Adaptive capacity management (RateLimiter + BulkHead) decorator #201

RobWin · 2018-02-06T12:43:33Z

See concept: https://www.youtube.com/watch?v=m64SWl9bfvk

storozhukBM · 2018-02-11T16:52:58Z

Implementation thoughts.

As Jon Moore mentioned in his talk we will can increased latency as implicit indication for adaptive concurrency adjustments.

Adaptive capacity management prerequisites

You should have a system with relatively stable response latency, because we will use latency measures to adapt concurrency limits.
To configure you system properly you should figure out two things:
2.1 Desirable average throughput per second (later X) [Example: 30 req/sec]
2.2 Desirable average request latency in seconds per operation (later R) [Example: 0.1 sec/op]
2.3 Maximum acceptable request latency (later Rmax). This number should be set wisely, because it can eliminate all adaptive capabilities, system will do its best to never reach such latency, so you can set it 20-30 % higher than your usual average latency.

Implementation

Resilience4j will provide new Bulkhead implementation called AdaptiveBulkhead. It will have following config params:

Desirable average throughput = X
Desirable request latency = R
Maximum acceptable request latency = Rmax (default R * 1.3)
Window duration for adaptation = Wa (default 5 sec)
Window duration for reconfiguration = Wr (default 900 sec)

From this params we will calculate:

Initial average number of concurrent requests (later N).
Example: N = X * R = 30 * 0.1 = 3 [op]
Initial max latency of current window (later cRmax).
Example: cRmax = min(R * 1.2, Rmax) = min(0.1 * 1.2, 0.13) = 0.12 [op]
Size of adaptation sliding window (later WaN)
Example: WaN = Wa * X = 30 * 5 = 150
Size of reconfiguration sliding window (later WrN)
Example: WrN = Wr / Wa = 900 / 5 = 180

From now on we will have two separate functions working inside AdaptiveBulkhead:

First function is `constantAdaptation`

It will constantly measure request latencies and add them to adaptation sliding window.
After each cycle with duration of Wa, constantAdaptation function will calculate current mean latency from adaptation window [cR]. Add this cR to reconfiguration sliding window. Then it will compare cR with cRmax.
If [cR] < cRmax constantAdaptation function will raise number of concurrent requests N by one, otherwise it will multiply N by 0.75.
Also it will use cR to calculate max permission wait time as R - cR.
So for next cycle Bulkhead will run with new params.

Second function is `configurationAdjustment`

This function will be triggered once per Wr and it will calculate standard deviation of latencies from reconfiguration sliding window and will try to recalculate new cRmax.
cRmax = min(cR + standard deviation of reconfiguration window, Rmax)`

This configurationAdjustment will help us handle daily latency changes gracefully without reaching cRmax often.

storozhukBM · 2018-02-13T15:15:08Z

@RobWin if you don't have any concerns I'll proceed with implementation.

RobWin · 2018-02-13T15:44:39Z

Can we implement this with a reactive streaming library like Reactor or RxJava?
We consume events from our existing RateLimiter or Bulkhead and reconfigure them at runtime?

storozhukBM · 2018-02-13T15:49:45Z

Yes, I think it is possible, but such implementation will have some performance drawbacks and will create runtime dependency from streaming library.

RobWin · 2018-02-13T15:50:14Z

I'm looking forward to your implementation 👍

storozhukBM · 2018-03-27T20:00:54Z

Graph from the first integration test

The main idea is working. Now I'll polish the implementation and make additional performance and integration tests.

RobWin · 2019-03-21T21:22:16Z

Can we progress with this?

RobWin · 2019-03-22T11:30:03Z

I think the multiplicative-decrease factor and additive-increase value should also be configurable.

RobWin · 2019-03-22T11:36:14Z

AIMD requires a signal of congestion. Most frequently, response time outs or bad response times serve as an implicit signal.
Do you think it also makes to check the content of successful responses if the server explicitly signals that the client should back off?

storozhukBM · 2019-03-25T20:58:22Z

Yep, we can provide external API to trigger back off, so our users can use any other type of analysis pluggable or written by hand.

liangGTY · 2019-08-06T10:21:11Z

hi, @RobWin I would like to ask if there is any progress, when will it be released?

RobWin · 2019-08-06T10:22:07Z

Yes, there is progress. But not sure when it's ready to be released.

