization')
+    plt.title('CPU Utilization VS No of users')
+    plt.tight_layout()
+    plt.grid()
+    plt.savefig(tag+'CPU Utilization VS No of users')
+
+    # Plot
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    # x = range(len(data['noOfUsers']))
+    plt.plot(x, data['requestDrop'], label="fcfs")
+    plt.plot(x, data['requestDropRR'], label="Round Robin")
+    # plt.xticks(x, data['noOfUsers'])
+    plt.xlabel('Number of Users')
+    plt.ylabel('Requests dropped')
+    plt.title('Requests Dropped VS No of users')
+    plt.tight_layout()
+    plt.grid()
+    plt.savefig(tag+'Requests Dropped VS No of users')
+    # plt.show()
+
+
+
+    plt.show(block=False)
+
+    return
+
+def plot_compareGraphs(rr,fcfs,m,label1,label2,label3,maxUser,tag):
+
+    fcfs = fcfs[fcfs['noOfUsers']<maxUser]
+    rr = rr[rr['noOfUsers']<maxUser]
+    x = rr['noOfUsers']
+
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    plt.plot(x,rr['meanRes'],label=label1)
+    plt.plot(x,fcfs['meanRes'],label=label2)
+    plt.plot(x,m['meanRes'],label=label3)
+    plt.xlabel('No Of Users')
+    plt.ylabel('Response Time')
+    plt.title('Response Time VS No of users')
+    plt.legend()
+    plt.grid()
+    plt.tight_layout()
+    plt.savefig(tag+'Comparision Response Time')
+
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    plt.plot(x,rr['throughput'],label=label1)
+    plt.plot(x,fcfs['throughput'],label=label2)
+    plt.plot(x,m['throughput'],label=label3)
+    plt.xlabel('No Of Users')
+    plt.ylabel('Throughput')
+    plt.title('Throughput VS No of users')
+    plt.legend()
+    plt.grid()
+    plt.tight_layout()
+    plt.savefig(tag+'Comparision Throughput')
+
+
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    plt.plot(x,rr['cpu'],label=label1)
+    plt.plot(x,fcfs['cpu'],label=label2)
+    plt.plot(x,m['cpu'],label=label3)
+    plt.xlabel('No Of Users')
+    plt.ylabel('CPU Utilization')
+    plt.title('CPU Utilization VS No of users')
+    plt.legend()
+    plt.grid()
+    plt.tight_layout()
+    plt.savefig(tag+'Comparision CPU Utlization')
+
+    plt.show(block=False)
+
+    return
+
+def plot_confident_graph(data,tag):
+
+    data['Open']=data['minRes']
+    data['Low']=data['minRes']
+    data['High']=data['maxRes']
+    data['Close']=data['maxRes']
+
+    data1 = data[['noOfUsers', 'Open', 'High',
+         'Low', 'Close']]
+
+    plt.figure(get_figure_count())
+    
+    candlestick_ohlc(plt.axes(), data1.values, width =2,
+                    colorup = 'blue', colordown = 'red',
+                    alpha = 1)
+    
+    plt.grid(True)
+    
+    plt.xlabel('No Of Users')
+    plt.ylabel('Response Time')    
+    plt.title('Confidence Plot Response Time ')
+    plt.tight_layout()
+    plt.savefig(tag+'Confidence Interval Response Time')
+    plt.show(block=False)
+
+    return
+
+def plot_tp_gp_bp(data,tag):
+    plt.figure(get_figure_count())
+    x = data['noOfUsers']
+    plt.plot(x,data['badput'],label='badput')
+    plt.plot(x,data['goodput'],label='goodput')
+    plt.plot(x,data['throughput'],label='throughput')
+    plt.xlabel('No Of Users')
+    plt.ylabel('throughput')
+    plt.grid()
+    # plt.tight_layout()
+    plt.legend()
+    plt.title('Throughput GoodPut Badput')
+    plt.savefig(tag+'GBBP')
+    plt.show(block = False)
+
+    return
+
+def plot_curiocity(data,tag):
+    data = data[data['meanRes']<2]
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    plt.plot(data['meanRes'], data['throughput'])
+    plt.xlabel('Response time (sec)')
+    plt.ylabel('throughput')
+    plt.title('Throughput Vs Response time')
+    plt.tight_layout()
+    plt.grid()
+    plt.savefig(tag+'Throughput Vs Response time')
+    plt.show(block=False)
+
+    plt.figure(get_figure_count(),figsize=(6.8, 4.2))
+    plt.plot(data['cpu'], data['throughput'])
+    plt.xlabel('cpu')
+    plt.ylabel('throughput')
+    plt.title('Throughput Vs cpu')
+    plt.tight_layout()
+    plt.grid()
+    plt.savefig(tag+'Throughput Vs cpu')
+    plt.show(block=False)
+    return
+
+
+
+
+def metric_plot_generation():
+
+    fcfs = "data/Assignment 2 data - FCFS comparision.csv"
+    fcfs1000 = "data/Assignment 2 data - FCFS 1000 data.csv"
+
+    roundRobin = 'data/Assignment 2 data - RR comparision.csv'
+
+    measureFile = 'data/Assignment 2 data - Measurement Closed.csv'
+
+    rr001 = "data/Assignment 2 data - 1000 RR.csv"
+    rr_01 = "data/Assignment 2 data - RR 0.01.csv"
+    rr_1 = "data/Assignment 2 data - rr1.csv"
+
+    gpbp = "data/Assignment 2 data - Goodput Badput B.csv"
+
+    fcfs1000_data = pd.read_csv(fcfs1000)
+
+    fcfs_data = pd.read_csv(fcfs)
+    rr_data = pd.read_csv(roundRobin)
+    measureData = pd.read_csv(measureFile)
+    rr001_data = pd.read_csv(rr001)
+    rr01_data = pd.read_csv(rr_01)
+    rr1_data = pd.read_csv(rr_1)
+
+    gpbp_data = pd.read_csv(gpbp)
+
+    measureData = measureData[['noOfUsers','meanRes','cpu','throughput']]
+    measureData = measureData[measureData['noOfUsers']>1]
+    measureData['cpu'] = measureData['cpu']/100
+
+    # plot_graphs(gpbp_data,'gpbp_rr_')
+    # plot_graphs(fcfs_data,'fcfs_')
+    plot_graphs(fcfs1000_data,'fcfs_')
+    # plot_graphs(rr_data,'rr_')
+    # plot_graphs(rr001_data,'rr001_')
+    # plot_graphs(rr01_data,'rr01_')
+    # plot_graphs(rr1_data,'rr1_')
+    # plot_graphs(gpbp_data,'gpbp_rr_')
+
+    # plot_confident_graph(fcfs1000_data,'fcfs1000_')
+    # plot_tp_gp_bp(gpbp_data,'rr')
+
+    # plot_compareGraphs(rr_data,fcfs_data,measureData,'Round Robin','FCFS','MEASURED DATA',210,'m_cmp_')
+    # plot_compareGraphs(rr001_data,rr01_data,rr1_data,'ctx time : 0.001','ctx time : 0.01','ctx time : 0.1',840,'ctx_Cmp_')
+
+
+    return
+
+if __name__ == "__main__":
+    # file = 'Report.csv'
+    # data = pd.read_csv(file)
+    # plot_graphs(data,'New_')
+    metric_plot_generation()
+    plt.pause(1.0) # Pause for interval seconds.
+    input("hit[enter] to end.")
+    plt.close('all')

infile.txt

+1
+0.2
+5
+1
+1
+20
+5
\ No newline at end of file