#include "CrushTester.h"
-// chi squared stuff
-#include <math.h>
-
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
-# include <boost/math/distributions/chi_squared.hpp>
-# include <boost/accumulators/statistics/variance.hpp>
-# include <boost/accumulators/accumulators.hpp>
-# include <boost/accumulators/statistics/stats.hpp>
-
-// random number generator
-# include <boost/random/mersenne_twister.hpp>
-# include <boost/random/discrete_distribution.hpp>
-# include <boost/random/uniform_int_distribution.hpp>
-
-// needed to compute chi squared value
-using boost::math::chi_squared;
-using boost::accumulators::stats;
-using boost::accumulators::variance;
-using boost::accumulators::accumulator_set;
-
-// create a random number generator to simulate placements
-
-// use the mersenne twister as our seed generator
-typedef boost::mt19937 generator;
-
-generator gen(42); // repeatable
-//generator gen(static_cast<unsigned int>(std::time(0))); // non-repeatable (ish)
-
-// create another random number generator to pick buckets when we want to mark devices down
-generator bucket_gen(42);
-
-#endif
+// a better RNG to be found within
+#include <stdlib.h>
void CrushTester::set_device_weight(int dev, float f)
void CrushTester::adjust_weights(vector<__u32>& weight)
{
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
if (mark_down_device_ratio > 0) {
// active buckets
}
}
- // create the uniform distribution on the number of buckets to visit
- boost::random::uniform_int_distribution<> bucket_choose(0, buckets_above_devices.size() - 1);
-
// permute bucket list
for (unsigned i = 0; i < buckets_above_devices.size(); i++) {
- unsigned j = bucket_choose(bucket_gen);
+ unsigned j = lrand48() % (buckets_above_devices.size() - 1);
std::swap(buckets_above_devices[i], buckets_above_devices[j]);
}
items.push_back(crush.get_bucket_item(id, o));
// permute items
- boost::random::uniform_int_distribution<> item_choose(0, size - 1);
for (int o = 0; o < size; o++) {
- int j = item_choose(bucket_gen);
+ int j = lrand48() % (crush.get_bucket_size(id) - 1);
std::swap(items[o], items[j]);
}
}
}
}
-#else
- err << "WARNING: boost::random not compiled in, mark down ratios not working" << std::endl;
-#endif
}
bool CrushTester::check_valid_placement(int ruleno, vector<int> out, const vector<__u32>& weight){
for (unsigned i = 0; i < weight.size(); i++)
proportional_weights[i] = (float) weight[i] / (float) total_weight;
-
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- // create a random number generator with the device weights to use for simulating placements
- boost::random::discrete_distribution<> dist(proportional_weights);
-#endif
-
// determine the real maximum number of devices to return
int devices_requested = min(maxout, get_maximum_affected_by_rule(ruleno));
bool accept_placement = false;
// create a vector to hold our trial mappings
int temp_array[devices_requested];
for (int i = 0; i < devices_requested; i++){
- temp_array[i] = dist(gen);
+ temp_array[i] = lrand48() % (crush.get_max_devices());
}
trial_placement.assign(temp_array, temp_array + devices_requested);
max_x = 1023;
}
-
// initial osd weights
vector<__u32> weight;
for (int o = 0; o < crush.get_max_devices(); o++) {
int num_objects = ((max_x - min_x) + 1);
float num_devices = (float) per.size(); // get the total number of devices, better to cast as a float here
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- float test_chi_statistic = 0.0; // our observed chi squared statistic
-
- // look up the maximum expected chi squared statistic for the 5% and 1% confidence levels
- float chi_statistic_five_percent = quantile(complement(chi_squared(num_devices_active-1), 0.05));
- float chi_statistic_one_percent = quantile(complement(chi_squared(num_devices_active-1), 0.01));
-#endif
-
// create a map to hold batch-level placement information
map<int, vector<int> > batch_per;
- vector <float> device_test_chi (per.size() );
int objects_per_batch = num_objects / num_batches;
int batch_min = min_x;
int batch_max = min_x + objects_per_batch - 1;
-
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- // place holders for the reference values, we will probably SEGFAULT if we try zero degrees of freedom
- float batch_chi_statistic_five_percent = -1.0;
- float batch_chi_statistic_one_percent = -1.0;