RobWin · 2020-10-06T11:25:25Z

Our AdaptiveBulkhead implements the TCP Congestion Avoidance algorithm in a protocol-agnostic way.
Instead of a Congestion Window Size, we have a Concurrency Limit.

TCP uses a mechanism called slow start to increase the congestion window after a connection is initialized or after a timeout. It starts with a certain window size. Although the initial rate is low, the rate of increase is very rapid; for every packet acknowledged, the congestion window increases by 1 MSS so that the congestion window effectively doubles for every round-trip time (RTT).

Our AdaptiveBulkhead is a state machine with two states: SLOW_START and CONGESTION_AVOIDANCE.
The user can configure:

Initial Concurrency Limit
Slow Start Concurrency Threshold
Minimum Concurrency Limit
Maximum Concurrency Limit
Increase Summand (default: 1)
Increase Multiplier (default: 2)
Decrease Multiplier (default: 0.5)

In the SLOW_START state the AdaptiveBulkhead will increase the concurrency limit for every successfuly call exponentially (by using "Increase Multiplier") until the slow-start threshold is reached, which is the "Slow Start Concurrency Threshold", then it enters the CONGESTION_AVOIDANCE state. In the CONGESTION_AVOIDANCE state the concurrency limit is increased linearly (by using "Increase Summand").

The AdaptiveBulkhead uses the same Sliding Window implementation like the CircuitBreaker to track successful/failed calls. The Sliding Window is used to calculate a failure rate (and slow call rate) based on the last N calls or calls of the last N seconds. The failure rate increases when certain exceptions occur, e.g. TimeoutException, IOException or TooManyCallsExceptions. If the failure rate (or slow call rate) threshold is exceeded, the AdaptiveBulkhead will multiplicative-decrease the concurrency limit and reset the Sliding Window.

If the Minimum Concurrency Limit is reached, it will enter SLOW_START state again.

Now we need to map this to the AdaptiveRateLimiter ;)
You are right, the AdaptiveRateLimiter shoud not multiplicative-decrease the rate limit on every failed call. But the RateLimiter is currently not tracking a failure rate. What is your suggestion without chaning the existing RateLimiter too much?

adwsingh · 2020-10-08T08:10:03Z

Hi are we also planning to cover circuit breaker closing using AIMD strategy here? The circuit breaker would gradually move from OPEN -> HALF_OPEN -> CLOSED based on AIMD.

RobWin · 2020-10-08T08:16:55Z

Not sure if an adaptive CircuitBreaker is really nessecarry, if there is an AdaptiveBulkhead based on AIMD. I have the feeling that the changes required in the CircuitBreaker would be too many.

We could rather need support to implement the AdaptiveBulkhead.
OPEN -> Concurrency Limit = 0
HALF_OPEN is SLOW_START phase
CLOSED is CONGESTION_AVOIDANCE phase

If this doesn't match your requirements, we should implement an AdaptiveCircuitBreaker, but not touch the existing too much.

adwsingh · 2020-10-08T12:06:52Z

Makes sense. For Adaptive Bulkhead, is the implementation done or can I help in building it?

Issue #201 adaptive bulkhead config and time fixed

* Issue #201 EventPublisher hierarchy fixed * Issue #201 onResult added to AdaptiveBulkhead * Issue #201 states extracted to own files

scott-deboy · 2023-11-29T21:52:04Z

Hey folks - this could really be useful for a project I'm involved with at work.

Could someone share what work is outstanding in order to consider this complete and get it merged? I may be able to contribute to the effort, or get others involved.

…DecreasedEvent merged into one event

* Issue resilience4j#201 EventPublisher hierarchy fixed * Issue resilience4j#201 onResult added to AdaptiveBulkhead * Issue resilience4j#201 states extracted to own files

…4j#1995)

…ilience4j#2001)

…nce4j#2007)

…DecreasedEvent merged into one event

* Issue resilience4j#201 EventPublisher hierarchy fixed * Issue resilience4j#201 onResult added to AdaptiveBulkhead * Issue resilience4j#201 states extracted to own files

…4j#1995)

…ilience4j#2001)

…nce4j#2007)

