/*
* Copyright 2015 Ben Manes. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.github.benmanes.caffeine.cache.simulator.policy.adaptive;
import static com.google.common.base.Preconditions.checkState;
import java.util.Set;
import com.github.benmanes.caffeine.cache.simulator.BasicSettings;
import com.github.benmanes.caffeine.cache.simulator.policy.Policy;
import com.github.benmanes.caffeine.cache.simulator.policy.PolicyStats;
import com.google.common.base.MoreObjects;
import com.google.common.collect.ImmutableSet;
import com.typesafe.config.Config;
import it.unimi.dsi.fastutil.longs.Long2ObjectMap;
import it.unimi.dsi.fastutil.longs.Long2ObjectOpenHashMap;
/**
* Adaptive Replacement Cache. This algorithm uses a queue for items that are seen once (T1), a
* queue for items seen multiple times (T2), and non-resident queues for evicted items that are
* being monitored (B1, B2). The maximum size of the T1 and T2 queues is adjusted dynamically based
* on the workload patterns and effectiveness of the cache.
* <p>
* This implementation is based on the pseudo code provided by the authors in their paper
* <a href="http://www.cs.cmu.edu/~15-440/READINGS/megiddo-computer2004.pdf">Outperforming LRU with
* an Adaptive Replacement Cache Algorithm</a> and is further described in their paper,
* <a href="https://www.usenix.org/event/fast03/tech/full_papers/megiddo/megiddo.pdf">ARC: A
* Self-Tuning, Low Overhead Replacement Cache</a>.
* <p>
* This algorithm is patented by IBM (6996676, 7096321, 7058766, 8612689) and Sun (7469320), making
* its use in applications ambiguous due to Sun's ZFS providing an implementation under the CDDL.
*
* @author ben.manes@gmail.com (Ben Manes)
*/
public final class ArcPolicy implements Policy {
// In Cache:
// - T1: Pages that have been accessed at least once
// - T2: Pages that have been accessed at least twice
// Ghost:
// - B1: Evicted from T1
// - B2: Evicted from T2
// Adapt:
// - Hit in B1 should increase size of T1, drop entry from T2 to B2
// - Hit in B2 should increase size of T2, drop entry from T1 to B1
private final Long2ObjectMap<Node> data;
private final PolicyStats policyStats;
private final int maximumSize;
private final Node headT1;
private final Node headT2;
private final Node headB1;
private final Node headB2;
private int sizeT1;
private int sizeT2;
private int sizeB1;
private int sizeB2;
private int p;
public ArcPolicy(Config config) {
BasicSettings settings = new BasicSettings(config);
this.policyStats = new PolicyStats("adaptive.Arc");
this.maximumSize = settings.maximumSize();
this.data = new Long2ObjectOpenHashMap<>();
this.headT1 = new Node();
this.headT2 = new Node();
this.headB1 = new Node();
this.headB2 = new Node();
}
/** Returns all variations of this policy based on the configuration parameters. */
public static Set<Policy> policies(Config config) {
return ImmutableSet.of(new ArcPolicy(config));
}
@Override
public void record(long key) {
policyStats.recordOperation();
Node node = data.get(key);
if (node == null) {
onMiss(key);
} else if (node.type == QueueType.B1) {
onHitB1(node);
} else if (node.type == QueueType.B2) {
onHitB2(node);
} else {
onHit(node);
}
}
private void onHit(Node node) {
// x ∈ T1 ∪ T2 (a hit in ARC(c) and DBL(2c)): Move x to the top of T2
if (node.type == QueueType.T1) {
sizeT1--;
sizeT2++;
}
node.remove();
node.type = QueueType.T2;
node.appendToTail(headT2);
policyStats.recordHit();
}
private void onHitB1(Node node) {
// x ∈ B1 (a miss in ARC(c), a hit in DBL(2c)):
// Adapt p = min{ c, p + max{ |B2| / |B1|, 1} }. REPLACE(p).
// Move x to the top of T2 and place it in the cache.
p = Math.min(maximumSize, p + Math.max(sizeB2 / sizeB1, 1));
evict(node);
sizeT2++;
sizeB1--;
node.remove();
node.type = QueueType.T2;
node.appendToTail(headT2);
policyStats.recordMiss();
}
private void onHitB2(Node node) {
// x ∈ B2 (a miss in ARC(c), a hit in DBL(2c)):
// Adapt p = max{ 0, p – max{ |B1| / |B2|, 1} } . REPLACE(p).
