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/*
*
* Copyright 2015, Google Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are
* met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following disclaimer
* in the documentation and/or other materials provided with the
* distribution.
* * Neither the name of Google Inc. nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
* OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
*/
#ifndef TEST_QPS_INTERARRIVAL_H
#define TEST_QPS_INTERARRIVAL_H
#include <chrono>
#include <cmath>
#include <cstdlib>
#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 GRPC_FINAL : public RandomDistInterface {
public:
explicit ExpDist(double lambda) : lambda_recip_(1.0 / lambda) {}
~ExpDist() GRPC_OVERRIDE {}
double transform(double uni) const GRPC_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_;
};
// UniformDist implements a random distribution that has
// interarrival time uniformly spread between [lo,hi). The
// mean interarrival time is (lo+hi)/2. For more information,
// see http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29
class UniformDist GRPC_FINAL : public RandomDistInterface {
public:
UniformDist(double lo, double hi) : lo_(lo), range_(hi - lo) {}
~UniformDist() GRPC_OVERRIDE {}
double transform(double uni) const GRPC_OVERRIDE {
return uni * range_ + lo_;
}
private:
double lo_;
double range_;
};
// DetDist provides a random distribution with interarrival time
// of val. Note that this is not additive, so using this on multiple
// flows of control (threads within the same client or separate
// clients) will not preserve any deterministic interarrival gap across
// requests.
class DetDist GRPC_FINAL : public RandomDistInterface {
public:
explicit DetDist(double val) : val_(val) {}
~DetDist() GRPC_OVERRIDE {}
double transform(double uni) const GRPC_OVERRIDE { return val_; }
private:
double val_;
};
// ParetoDist provides a random distribution with interarrival time
// spread according to a Pareto (heavy-tailed) distribution. In this
// model, many interarrival times are close to the base, but a sufficient
// number will be high (up to infinity) as to disturb the mean. It is a
// good representation of the response times of data center jobs. See
// http://en.wikipedia.org/wiki/Pareto_distribution
class ParetoDist GRPC_FINAL : public RandomDistInterface {
public:
ParetoDist(double base, double alpha)
: base_(base), alpha_recip_(1.0 / alpha) {}
~ParetoDist() GRPC_OVERRIDE {}
double transform(double uni) const GRPC_OVERRIDE {
// Note: Use 1.0-uni above to avoid div by zero if uni is 0
return base_ / pow(1.0 - uni, alpha_recip_);
}
private:
double base_;
double alpha_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) {
for (int i = 0; i < entries; i++) {
// rand is the only choice that is portable across POSIX and Windows
// and that supports new and old compilers
const double uniform_0_1 =
static_cast<double>(rand()) / static_cast<double>(RAND_MAX);
random_table_.push_back(
static_cast<int64_t>(1e9 * r.transform(uniform_0_1)));
}
// 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
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