/*

 * Copyright 2001-2005 Sun Microsystems, Inc.  All Rights Reserved.

 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.

 *

 * This code is free software; you can redistribute it and/or modify it

 * under the terms of the GNU General Public License version 2 only, as

 * published by the Free Software Foundation.

 *

 * This code is distributed in the hope that it will be useful, but WITHOUT

 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or

 * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License

 * version 2 for more details (a copy is included in the LICENSE file that

 * accompanied this code).

 *

 * You should have received a copy of the GNU General Public License version

 * 2 along with this work; if not, write to the Free Software Foundation,

 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.

 *

 * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,

 * CA 95054 USA or visit www.sun.com if you need additional information or

 * have any questions.

 *

 */

class AllocationStats VALUE_OBJ_CLASS_SPEC {

  // A duration threshold (in ms) used to filter

  // possibly unreliable samples.

  static float _threshold;

  // We measure the demand between the end of the previous sweep and

  // beginning of this sweep:

  //   Count(end_last_sweep) - Count(start_this_sweep)

  //     + splitBirths(between) - splitDeaths(between)

  // The above number divided by the time since the start [END???] of the

  // previous sweep gives us a time rate of demand for blocks

  // of this size. We compute a padded average of this rate as

  // our current estimate for the time rate of demand for blocks

  // of this size. Similarly, we keep a padded average for the time

  // between sweeps. Our current estimate for demand for blocks of

  // this size is then simply computed as the product of these two

  // estimates.

  AdaptivePaddedAverage _demand_rate_estimate;

  ssize_t     _desired;          // Estimate computed as described above

  ssize_t     _coalDesired;     // desired +/- small-percent for tuning coalescing

  ssize_t     _surplus;         // count - (desired +/- small-percent),

                                // used to tune splitting in best fit

  ssize_t     _bfrSurp;         // surplus at start of current sweep

  ssize_t     _prevSweep;       // count from end of previous sweep

  ssize_t     _beforeSweep;     // count from before current sweep

  ssize_t     _coalBirths;      // additional chunks from coalescing

  ssize_t     _coalDeaths;      // loss from coalescing

  ssize_t     _splitBirths;     // additional chunks from splitting

  ssize_t     _splitDeaths;     // loss from splitting

  size_t     _returnedBytes;    // number of bytes returned to list.

 public:

  void initialize() {

    AdaptivePaddedAverage* dummy =

      new (&_demand_rate_estimate) AdaptivePaddedAverage(CMS_FLSWeight,

                                                         CMS_FLSPadding);

    _desired = 0;

    _coalDesired = 0;

    _surplus = 0;

    _bfrSurp = 0;

    _prevSweep = 0;

    _beforeSweep = 0;

    _coalBirths = 0;

    _coalDeaths = 0;

    _splitBirths = 0;

    _splitDeaths = 0;

    _returnedBytes = 0;

  }

  AllocationStats() {

    initialize();

  }

  // The rate estimate is in blocks per second.

  void compute_desired(size_t count,

                       float inter_sweep_current,

                       float inter_sweep_estimate) {

    // If the latest inter-sweep time is below our granularity

    // of measurement, we may call in here with

    // inter_sweep_current == 0. However, even for suitably small

    // but non-zero inter-sweep durations, we may not trust the accuracy

    // of accumulated data, since it has not been "integrated"

    // (read "low-pass-filtered") long enough, and would be

    // vulnerable to noisy glitches. In such cases, we

    // ignore the current sample and use currently available

    // historical estimates.

    if (inter_sweep_current > _threshold) {

      ssize_t demand = prevSweep() - count + splitBirths() - splitDeaths();

      float rate = ((float)demand)/inter_sweep_current;

      _demand_rate_estimate.sample(rate);

      _desired = (ssize_t)(_demand_rate_estimate.padded_average()

                           *inter_sweep_estimate);

    }

  }

  ssize_t desired() const { return _desired; }

  ssize_t coalDesired() const { return _coalDesired; }

  void set_coalDesired(ssize_t v) { _coalDesired = v; }

  ssize_t surplus() const { return _surplus; }

  void set_surplus(ssize_t v) { _surplus = v; }

  void increment_surplus() { _surplus++; }

  void decrement_surplus() { _surplus--; }

  ssize_t bfrSurp() const { return _bfrSurp; }

  void set_bfrSurp(ssize_t v) { _bfrSurp = v; }

  ssize_t prevSweep() const { return _prevSweep; }

  void set_prevSweep(ssize_t v) { _prevSweep = v; }

  ssize_t beforeSweep() const { return _beforeSweep; }

  void set_beforeSweep(ssize_t v) { _beforeSweep = v; }

  ssize_t coalBirths() const { return _coalBirths; }

  void set_coalBirths(ssize_t v) { _coalBirths = v; }

  void increment_coalBirths() { _coalBirths++; }

  ssize_t coalDeaths() const { return _coalDeaths; }

  void set_coalDeaths(ssize_t v) { _coalDeaths = v; }

  void increment_coalDeaths() { _coalDeaths++; }

  ssize_t splitBirths() const { return _splitBirths; }

  void set_splitBirths(ssize_t v) { _splitBirths = v; }

  void increment_splitBirths() { _splitBirths++; }

  ssize_t splitDeaths() const { return _splitDeaths; }

  void set_splitDeaths(ssize_t v) { _splitDeaths = v; }

  void increment_splitDeaths() { _splitDeaths++; }

  NOT_PRODUCT(

    size_t returnedBytes() const { return _returnedBytes; }

    void set_returnedBytes(size_t v) { _returnedBytes = v; }

  )

};