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* Copyright 2015, Google Inc.
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following disclaimer
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* in the documentation and/or other materials provided with the
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* * Neither the name of Google Inc. nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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#ifndef GRPC_INTERNAL_CORE_IOMGR_TIME_AVERAGED_STATS_H
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#define GRPC_INTERNAL_CORE_IOMGR_TIME_AVERAGED_STATS_H
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/* This tracks a time-decaying weighted average. It works by collecting
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batches of samples and then mixing their average into a time-decaying
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weighted mean. It is designed for batch operations where we do many adds
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before updating the average. */
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/* The initial average value. This is the reported average until the first
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grpc_time_averaged_stats_update_average call. If a positive regress_weight
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is used, we also regress towards this value on each update. */
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/* The sample weight of "init_avg" that is mixed in with each call to
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grpc_time_averaged_stats_update_average. If the calls to
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grpc_time_averaged_stats_add_sample stop, this will cause the average to
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regress back to the mean. This should be non-negative. Set it to 0 to
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disable the bias. A value of 1 has the effect of adding in 1 bonus sample
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with value init_avg to each sample period. */
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double regress_weight;
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/* This determines the rate of decay of the time-averaging from one period
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to the next by scaling the aggregate_total_weight of samples from prior
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periods when combining with the latest period. It should be in the range
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[0,1]. A higher value adapts more slowly. With a value of 0.5, if the
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batches each have k samples, the samples_in_avg_ will grow to 2 k, so the
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weighting of the time average will eventually be 1/3 new batch and 2/3
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double persistence_factor;
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/* The total value of samples since the last UpdateAverage(). */
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double batch_total_value;
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/* The number of samples since the last UpdateAverage(). */
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double batch_num_samples;
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/* The time-decayed sum of batch_num_samples_ over previous batches. This is
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the "weight" of the old aggregate_weighted_avg_ when updating the
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double aggregate_total_weight;
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/* A time-decayed average of the (batch_total_value_ / batch_num_samples_),
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computed by decaying the samples_in_avg_ weight in the weighted average. */
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double aggregate_weighted_avg;
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} grpc_time_averaged_stats;
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/* See the comments on the members above for an explanation of init_avg,
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regress_weight, and persistence_factor. */
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void grpc_time_averaged_stats_init(grpc_time_averaged_stats *stats,
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double init_avg, double regress_weight,
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double persistence_factor);
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/* Add a sample to the current batch. */
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void grpc_time_averaged_stats_add_sample(grpc_time_averaged_stats *stats,
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/* Complete a batch and compute the new estimate of the average sample
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double grpc_time_averaged_stats_update_average(grpc_time_averaged_stats *stats);
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#endif /* GRPC_INTERNAL_CORE_IOMGR_TIME_AVERAGED_STATS_H */