1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
|
/*
*
* Copyright 2015 gRPC authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*/
#ifndef TEST_QPS_INTERARRIVAL_H
#define TEST_QPS_INTERARRIVAL_H
#include <chrono>
#include <cmath>
#include <random>
#include <vector>
#include <grpc++/support/config.h>
namespace grpc {
namespace testing {
// First create classes that define a random distribution
// Note that this code does not include C++-specific random distribution
// features supported in std::random. Although this would make this code easier,
// this code is required to serve as the template code for other language
// stacks. Thus, this code only uses a uniform distribution of doubles [0,1)
// and then provides the distribution functions itself.
class RandomDistInterface {
public:
RandomDistInterface() {}
virtual ~RandomDistInterface() = 0;
// Argument to transform is a uniform double in the range [0,1)
virtual double transform(double uni) const = 0;
};
inline RandomDistInterface::~RandomDistInterface() {}
// ExpDist implements an exponential distribution, which is the
// interarrival distribution for a Poisson process. The parameter
// lambda is the mean rate of arrivals. This is the
// most useful distribution since it is actually additive and
// memoryless. It is a good representation of activity coming in from
// independent identical stationary sources. For more information,
// see http://en.wikipedia.org/wiki/Exponential_distribution
class ExpDist final : public RandomDistInterface {
public:
explicit ExpDist(double lambda) : lambda_recip_(1.0 / lambda) {}
~ExpDist() override {}
double transform(double uni) const override {
// Note: Use 1.0-uni above to avoid NaN if uni is 0
return lambda_recip_ * (-log(1.0 - uni));
}
private:
double lambda_recip_;
};
// A class library for generating pseudo-random interarrival times
// in an efficient re-entrant way. The random table is built at construction
// time, and each call must include the thread id of the invoker
class InterarrivalTimer {
public:
InterarrivalTimer() {}
void init(const RandomDistInterface& r, int threads, int entries = 1000000) {
std::random_device devrand;
std::mt19937_64 generator(devrand());
std::uniform_real_distribution<double> rando(0, 1);
for (int i = 0; i < entries; i++) {
random_table_.push_back(
static_cast<int64_t>(1e9 * r.transform(rando(generator))));
;
}
// Now set up the thread positions
for (int i = 0; i < threads; i++) {
thread_posns_.push_back(random_table_.begin() + (entries * i) / threads);
}
}
virtual ~InterarrivalTimer(){};
int64_t next(int thread_num) {
auto ret = *(thread_posns_[thread_num]++);
if (thread_posns_[thread_num] == random_table_.end())
thread_posns_[thread_num] = random_table_.begin();
return ret;
}
private:
typedef std::vector<int64_t> time_table;
std::vector<time_table::const_iterator> thread_posns_;
time_table random_table_;
};
}
}
#endif
|
