序
本文主要研究一下rsocket load balancer的Ewma
Moving Average
SMA
SMA(Simple Moving Average)，即简单移动平均，其公式如下：
SMAt = (Pt + Pt-1 + Pt-2 + Pt-3 + ... + Pt-n+1) / n这里的Pt到为Pt-n+1为最近的n个数据
WMA
WMA(Weighted Moving Average)，即加权移动平均，其公式如下：
WMAt = (Pt * Wt) + (Pt-1 * Wt-1) + ... + (Pt-n+1 * Wt-n+1)WMA会给最近的n个数据加上权重，其中这些权重加起来和为1，一般是较近的数据权重比较大
EMA或EWMA
EMA(Exponentially Moving Average)指数移动平均或EWMA(Exponentially Weighted Moving Average)指数加权移动平均，其公式如下：
EMAt = (Pt * S) + (1- S) * EMAt-1它有一个S参数为平滑指数，一般是取2/(N+1)
Ewma
rsocket-load-balancer-0.12.1-sources.jar!/io/rsocket/stat/Ewma.java
public class Ewma {
private final long tau;
private volatile long stamp;
private volatile double ewma;
public Ewma(long halfLife, TimeUnit unit, double initialValue) {
this.tau = Clock.unit().convert((long) (halfLife / Math.log(2)), unit);
stamp = 0L;
ewma = initialValue;
}
public synchronized void insert(double x) {
long now = Clock.now();
double elapsed = Math.max(0, now - stamp);
stamp = now;
double w = Math.exp(-elapsed / tau);
ewma = w * ewma + (1.0 - w) * x;
}
public synchronized void reset(double value) {
stamp = 0L;
ewma = value;
}
public double value() {
return ewma;
}
@Override
public String toString() {
return "Ewma(value=" + ewma + ", age=" + (Clock.now() - stamp) + ")";
}
}- Ewma的构造器需要指定halfLife、timeunit、initialValue(
ewma初始值)参数；ewma = w ewma + (1.0 - w) x，其中x为当前值，w为权重 - 权重w = Math.exp(-elapsed / tau)，即e的-elapsed / tau次方；elapsed为距离上次计算的时间长度；tau(
希腊字母)为EWMA的时间常量 - 这里的tau = halfLife / Math.log(2)根据timeunit转换后的值；其中halfLife参数，代表speed of convergence，即收敛的速度
RSocketSupplier
rsocket-load-balancer-0.12.1-sources.jar!/io/rsocket/client/filter/RSocketSupplier.java
public class RSocketSupplier implements Availability, Supplier>, Closeable {
private static final double EPSILON = 1e-4;
private Supplier> rSocketSupplier;
private final MonoProcessor onClose;
private final long tau;
private long stamp;
private final Ewma errorPercentage;
public RSocketSupplier(Supplier> rSocketSupplier, long halfLife, TimeUnit unit) {
this.rSocketSupplier = rSocketSupplier;
this.tau = Clock.unit().convert((long) (halfLife / Math.log(2)), unit);
this.stamp = Clock.now();
this.errorPercentage = new Ewma(halfLife, unit, 1.0);
this.onClose = MonoProcessor.create();
}
public RSocketSupplier(Supplier> rSocketSupplier) {
this(rSocketSupplier, 5, TimeUnit.SECONDS);
}
@Override
public double availability() {
double e = errorPercentage.value();
if (Clock.now() - stamp > tau) {
// If the window is expired artificially increase the availability
double a = Math.min(1.0, e + 0.5);
errorPercentage.reset(a);
}
if (e < EPSILON) {
e = 0.0;
} else if (1.0 - EPSILON < e) {
e = 1.0;
}
return e;
}
private synchronized void updateErrorPercentage(double value) {
errorPercentage.insert(value);
stamp = Clock.now();
}
@Override
public Mono get() {
return rSocketSupplier
.get()
.doOnNext(o -> updateErrorPercentage(1.0))
.doOnError(t -> updateErrorPercentage(0.0))
.map(AvailabilityAwareRSocketProxy::new);
}
@Override
public void dispose() {
onClose.onComplete();
}
@Override
public boolean isDisposed() {
return onClose.isDisposed();
}
@Override
public Mono onClose() {
return onClose;
}
