From 83e8365f0e81b8eae5a1bd1f59e3901db82548ec Mon Sep 17 00:00:00 2001 From: Kamil Rykowski Date: Thu, 9 Apr 2015 12:55:23 +0200 Subject: [PATCH] Cosmetic changes for system architecture docs During getting through the "System Architecture" documentation I've hit some cosmetic issues which would be nice to have in our codebase. Change-Id: I55179d91c95e215f7b4b05b9a19245faa42f0661 --- doc/source/architecture.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/source/architecture.rst b/doc/source/architecture.rst index b8634c267..803a5e801 100644 --- a/doc/source/architecture.rst +++ b/doc/source/architecture.rst @@ -25,7 +25,7 @@ High-Level Architecture An overall summary of Ceilometer's logical architecture. Each of Ceilometer's services are designed to scale horizontally. Additional -workers and nodes can added depending on the expected load. Ceilometer offers +workers and nodes can be added depending on the expected load. Ceilometer offers five core services, the data agents designed to work independently from collection and alarming, but also designed to work together as a complete solution: @@ -85,7 +85,7 @@ Notification Agents: Listening for data The heart of the system is the notification daemon (agent-notification) which monitors the message bus for data being provided by other OpenStack components such as Nova, Glance, Cinder, Neutron, Swift, Keystone, -and Heat +and Heat. The notification daemon loads one or more *listener* plugins, using the namespace ``ceilometer.notification``. Each plugin can listen to any topics, @@ -152,7 +152,7 @@ Pipeline Manager :align: center :alt: Ceilometer pipeline - The assembly of components making the Ceilometer pipeline + The assembly of components making the Ceilometer pipeline. Ceilometer offers the ability to take data gathered by the agents, manipulate it, and publish it in various combinations via multiple pipelines. @@ -166,7 +166,7 @@ Transforming the data :alt: Transformer example Example of aggregation of multiple cpu time usage samples in a single - cpu percentage sample + cpu percentage sample. The data gathered from the polling and notifications agents contains a wealth of data and if combined with historical or temporal context, can be used to