mesosphere / serenity Goto Github PK
View Code? Open in Web Editor NEWIntel:Mesosphere oversubscription technologies for Apache Mesos
License: Other
Intel:Mesosphere oversubscription technologies for Apache Mesos
License: Other
From Heracles paper we can learn that scheduling Best Effort tasks while having utilization over 85% on host is non-optimal.
Instead of hardcoding configuration parameters (even with a separated cpp file), we can take those directly from the modules json to Mesos (which let's you pass parameters in key value pairs)
Using libprocess we could improve our components and consider them as seperate actor. As a result each component can work in parallel.
We need to have ResourceUsage class wrapped in our class to elegantly add some features:
SyncConsumer functionality should be implemented in Filter class.
Our throttled BE tasks can still interfere with LC PR task because they still can be scheduled for a short while by Linux Scheduler. They could create ctx switching and interfere with PR. (There could be no IPC drop, however interference appears)
Mitigations:
We should make some experiments on how does BE task cntx switches affects LC task on different levels of PR utilization and amount of CPU-stress BE tasks.
Currently QoS controller revokes only BE tasks, so observers should only raise interference signals when BE tasks exists.
As is, the valve filters allow any value to be set and matches on "true". We should return an error if the value is malformed.
Current moving average filter code (ema.cpp/ema.hpp) is a mix of "base" filter class and "exponential moving average" algorithm.
We should refactor the code:
When this is done, all instances of setEmaIps, getEmaIps, setEmaCpuUsage etc. should be removed.
tbd
Add code coverage badge, eg. from Coveralls.
Valve filter should accept StopOversubscription message that will stop estimation of slack resources.
ResourceUsage in Serenity is currently Mesos protobuffer message.
We should extend it and add some features:
Observe for a network dropped packets increase and trigger action based on that.
ChangePointDetector : Object responsible for detecting drops in given stream of "doubles" using different algorithms.
Observer : Observer should rather produce signals/suggestions. It does not produce QoS corrections. It's rather "As an observer, I observe degradation of CPI metric, which could be a result of resource contention, so I raise a ResourceContention signal" or "As an observer I see this amout of slack".
Decider : "As a RE decider I decide to expose this amount of observed slack to Master"
"As a QoS decider I decide to kill these executors because of this contention signal"
We would like to know your opinion. For us (PL) it is understandable, however you, native speakers, could have opposite feeling.
The initial idea was to make this pipeline flexible and fast. Let's try to introduce parallel way to run pipeline.
Proposed ideas:
Hello,
I have built Serenity with mesos-1.3.0 but when I try to load the libserenity.so to the agent I get the following error: undefined symbol: _ZNK6google8protobuf7Message13SpaceUsedLongEv
Is it actually possible to build with mesos-1.3.0 or I have to use mesos-0.27.x?
s/TooHighUtilizationDetector/OverloadDetector
s/IPCSignalDropDetector/IPCDropDetector
Serenity needs to add guarantees to QoS controller. We need to make sure, that revocation of BE tasks will last until:
a) Signal that raised interference returned back to normal
b) All BE tasks are revoked
Logic that has to be inside controller:
Track perf events for only the high-priority (PR) tasks.
We should design how serenity filters will behave in case of any error during pipeline flow.
Some definitions:
Continuing pipeline -> run produce() and pass some form of ResourceUsage further.
Abort pipeline -> return Nothing() and wait for next usage.
The are plenty issues here:
WID class are redundant and it's occurrences should be removed. Whenever they were useful, they should be changed to ExecutorInfo message.
We have tested most of our components using unit tests, but we need to make sure that our pipelines produce results.
We few tests to check if Estimator returns slack when:
And for QoS Controller:
#149 Implemented std::vector consumables() function.
Because some products are vectors, some components are consumers of vector<vector>.
For easier development, we should have flattenedConsumables that will return flattened result.
Currenlty we have not flexible solution. We are reusing some double fields in ResourceUsage (e.g net statistics) for EMA puposes. For better code readability we should reuse the same fields and add some Tags in which we will write what filtering were done on given data. e.g:
Source -> RU with perf instructions,tag<> -> EMA -> RU with EMA instructions, tag<ema>
Using aforementioned mitigation enables for us the possibilty to configure pipeline even without recompling.
To elaborate about tagging, here's use case:
"As a serenity module developer I would like to register for ResourceUsage information from two different sources and I would like to know which message is from which filter (eg, raw, ema)". It's about adding distinguishable labels to non-distinguishable messages.
I'm reluctant to it, because it leads to hardcoded pipelines - but it's a idea that comes from time to time (but I haven't seen any real "i need this" yet).
Our current filter's constructors and pipelines are messy. They accept a lot of parameters, have a lot of parameters defaulted and it's hard to what do they mean. It is also hard to recognize how our pipelines are created.
I would like to propose a standardization:
Each component would expose an API:
For pipeline, we should move from 'constructor initialization list' to constructor body and create a pipeline in logical "phases" to make it easier to read and understand.
Currently, some of statistics (cpu_usage) is cumulative - that means, it’s value is sum of values since beginning of executor. Others are sampled/counted - value is sample from specific timestamp/timeframe
We should create a filter that takes cumulative values (cpu_usage) and normalizes them to sampled/counted value. This should simplify filter development/debugging because all values would be treated the same way
Use cases:
Add InfluxDB9 as a database backend.
Deprecate InfluxDB 8.
At the moment, we can loose track of how much we need to correct if corrections take long (or have been done incorrectly, taken we kill the wrong best effort tasks).
It is important when doing assurance drop contentions. We are observing signal if it's returned to normal state or not. (currently first drop), but we may have to ease on the goal over time.
To identify leaks, let's get valgrind wired up to our CI.
Currently, RE counts only slack from production executors (from observers/slack_resource.cpp).
RE should substract BE resource usage from this value (it is currently done in estimator/serenity_estimator.cpp which is not best place to do it).
Our code could be understandable easier when having similar style to Apache Mesos itself.
This lint script could be also upstreamed to Mesos then.
#149 Implemented simple tracking of products for consumption and production in components.
This should be extended to keep track of specific ID of producers, to be sure that our pipelines are properly working.
With low PR tasks CPU utilization, we will have false positives.
E.g Start Serenity with conservative policies to more liberal one when no one is affected.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.