private class AvailabilityAwareRSocketProxy extends RSocketProxy {
public AvailabilityAwareRSocketProxy(RSocket source) {
super(source);
onClose.doFinally(signalType -> source.dispose()).subscribe();
}
@Override
public Mono fireAndForget(Payload payload) {
return source
.fireAndForget(payload)
.doOnError(t -> errorPercentage.insert(0.0))
.doOnSuccess(v -> updateErrorPercentage(1.0));
}
@Override
public Mono requestResponse(Payload payload) {
return source
.requestResponse(payload)
.doOnError(t -> errorPercentage.insert(0.0))
.doOnSuccess(p -> updateErrorPercentage(1.0));
}
@Override
public Flux requestStream(Payload payload) {
return source
.requestStream(payload)
.doOnError(th -> errorPercentage.insert(0.0))
.doOnComplete(() -> updateErrorPercentage(1.0));
}
@Override
public Flux requestChannel(Publisher payloads) {
return source
.requestChannel(payloads)
.doOnError(th -> errorPercentage.insert(0.0))
.doOnComplete(() -> updateErrorPercentage(1.0));
}
@Override
public Mono metadataPush(Payload payload) {
return source
.metadataPush(payload)
.doOnError(t -> errorPercentage.insert(0.0))
.doOnSuccess(v -> updateErrorPercentage(1.0));
}
@Override
public double availability() {
// If the window is expired set success and failure to zero and return
// the child availability
if (Clock.now() - stamp > tau) {
updateErrorPercentage(1.0);
}
return source.availability() * errorPercentage.value();
}
}
} - RSocketSupplier实现了Availability、Supplier、Closeable接口，其中它定义了errorPercentage变量，其类型为Ewma；如果没有指定halfLife值，则RSocketSupplier默认halfLife为5秒，ewma的初始值为1.0
- RSocketSupplier定义了一个常量EPSILON = 1e-4，其availability方法会先计算availability，然后在距离上次计算时间stamp超过tau值时会重置errorPercentage；之后当availability小于EPSILON时返回0，当availability + EPSILON大于1时返回1.0
- updateErrorPercentage方法用于往ewma插入新值，同时更新stamp；get方法的doOnNext方法updateErrorPercentage值为1.0，doOnError方法updateErrorPercentage值为0.0；map会将RSocket转换为AvailabilityAwareRSocketProxy；AvailabilityAwareRSocketProxy对目标RSocket进行代理，对相关方法的doOnError及doOnSuccess都织入errorPercentage的统计
小结
- Moving Average有好几种算法，包括SMA(
Simple Moving Average)、WMA(Weighted Moving Average)、EMA(Exponentially Moving Average)或EWMA(Exponentially Weighted Moving Average) - Ewma的构造器需要指定halfLife、timeunit、initialValue(
ewma初始值)参数；ewma = w ewma + (1.0 - w) x，其中x为当前值，w为权重；权重w = Math.exp(-elapsed / tau)，即e的-elapsed / tau次方；elapsed为距离上次计算的时间长度；tau(希腊字母)为EWMA的时间常量；这里的tau = halfLife / Math.log(2)根据timeunit转换后的值；其中halfLife参数，代表speed of convergence，即收敛的速度 - rsocket load balancer使用了Ewma了统计服务的availability；其中RSocketSupplier实现了Availability、Supplier、Closeable接口，其中它定义了errorPercentage变量，其类型为Ewma；如果没有指定halfLife值，则RSocketSupplier默认halfLife为5秒，ewma的初始值为1.0；RSocketSupplier的get方法会将RSocket转换为AvailabilityAwareRSocketProxy，而AvailabilityAwareRSocketProxy则会对目标RSocket进行代理，对相关方法的doOnError及doOnSuccess都织入errorPercentage的统计
doc
- Simple Moving Average - SMA Definition
- Weighted Moving Averages: The Basics
- Exponential Moving Average - EMA Definition
- How Is the Exponential Moving Average (EMA) Formula Calculated?
- Moving Average, Weighted Moving Average, and Exponential Moving Average
- Exploring the Exponentially Weighted Moving Average
- EWMA 移动平均模型
- rsocket EWMA