// Move x to the top of T2 and place it in the cache.
p = Math.max(0, p - Math.max(sizeB1 / sizeB2, 1));
evict(node);
sizeT2++;
sizeB2--;
node.remove();
node.type = QueueType.T2;
node.appendToTail(headT2);
policyStats.recordMiss();
}
private void onMiss(long key) {
// x ∈ L1 ∪ L2 (a miss in DBL(2c) and ARC(c)):
// case (i) |L1| = c:
// If |T1| < c then delete the LRU page of B1 and REPLACE(p).
// else delete LRU page of T1 and remove it from the cache.
// case (ii) |L1| < c and |L1| + |L2|≥ c:
// if |L1| + |L2|= 2c then delete the LRU page of B2.
// REPLACE(p) .
// Put x at the top of T1 and place it in the cache.
Node node = new Node(key);
node.type = QueueType.T1;
int sizeL1 = (sizeT1 + sizeB1);
int sizeL2 = (sizeT2 + sizeB2);
if (sizeL1 == maximumSize) {
if (sizeT1 < maximumSize) {
Node victim = headB1.next;
data.remove(victim.key);
victim.remove();
sizeB1--;
evict(node);
} else {
Node victim = headT1.next;
data.remove(victim.key);
victim.remove();
sizeT1--;
}
} else if ((sizeL1 < maximumSize) && ((sizeL1 + sizeL2) >= maximumSize)) {
if ((sizeL1 + sizeL2) >= (2 * maximumSize)) {
Node victim = headB2.next;
data.remove(victim.key);
victim.remove();
sizeB2--;
}
evict(node);
}
sizeT1++;
data.put(key, node);
node.appendToTail(headT1);
policyStats.recordMiss();
}
/** Evicts while the map exceeds the maximum capacity. */
private void evict(Node candidate) {
// if (|T1| ≥ 1) and ((x ∈ B2 and |T1| = p) or (|T1| > p))
// then move the LRU page of T1 to the top of B1 and remove it from the cache.
// else move the LRU page in T2 to the top of B2 and remove it from the cache.
if ((sizeT1 >= 1) && (((candidate.type == QueueType.B2) && (sizeT1 == p)) || (sizeT1 > p))) {
Node victim = headT1.next;
victim.remove();
victim.type = QueueType.B1;
victim.appendToTail(headB1);
sizeT1--;
sizeB1++;
} else {
Node victim = headT2.next;
victim.remove();
victim.type = QueueType.B2;
victim.appendToTail(headB2);
sizeT2--;
sizeB2++;
}
policyStats.recordEviction();
}
@Override
public PolicyStats stats() {
return policyStats;
}
@Override
public void finished() {
checkState(sizeT1 == data.values().stream().filter(node -> node.type == QueueType.T1).count());
checkState(sizeT2 == data.values().stream().filter(node -> node.type == QueueType.T2).count());
checkState(sizeB1 == data.values().stream().filter(node -> node.type == QueueType.B1).count());
checkState(sizeB2 == data.values().stream().filter(node -> node.type == QueueType.B2).count());
checkState((sizeT1 + sizeT2) <= maximumSize);
checkState((sizeB1 + sizeB2) <= maximumSize);
}
private enum QueueType {
T1, B1,
T2, B2,
}
static final class Node {
final long key;
Node prev;
Node next;
QueueType type;
Node() {
this.key = Long.MIN_VALUE;
this.prev = this;
this.next = this;
}
Node(long key) {
this.key = key;
}
/** Appends the node to the tail of the list. */
public void appendToTail(Node head) {
Node tail = head.prev;
head.prev = this;
tail.next = this;
next = head;
prev = tail;
}
/** Removes the node from the list. */
public void remove() {
checkState(key != Long.MIN_VALUE);
prev.next = next;
next.prev = prev;
prev = next = null;
type = null;
}
@Override
public String toString() {
return MoreObjects.toStringHelper(this)
.add("key", key)
.add("type", type)
.toString();
}
}
}