ppm/sim_graph.py at master · acharapko/ppm · GitHub

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import model_multipaxos as model
import paxos_defaults as params
import matplotlib.pyplot as plt
import numpy as np
from cycler import cycler

repeats = 1

start_tp = 5
tp_step = 200

n_p = 1

fig, ax = plt.subplots()
plt.xlabel('Throughput (rounds/sec)')
plt.ylabel('Latency (ms)')
#plt.title('Throughput vs. Latency [Model]')
plt.rc('lines', linewidth=1)
colors = ['g', 'r', 'b', 'c', 'k']
markers = ['o', 's', '*', 'X', 'D']
colorID = 0

for n in range(300, 1300, 200):
    lats = {}
    Rmax = model.computeRmax(n, n_p, params.mu_md, params.mu_ms, params.ttx)
    tp_step = 5
    end_tp = int(Rmax)-4
    print "end tp = " + str(end_tp)
    print "Rmax = " + str(Rmax)
    tp = []
    r = start_tp
    #for r in range(start_tp, end_tp, tp_step):
    while r < end_tp:

        tp.append(r)
        r += tp_step
        for i in range(0, repeats):
            print "tick: " + str(n) + "," + str(r) + ","+str(i)
            mu_r = 1000.0 / r
            sigma_r = mu_r / 0.5
            numops, simlats = model.model_random_round_arrival(
                N=n,
                qs=n / 2 + 1,  # majority quorum
                mu_local=params.mu_local,
                sigma_local=params.sigma_local,
                mu_ms=params.mu_ms,
                sigma_ms=params.sigma_ms,
                mu_md=params.mu_md,
                sigma_md=params.sigma_md,
                ttx=params.ttx,
                ttx_stddev=params.ttx_stddev,
                n_p=n_p,
                mu_r=mu_r,
                sigma_r=sigma_r,
                sim_clients=True
            )

            l = np.average(simlats) * 1000  # convert to ms
            # print "average = " + str(l) + " ms"
            if r in lats:
                lats[r] += l
            else:
                lats[r] = l
        lats[r] /= repeats
        # print lats

    #tp = range(start_tp, end_tp, tp_step)
    lat = [lats[key] for key in sorted(lats.keys(), reverse=False)]
    print lat
    p2 = ax.plot(tp, lat, marker=markers[colorID % 5], color=colors[colorID % 5], label=str(n) + " Nodes")
    colorID += 1

plt.ylim(0,500)
legend = ax.legend(loc='lower right', shadow=True)
plt.show()