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Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Fixed Issue #2040: Micronaut BulkheadInterceptor always uses the "default" configuration in micronaut application * Support Micronaut 4 (#1951) * FallbackMethod supports AOP (#2058) * Add AOP in FallbackMethod * Add test * Fixed the fallback method bug --------- Co-authored-by: 임수현 <soohyun.lim2@cj.net> * Support class name using SpEL expression at Circuitbreaker annotation name field. (#2053) * Issue #1565: Do not retry if IntervalFunction returns interval less than 0. Emit correct events based on result of IF. 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(#844) * Issue #596: The Spring Boot fallback method is not invoked when a BulkheadFullException occurs #847 The ThreadPoolBulkhead is does not return a CompletionStage when a task could not be submitted, because the Bulkhead is full. The ThreadPoolBulkhead throws a BulkheadFullException instead. Analog to the ThreadPoolExecutor which throws the RejectedExecutionException. The BulkheadFullException is not handled correctly by the BulkheadAspect. 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rchinmay · 2024-05-21T20:49:48Z

Hello guys, this seems to be in progress since a very long time. I can join in and help if necessary. Can someone please point out to the current branch where progress is being made, since there seems to be a lot of branches related to this feature?

RobWin added the enhancement label Feb 6, 2018

storozhukBM self-assigned this Mar 25, 2018

storozhukBM added the implementing label Mar 25, 2018

RobWin added this to the 0.14.0 milestone Jun 18, 2018

RobWin mentioned this issue Mar 21, 2019

Distributed rate limiter? #350

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Romeh mentioned this issue Jun 25, 2019

Adaptive bulkhead implementation #520

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Romeh added this to Review in progress in Release v0.17.0 Jun 25, 2019

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RobWin mentioned this issue Oct 15, 2019

New Annotation to combine different Annotations #643

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RobWin mentioned this issue Sep 23, 2020

Please advise: calculating a new rate limit based off of events during a cycle. #1177

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RobWin mentioned this issue Oct 6, 2020

Circuit Breaker Feature request: AIMD support while moving from half-open to close status #985

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Merge pull request #1922 from hexmind/adaptive_bulkhead_3.3

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Issue #201 adaptive bulkhead config and time fixed

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Adaptive bulkhead 4.1 (#1947)

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Adaptive capacity management (RateLimiter + BulkHead) decorator #201

Adaptive capacity management (RateLimiter + BulkHead) decorator #201

RobWin commented Feb 6, 2018

storozhukBM commented Feb 11, 2018

storozhukBM commented Feb 13, 2018

RobWin commented Feb 13, 2018 •

edited

storozhukBM commented Feb 13, 2018

RobWin commented Feb 13, 2018

storozhukBM commented Mar 27, 2018 •

edited

RobWin commented Mar 21, 2019

RobWin commented Mar 22, 2019

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edited

storozhukBM commented Mar 25, 2019

liangGTY commented Aug 6, 2019

RobWin commented Aug 6, 2019

RobWin commented Oct 6, 2020 •

edited

adwsingh commented Oct 8, 2020

RobWin commented Oct 8, 2020 •

edited

adwsingh commented Oct 8, 2020

scott-deboy commented Nov 29, 2023 •

edited

rchinmay commented May 21, 2024

Adaptive capacity management (RateLimiter + BulkHead) decorator #201

Adaptive capacity management (RateLimiter + BulkHead) decorator #201

Comments

RobWin commented Feb 6, 2018

storozhukBM commented Feb 11, 2018

Implementation thoughts.

Adaptive capacity management prerequisites

Implementation

First function is constantAdaptation

Second function is configurationAdjustment

storozhukBM commented Feb 13, 2018

RobWin commented Feb 13, 2018 • edited

storozhukBM commented Feb 13, 2018

RobWin commented Feb 13, 2018

storozhukBM commented Mar 27, 2018 • edited

Graph from the first integration test

RobWin commented Mar 21, 2019

RobWin commented Mar 22, 2019

RobWin commented Mar 22, 2019 • edited

storozhukBM commented Mar 25, 2019

liangGTY commented Aug 6, 2019

RobWin commented Aug 6, 2019

RobWin commented Oct 6, 2020 • edited

adwsingh commented Oct 8, 2020

RobWin commented Oct 8, 2020 • edited

adwsingh commented Oct 8, 2020

scott-deboy commented Nov 29, 2023 • edited

rchinmay commented May 21, 2024

First function is `constantAdaptation`

Second function is `configurationAdjustment`

RobWin commented Feb 13, 2018 •

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storozhukBM commented Mar 27, 2018 •

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RobWin commented Mar 22, 2019 •

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RobWin commented Oct 6, 2020 •

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RobWin commented Oct 8, 2020 •

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