Forward Acknowledgement原理

本文深入探讨了FACK算法的工作原理及其在网络拥塞控制中的应用。通过对比Reno TCP和其他算法,详细介绍了FACK如何实现更精确的数据恢复和拥塞控制。文章还分析了FACK算法在处理大量数据丢失情况下的优势。

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SACK is generally viewed as a method to address data recovery, it has not been widely investigated to
address congestion control issues.
FACK decouples the congestion control algorithms (which determine when and how much
data to send) from the data recovery algorithms (which determine what data to send).

multiple packets loss in a window
Floyd pointed out that multiple segment losses can cause Reno TCP to lose its Self-clock, resulting
in a retransmission timeout. These timeouts can cause a substantial performance degradation.
During the timeout interval, no data is sent. In addition, the timeout is followed by a period of Slow-start.
This sequence of events underutilizes the network over several round-trip times, which results in a
significant performance reduction on long-delay links.

TCP Vegas
Through the use of delay measurements, TCP Vegas attempts to eliminate the periodic self-induced
segment losses caused in Reno TCP. The Vegas Congestion Avoidance Mechanism (CAM) algorithm
modifies the "linear increase" phase of congestion avoidance.

Conservation of packets
Conservation of packets requires that a new segment not be injected into the network until an old
segment has left. This principle leads to an inherent stability by ensuring that the number of segments
in the network remains constant. Other schemes, especially rate-based transmission, can cause the
number of segments in the network to grow without bound during periods of congestion, because
during congestion the transmission time for segments increases. TCP implements conservation of
packets by relying on "Self-clocking": segment transmissions are generally triggered by returning
acknowledgements. TCP's Self-clock contributes substantially to protecting the network from
congestion.

Fast Recovery
The Fast Recovery algorithm attempts to estimate how much data remains outstanding in the network
by counting duplicate ACKs. It artificially inflates cwnd on each duplicate ACK that is received, causing
new data to be transmitted as cwnd becomes large enough. Fast Recovery allows one (halved)
window of new data to be transmitted following a Fast Retransmit.

FACK Design Goals
The requisite network state information can be obtained with accurate knowledge about the forward
most data held by the receiver. By forward-most, we mean the correctly-received data with the highest
sequence number. This is the origin of the name "forward acknowledgement."
The goal of the FACK algorithm is to perform precise congestion control during recovery by keeping
an accurate estimate of the amount of data outstanding in the network.

The FACK Algorithm
When a SACK block is received which acknowledges data with a higher sequence number than the
current value of snd.fack, snd.fack is updated to reflect the highest sequence number known to have
been received plus one.

The FACK algorithm uses the additional information provided by the SACK option to keep an explicit
measure of the total number of bytes of data outstanding in the network. In contrast, Reno and
Reno + SACK both attempt to estimate this by assuming that each duplicate ACK received represents
one segment which has left the network.
The FACK algorithm is able to do this in a staightforward way by introducing two new state variables,
snd.fack and retran_data.

TCP's estimate of the amount of data outstanding in the network during recovery is given by:
awind = snd.nxt - snd.fack + retran_data

Triggering Recovery
Reno invokes Fast Recovery by counting duplicate acknowledgements:
if ( dupacks == 3 ) {
        ...
}
This algorithm causes an unnecessary delay if several segments are lost prior to receiving three
duplicate acknowledgements. In the FACK version, the cwnd adjustment and retransmission are
also triggered when the receiver reports that the reassembley queue is longer than 3 segments:
if ( ( snd.fack - snd.una ) > (3*MSS) ) || (dupacks == 3) ) {
        ...
}

Ending Recovery
The recovery period ends when snd.una advances to or beyond snd.nxt at the time the first loss
was detected. During the recovery period, cwnd is held constant; when recovery ends TCP
returns to Congestion Avoidance and performs linear increase on cwnd.

 

性能比较

The conservative approach promoted by IETF is to consider all unacknowledged data to be
outstanding in the network. The Forward Acknowledgements algorithm takes a more
aggressive approach and considers the unacknowledged holes between the SACK blocks
as lost packets. Although this approach often results in better TCP performance than the
conservative approach, it is overly aggressive if packets have been reordered in the network,
because the holes between SACK blocks do not indicate lost packets in this case.


Due to the large number of lost segments, Reno + SACK underestimates the window during
recovery, and requires several round trip times to complete recovery.
FACK divides its window size by two, waits half of an RTT for data to exit the network, and
then proceeds to retransmit lost segments.

SACK的处理:when sender receives a dup ack, pipe = pipe - 1
然而,如果此packet前面有N个丢包呢?那么应该 pipe = pipe - N 才对。

SACK的处理,For partial acks :pipe = pipe -2 (one for original dropped packet, one for
retransmitted packet。
在恢复packet后,pipe才对其丢失做出反应,不及时。
所以说,如果在一个窗口内丢失了大量的数据包,SACK可以及时的知道丢了哪些数据包,知道
要重传哪些包。但是,pipe没跟上,pipe不能准确地反映实际情况,也就是没有剔除掉丢失的
数据包,低估了可以传输的数据量。
pipe的计算属于Fast Recovery中的拥塞控制方面。

If more than half a window of data is lost, the window estimate of Reno + SACK will not be
sufficiently accurate.

