From 474e4b89171013565647369c6f7b6bcd6430af82 Mon Sep 17 00:00:00 2001 From: Daisuke Fujita Date: Fri, 25 Jan 2019 04:11:07 +0000 Subject: [PATCH] Update README Currently, README has invalid markup which will not be rendered on PyPI. This patch updates README to be rendered on PyPI and to be more readable. Change-Id: Ic081c0729e44f4e09ac21c47a5dbbd0da6deb77f --- README.md | 25 +++---------------------- doc/use_cases.md | 20 ++++++++++++++++++++ 2 files changed, 23 insertions(+), 22 deletions(-) create mode 100644 doc/use_cases.md diff --git a/README.md b/README.md index 29e3d72..991d920 100644 --- a/README.md +++ b/README.md @@ -23,37 +23,18 @@ we can speed up the extraction of actionable infrastructure insight. ### Advantages :thumbsup: Decouple algorithm design from execution. -:thumbsup: Reusable specification of the desired -[flow](doc/design.md#flow). +:thumbsup: Reusable specification of the desired [flow](doc/design.md#flow). :thumbsup: Language independent [flow](doc/design.md#flow) definition. :thumbsup: Data source and format independent. -:thumbsup: Easy to add new [SML](doc/design.md#sml) algorithms and # -combine them with pre-existing ones in the [flow](doc/design.md#flow). +:thumbsup: Easy to add new [SML](doc/design.md#sml) algorithms and # combine them with pre-existing ones in the [flow](doc/design.md#flow). :thumbsup: Transparently exploit data parallelism. ### Example Use Cases -Below are few use cases that are relevant to OpenStack. However, MoNanas -enables you to add your own [data ingestors](doc/dev_guide.md#ingestors). - -| Example | Alert Fatigue Management | Anomaly Detection | -|:------------------------------|:-------------------------|:------------------| -| **Dataset** | Synthetic, but representative, set of Monasca alerts that are processed in a stream manner. This alert set represents alerts that are seen in a data center consisting of several racks, enclosures and nodes. | `iptables` rules together with the number of times they are fired in a time period. | -| **Parsing** | Monasca alert parser. | Simple parser extracting period and number of fire events per rule. | -| **SML algorithm flow** | `filter(bad_formatted) -> filter(duplicates) -> aggregate() >> aggregator` aggregation can utilize conditional independence causality, score-based causality, linear algebra causality. | `detect_anomaly() >> aggregator` anomaly detection could be based on SVM, trend, etc. | -| **Output** | Directed acyclic alert graph with potential root causes at the top. | Rule set with an anomalous number of firing times in a time period. | -| **:information_source: Note** | Even though this could be consumed directly by devops, the usage of [Vitrage MoNanas Sink](doc/getting_started.md#vitrage_sink) is recommended. The output of this module can speed up creation of a [Vitrage](https://wiki.openstack.org/wiki/Vitrage) entity graph to do further analysis on it. | None. | - -`->` indicates a sequential operation in the flow. - -`//` indicates beginning of group of operations running in parallel. - -`-` indicates operations running in parallel. - -`>>` indicates end of group of operations running in parallel. +* [MoNanas/UseCases](doc/use_cases.md): Use cases that are relevant to OpenStack ### Documentation * [MoNanas/GettingStarted](doc/getting_started.md): A starting point for users diff --git a/doc/use_cases.md b/doc/use_cases.md new file mode 100644 index 0000000..6134a25 --- /dev/null +++ b/doc/use_cases.md @@ -0,0 +1,20 @@ +# Example Use Cases + +Below are few use cases that are relevant to OpenStack. However, MoNanas +enables you to add your own [data ingestors](doc/dev_guide.md#ingestors). + +| Example | Alert Fatigue Management | Anomaly Detection | +|:------------------------------|:-------------------------|:------------------| +| **Dataset** | Synthetic, but representative, set of Monasca alerts that are processed in a stream manner. This alert set represents alerts that are seen in a data center consisting of several racks, enclosures and nodes. | `iptables` rules together with the number of times they are fired in a time period. | +| **Parsing** | Monasca alert parser. | Simple parser extracting period and number of fire events per rule. | +| **SML algorithm flow** | `filter(bad_formatted) -> filter(duplicates) -> aggregate() >> aggregator` aggregation can utilize conditional independence causality, score-based causality, linear algebra causality. | `detect_anomaly() >> aggregator` anomaly detection could be based on SVM, trend, etc. | +| **Output** | Directed acyclic alert graph with potential root causes at the top. | Rule set with an anomalous number of firing times in a time period. | +| **:information_source: Note** | Even though this could be consumed directly by devops, the usage of [Vitrage MoNanas Sink](doc/getting_started.md#vitrage_sink) is recommended. The output of this module can speed up creation of a [Vitrage](https://wiki.openstack.org/wiki/Vitrage) entity graph to do further analysis on it. | None. | + +`->` indicates a sequential operation in the flow. + +`//` indicates beginning of group of operations running in parallel. + +`-` indicates operations running in parallel. + +`>>` indicates end of group of operations running in parallel.