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@@ -15,6 +15,7 @@ import subprocess
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import os
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import sys
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import math
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+import time
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from gen_manifest import generate_manifest
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from gen_enclave_config import generate_config
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from logs_to_csv import parse_output_logs
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@@ -24,10 +25,10 @@ from logs_to_csv import parse_output_logs
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# CONFIGS TO SET:
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MANIFEST_FILE = "App/manifest.yaml"
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-LOG_FOLDER = "Test_diagnostic1/"
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+LOG_FOLDER = "Test/"
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NUM_EPOCHS = 5
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-PRIVATE_ROUTE = True
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+PRIVATE_ROUTE = False
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PRIV_OUT = 1
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PRIV_IN = 1
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PUB_OUT = 1
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@@ -43,14 +44,14 @@ PUB_IN = 1
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# T = [16, 8, 4, 2, 1]
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## Figure 7
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-N = [1<<17]
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-M = [4]
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-T = [4]
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+#N = [1<<15, 1<<16, 1<<17, 1<<18, 1<<19, 1<<20]
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+#M = [4]
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+#T = [4]
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## Figure 8
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-# N = [1<<20]
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-# M = [72, 64, 48, 36, 32, 24, 16, 8, 4, 2, 1]
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-# T = [1]
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+N = [1<<16, 1<<20]
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+M = [72, 64, 48, 36, 32, 24, 16, 8, 4, 2, 1]
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+T = [1]
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# Max servers depending on number of threads (assuming 72 available
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# cores for servers)
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@@ -105,8 +106,13 @@ if __name__ == "__main__":
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for m in M:
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if(m <= m_end):
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for n in N:
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- for run in ["diagnostic", "experiment"]:
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+ #for run in ["diagnostic", "experiment"]:
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+ for run in ["experiment"]:
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num_WN_to_precompute = 0
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+ if(PRIVATE_ROUTE):
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+ num_WN_to_precompute = 2 * 3
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+ else:
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+ num_WN_to_precompute = 2 * 8
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# Make the correct output folder for diagnostic/experiment
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experiment_name = str(n) + "_" + str(m) + "_" + str(t) + "_" + str(b) + "/"
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@@ -122,19 +128,22 @@ if __name__ == "__main__":
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print("\n\n Running DIAGNOSTIC t = %d, m = %d, n = %d \n\n" % (t, m, n))
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# Manifest generated by diagnostic can be reused by the actual experiment
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generate_manifest(n, m, t, b, PRIVATE_ROUTE, PRIV_OUT, PRIV_IN, PUB_OUT, PUB_IN)
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- generate_config(n, m, t, b, PRIVATE_ROUTE, PRIV_OUT, PRIV_IN, PUB_OUT, PUB_IN)
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+ generate_config(n, m, t, b, PRIVATE_ROUTE, PRIV_OUT, PRIV_IN, PUB_OUT, PUB_IN, num_WN_to_precompute)
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epoch_param = epoch_time_estimate(n, m, t, b)
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elif(run == "experiment"):
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- num_sizes, pwn_max, epoch_max, scm_max = parse_output_logs(DIAGNOSTIC_FOLDER, experiment_name)
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- print("From logs_to_csv: num_sizes = %d, pwn_max = %f, epoch_max = %f, scm_max = %f"
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- % (num_sizes, pwn_max, epoch_max, scm_max))
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+ #print("Waiting for 2 mins to reset sockets")
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+ #time.sleep(120)
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+ #num_sizes, pwn_max, epoch_max, scm_max = parse_output_logs(DIAGNOSTIC_FOLDER, experiment_name)
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+ #print("From logs_to_csv: num_sizes = %d, pwn_max = %f, epoch_max = %f, scm_max = %f"
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+ #% (num_sizes, pwn_max, epoch_max, scm_max))
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print("\n\n Running EXPERIMENT t = %d, m = %d, n = %d \n\n" % (t, m, n))
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- num_WN_to_precompute = math.ceil((num_sizes * pwn_max)/epoch_max)
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- print("num_WN_to_precompute = %d" %(num_WN_to_precompute))
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- if(num_WN_to_precompute < 2 * num_sizes):
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- num_WN_to_precompute = 2 * num_sizes
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- print("num_WN_to_precompute (pushed up to min 2 sets) = %d" %(num_WN_to_precompute))
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- epoch_param = epoch_max + 2 * scm_max
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+ #num_WN_to_precompute = math.ceil((num_sizes * pwn_max)/epoch_max)
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+ #print("num_WN_to_precompute = %d" %(num_WN_to_precompute))
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+ #if(num_WN_to_precompute < 2 * num_sizes):
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+ # num_WN_to_precompute = 2 * num_sizes
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+ #print("num_WN_to_precompute (pushed up to min 2 sets) = %d" %(num_WN_to_precompute))
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+ #epoch_param = math.ceil(epoch_max + 10 * m * scm_max)
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+ epoch_param = epoch_time_estimate(n, m, t, b)
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generate_config(n, m, t, b, PRIVATE_ROUTE, PRIV_IN, PUB_OUT, PUB_IN, num_WN_to_precompute)
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# Either estimate from epoch_time_estimate for diagnostic
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