这个时候,不能精确计算pipe就显得致命了。pipe > cwnd,不能发送数据!而实际上网络中根本
就没有数据了!这会引起超时,接着用慢启动恢复,同Tahoe。
由于FACK可以精确计算pipe,所以不会出现上述问题。

 

<think>好的,我现在要处理用户关于Kafka中acks参数配置和使用的问题。首先,我需要回顾一下Kafka生产者配置中acks参数的作用和不同取值的含义。用户提到他们想了解如何正确配置和使用acknowledgments,也就是acks参数,所以我的回答需要覆盖这三个方面:参数的作用、不同值的配置方法以及最佳实践。 根据引用[1],acks参数是在生产者客户端配置的,决定了broker在什么情况下认为消息写入成功。它有三个可能的值:0、1和all。我需要详细解释每个值的影响。例如,acks=0表示生产者不等待任何确认,这可能会丢失消息,但吞吐量最高。acks=1是默认值,确保leader副本写入成功,但可能在leader故障时丢失数据。acks=all(或-1)要求所有ISR副本都确认,提供最高的持久性,但延迟增加。 接下来,用户可能需要了解如何具体配置这个参数。在Java客户端中,可以通过设置`acks`属性为字符串值。例如,`props.put("acks", "all")`。这部分需要给出代码示例,并说明不同值的配置方法,如“0”、“1”、“all”。 然后,结合引用[4]提到的分布式共识算法,如Zab和Raft,可以解释acks=all时,Kafka如何依赖ISR机制来确保数据持久化。需要提到min.insync.replicas参数的重要性,它决定了至少需要多少个ISR副本确认才能认为写入成功。如果可用副本数不足,生产者会抛出异常,这时候需要处理这些异常,比如重试或记录日志。 此外,用户可能关心性能与可靠性的权衡。acks=0适合日志收集等允许丢失的场景,而金融交易则需要acks=all。同时,需要建议用户根据实际需求调整其他参数,比如retries和max.in.flight.requests.per.connection,尤其是当acks=all时,设置retries>0和max.in.flight.requests=1可以避免消息乱序。 另外,引用[2]和[3]提到Zookeeper配置和网络超时,虽然与acks参数不直接相关,但可能需要考虑生产者的整体配置环境。例如,网络超时参数socket.timeout.ms和request.timeout.ms需要合理设置,避免因网络问题导致的生产者阻塞。 最后,确保回答结构清晰,分点说明参数详解、配置方法和最佳实践。同时生成的相关问题要围绕acks参数的影响、与其他参数的协同以及故障处理等方面,帮助用户深入理解。</think>### Kafka Acknowledgements(acks参数)配置指南 在Kafka生产者配置中,`acks`参数是控制消息持久化可靠性的核心参数[^1]。其配置直接影响数据完整性和吞吐量之间的平衡。 #### 一、参数详解 1. **acks=0** 生产者不等待broker任何确认,消息发送后立即视为成功。 *特点*:吞吐量最高,但可能丢失数据(如broker宕机时未写入磁盘)[^1]。 2. **acks=1**(默认值) 要求分区leader副本将消息写入本地日志后返回确认。 *特点*:平衡可靠性与性能,但若leader宕机且未同步到follower时仍会丢数据。 3. **acks=all/-1** 要求所有ISR(In-Sync Replicas)副本均确认写入。需配合`min.insync.replicas`参数使用。 *特点*:最高可靠性,但延迟增加且吞吐量降低[^4]。 #### 二、配置方法 在生产者客户端配置示例(Java): ```java Properties props = new Properties(); props.put("bootstrap.servers", "kafka1:9092,kafka2:9092"); props.put("acks", "all"); // 设置最高可靠性模式 props.put("min.insync.replicas", 2); // 至少需要2个ISR副本 props.put("retries", 5); // 配合重试机制增强可靠性 ``` #### 三、最佳实践 1. **关键业务场景**(如金融交易) 使用`acks=all` + `min.insync.replicas≥2`,确保至少两个副本持久化数据[^4]。 2. **日志收集等可容忍丢失场景** 使用`acks=0`或`1`以提升吞吐量,配合`linger.ms`实现批量发送优化。 3. **异常处理** 当触发`NotEnoughReplicasException`时,建议: - 增加ISR副本数量 - 检查副本健康状况 - 实现重试逻辑(需设置`enable.idempotence=true`防止重复) 4. **性能调优** - 当使用`acks=all`时,适当增加`request.timeout.ms`(默认30秒) - 设置`max.in.flight.requests.per.connection=1`避免消息乱序[^3] $$ \text{系统可靠性} \propto \frac{\text{acks级别} \times \text{ISR数量}}{\text{网络延迟}} $$
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