/* * * 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 #include #include #include #include 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(rand()) / static_cast(RAND_MAX); random_table_.push_back( static_cast(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 time_table; std::vector thread_posns_; time_table random_table_; }; } } #endif