inflight 守恒算法的实现和仿真

本文主要是介绍inflight 守恒算法的实现和仿真，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

前面介绍过，只要某条流的 inflt 在 bdp 之外再增加一个相等的余量 I，即 inflt = bdp + I，比如 I = 2，I = 3，…，就一定会收敛到公平，且不会占据过多 buffer，因此 rtt 不会膨胀，I 的大小影响收敛速度，I 越大，收敛越快，但 buffer 占据也更多，I 越小，收敛越慢，但 buffer 占据更少，所以效率和公平的 tradeoff 在此体现。

记住这个简洁的结论，然后将 I 调整为动态的负反馈，就是一个新算法，该算法占据 “一定量” 的 buffer 而不是像 aimd 那样抖动，占据 buffer 的大小由 I 的均值决定。平稳压倒一切，抖动是低效的根源，始终占据一定量的 buffer 是可以接受的，通过调参可以将这个 “一定量” 压到尽可能小。

简单用 c 实现了一版 inflight 守恒算法，非常简洁：

#include <stdio.h>
#include <stdlib.h>#define BW_FILTER_LEN 10double RTPROP = 1;
double C = 100.0; // bottleneck_link_bw
double I = 0.0;struct es {double E;double bw;
};struct ebest_flow {int index;               /* flow identifier */int status;double I;double inflt;double min_rtt;double srtt;double sending_bw;       /* current receive bw */double receive_bw;       /* current receive bw */struct es max_e;           /* current estimated bw */struct es e_samples[BW_FILTER_LEN];int phase_offset;
};struct ebest_flow f1;
struct ebest_flow f2;
struct ebest_flow f3;
struct ebest_flow f4;int t = 0;
int bw_filter_index = 0;#define max(a, b) (a > b) ? (a) : (b)
#define min(a, b) (a < b) ? (a) : (b)void ebest_set_max_e(struct ebest_flow *f)
{int i = 0;f->max_e.bw = 0;for (i = 0; i < BW_FILTER_LEN; i++) {f->max_e.E = max(f->max_e.E, f->e_samples[i].E);f->max_e.bw = f->e_samples[i].bw;}f->I = 0.7 * f->I + 0.3 * 40 * f->min_rtt * f->max_e.bw/(20 * f->min_rtt + f->max_e.bw * f->srtt) * (f->min_rtt / f->srtt);
}void ebest_update_maxbw_minrtt(struct ebest_flow *f, double rtt)
{rtt = (rtt > RTPROP)?:RTPROP;f->e_samples[bw_filter_index].E = f->receive_bw / rtt;f->e_samples[bw_filter_index].bw = f->receive_bw;ebest_set_max_e(f);if (rtt <= f->min_rtt) {f->srtt = f->min_rtt = rtt;} else {f->srtt = rtt;}
}void ebest_update_sending_bw(struct ebest_flow *f)
{f->inflt = f->max_e.bw * f->min_rtt + f->I;printf("#### f: %d  %.3f\n", f->index, f->I);f->sending_bw = f->max_e.bw;printf("flow %d phase: %d max_bw: %.3f sending_bw: %.3f\n",f->index, 0, f->max_e.bw, f->sending_bw);
}void simulate_one_phase(int i)
{double rtt;//if (i == 1500)//  C = 160;//if (i == 2500)//  C = 40;ebest_update_sending_bw(&f1);ebest_update_sending_bw(&f2);ebest_update_sending_bw(&f3);ebest_update_sending_bw(&f4);printf("t= %04d sending: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, f1.sending_bw, f2.sending_bw, f3.sending_bw, f4.sending_bw);double total_I = 0;if (i < 1000) {rtt = (f1.inflt + f2.inflt + f3.inflt) / C;f1.receive_bw = C * f1.inflt / (f1.inflt + f2.inflt + f3.inflt);f2.receive_bw = C * f2.inflt / (f1.inflt + f2.inflt + f3.inflt);f3.receive_bw = C * f3.inflt / (f1.inflt + f2.inflt + f3.inflt);f4.receive_bw = 0;f4.max_e.bw = 0;f4.inflt = 0;if (i == 999) {f4.max_e.bw = 0.1 * C;f4.inflt = 0.1 * C * RTPROP + I;f4.I = I;f4.receive_bw = 0.1 * C;printf("@@@@### time: %d  f1: %.3f  f2: %.3f  f3: %.3f  f4: %.3f \n", t, f1.inflt, f2.inflt, f3.inflt, f4.inflt);}total_I = f1.I + f2.I + f3.I;printf("t= %04d  remain: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, f1.I, f2.I, f3.I, total_I);} else if (i >= 1000 && i < 2000) {rtt = (f1.inflt + f2.inflt + f3.inflt + f4.inflt) / C;f1.receive_bw = C * f1.inflt / (f1.inflt + f2.inflt + f3.inflt + f4.inflt);f2.receive_bw = C * f2.inflt / (f1.inflt + f2.inflt + f3.inflt + f4.inflt);f3.receive_bw = C * f3.inflt / (f1.inflt + f2.inflt + f3.inflt + f4.inflt);f4.receive_bw = C * f4.inflt / (f1.inflt + f2.inflt + f3.inflt + f4.inflt);if (i < 1100) {printf("@@@@### time: %d  f1: %.3f  f2: %.3f  f3: %.3f  f4: %.3f \n", t, f1.inflt, f2.inflt, f3.inflt, f4.inflt);}total_I = f1.I + f2.I + f3.I + f4.I;printf("t= %04d  remain: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, f1.I, f2.I, f3.I, total_I);} else {rtt = (f1.inflt + f2.inflt) / C;f1.receive_bw = C * f1.inflt / (f1.inflt + f2.inflt);f2.receive_bw = C * f2.inflt / (f1.inflt + f2.inflt);f3.receive_bw = 0;f4.receive_bw = 0;f3.max_e.bw = 0;f4.max_e.bw = 0;f3.inflt = 0;f4.inflt = 0;total_I = f1.I + f2.I;printf("t= %04d  remain: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, f1.I, f2.I, f3.I, total_I);}if (rtt < RTPROP)rtt = RTPROP;printf("t= %04d receive: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, f1.receive_bw, f2.receive_bw, f3.receive_bw, f4.receive_bw);ebest_update_maxbw_minrtt(&f1, rtt);ebest_update_maxbw_minrtt(&f2, rtt);ebest_update_maxbw_minrtt(&f3, rtt);ebest_update_maxbw_minrtt(&f4, rtt);printf("t= %04d  max_bw: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, f1.max_e.bw, f2.max_e.bw, f3.max_e.bw, f4.max_e.bw);printf("t= %04d  inflt: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, f1.inflt, f2.inflt, f3.inflt, f4.inflt);printf("t= %04d  min_rtt: f1: %.3f f2: %.3f f3: %.3f f4: %.3f\n",t, rtt, f2.min_rtt, f3.min_rtt, f4.min_rtt);t++;bw_filter_index = (bw_filter_index + 1) % BW_FILTER_LEN;
}int main(int argc, char *argv[])
{int i = 0;if (argc > 1) I = atof(argv[1]);f1.index = 1;f2.index = 2;f3.index = 3;f4.index = 4;f1.max_e.bw = 0.9 * C;f2.max_e.bw = 0.3 * C;f3.max_e.bw = 0.6 * C;f1.max_e.E = f1.max_e.bw / RTPROP;f2.max_e.E = f2.max_e.bw / RTPROP;f3.max_e.E = f3.max_e.bw / RTPROP;f1.I = I;f2.I = I;f3.I = I;f4.I = 0;f1.srtt = f1.min_rtt = RTPROP;f2.srtt = f2.min_rtt = RTPROP;f3.srtt = f3.min_rtt = RTPROP;f4.srtt = f4.min_rtt = RTPROP;f1.inflt = 0.1 * C * RTPROP;f2.inflt = 0.3 * C * RTPROP;f3.inflt = 0.6 * C * RTPROP;f1.e_samples[BW_FILTER_LEN - 1] = f1.max_e;f2.e_samples[BW_FILTER_LEN - 1] = f2.max_e;f3.e_samples[BW_FILTER_LEN - 1] = f3.max_e;for (i = 0; i < 3000; i++) {simulate_one_phase(i);}return 0;
}

算法和建模分别参见 inflight 守恒背后的哲学与 inflight 守恒数学建模.

这个算法的核心只需要设置 remain 余量，剩下的跟踪 E_best = max(bw / delay) 即可，因此 remain 一定是个负反馈方程：

$Remain=\dfrac{\alpha\cdot RTT_{min}\cdot BW_{when\_E\_best}}{\beta \cdot RTT_{min}+BW_{when\_E\_best}\cdot RTT_{smooth}}\cdot \dfrac{RTT_{min}}{RTT_{smooth}}$