-
- if (num_batches > 1) {
- // look up the chi squared statistic for the 5% and 1% confidence levels
- batch_chi_statistic_five_percent = quantile(complement(chi_squared(num_batches-1), 0.05));
- batch_chi_statistic_one_percent = quantile(complement(chi_squared(num_batches-1), 0.01));
- }
-#endif
// get the total weight of the system
int total_weight = 0;
for (unsigned i = 0; i < per.size(); i++)
proportional_weights[i] = (float) weight[i] / (float) total_weight;
-
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- // create a random number generator with the device weights to use for simulating placements
- boost::random::discrete_distribution<> dist(proportional_weights);
-#endif
-
// compute the expected number of objects stored per device when a device's weight is considered
vector<float> num_objects_expected(num_devices);
crush.do_rule(r, x, out, nr, weight);
} else {
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
+
if (output_statistics)
err << "RNG"; // prepend RNG to placement output to denote simulation
// test our new monte carlo placement generator
random_placement(r, out, nr, weight);
-#endif
}
if (output_statistics){
}
}
- // compute chi squared statistic for device examining the uniformity this batch of placements
- for (unsigned i = 0; i < per.size(); i++){
- device_test_chi[i] += pow( (temporary_per[i] - batch_num_objects_expected[i]), 2) /
- batch_num_objects_expected[i];
- }
-
batch_min = batch_max + 1;
batch_max = batch_min + objects_per_batch - 1;
}
<< " result size == " << p->first << ":\t"
<< p->second << "/" << (max_x-min_x+1) << std::endl;
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- // compute our overall test chi squared statistic examining the final distribution of placements
- for (unsigned i = 0; i < per.size(); i++)
- if (num_objects_expected[i] > 0){
- test_chi_statistic += pow((per[i] - num_objects_expected[i] ),2) / (float) num_objects_expected[i];
- }
-
- int num_devices_failing_at_five_percent = 0;
- int num_devices_failing_at_one_percent = 0;
-
- for (unsigned i = 0; i < per.size(); i++) {
- if (device_test_chi[i] > batch_chi_statistic_five_percent)
- num_devices_failing_at_five_percent++;
- if (device_test_chi[i] > batch_chi_statistic_one_percent)
- num_devices_failing_at_one_percent++;
- }
-
-#endif
-
if (output_statistics)
for (unsigned i = 0; i < per.size(); i++) {
if (output_utilization && num_batches > 1){
err << " device " << i << ":\t"
<< "\t" << " stored " << ": " << per[i]
<< "\t" << " expected " << ": " << num_objects_expected[i]
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- << "\t" << " X^2 " << ": " << device_test_chi[i]
- << "\t" << " critical (5% confidence) " << ": " << batch_chi_statistic_five_percent
- << "\t" << " (1% confidence) " << ": " << batch_chi_statistic_one_percent
-#endif
<< std::endl;
}
} else if (output_utilization_all && num_batches > 1) {
err << " device " << i << ":\t"
<< "\t" << " stored " << ": " << per[i]
<< "\t" << " expected " << ": " << num_objects_expected[i]
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- << "\t" << " X^2 " << ": " << device_test_chi[i]
- << "\t" << " critical X^2 (5% confidence) " << ": " << batch_chi_statistic_five_percent
- << "\t" << " (1% confidence) " << ": " << batch_chi_statistic_one_percent
-#endif
<< std::endl;
}
}
-
-#ifdef HAVE_BOOST_RANDOM_DISCRETE_DISTRIBUTION
- err << " chi squared = " << test_chi_statistic
- << " (vs " << chi_statistic_five_percent << " / "
- << chi_statistic_one_percent
- << " for 5% / 1% confidence level) " << std::endl;
- if (output_utilization || output_utilization_all)
- err << " total system weight (dec) = " << (total_weight / (float) 0x10000) << std::endl;
-
- if (num_batches > 1 && output_statistics) {
- err << " " << num_devices_failing_at_five_percent << "/" << num_devices_active << " ("
- << (100.0*((float) num_devices_failing_at_five_percent / (float) num_devices_active))
- << "%) devices failed testing at 5% confidence level" << std::endl;
- err << " " << num_devices_failing_at_one_percent << "/" << num_devices_active << " ("
- << (100.0*((float) num_devices_failing_at_one_percent / (float) num_devices_active))
- << "%) devices failed testing at 1% confidence level" << std::endl;
- }
-#endif
}
}
projects / ceph.git / commitdiff