Changes between Version 8 and Version 9 of OperationalMonitoring/Overview


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Timestamp:
03/17/14 14:28:50 (10 years ago)
Author:
rirwin@bbn.com
Comment:

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  • OperationalMonitoring/Overview

    v8 v9  
    55== Introduction ==
    66
    7 The monitoring architecture is based on the concept of distributing sources of information in a common fashion.  The sources of information (relational data or time-series data) is placed into what are called "Local Datastores".  These datastores have a common REST polling API for retrieving information.  The component that automatically retrieves data from the Local Datastores are called "Aggregators".
     7The monitoring architecture is based on the concept of distributing sources of information in a common fashion.  The sources of information (relational data or time-series data) is placed into what are called "Local Datastores".  These datastores have a common REST polling API for retrieving information.  The component that automatically retrieves data from the Local Datastores are called "collectors".
    88
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    1414       <td> <img src="http://groups.geni.net/geni/attachment/wiki/OperationalMonitoring/Overview/aggregator_local_datastore_overview.png?format=raw" width="500" height="317"> </td>
    1515  </tr>
    16   <caption align="bottom"> <b> Architectural Highlight of an Aggregator and a Local Datastore </b></caption>
     16  <caption align="bottom"> <b> Architectural Highlight of an collector and a Local Datastore </b></caption>
    1717</table>
    1818}}}
    19 An aggregator can poll a variety of datastores and any subset of data within each datastore.  A monitoring application relies on an aggregators for its data queries, so aggregators poll whatever data is necessary to have sufficient data to support the attached monitoring applications (i.e., alerting, reporting, historical analysis, visualization). 
     19An collector can poll a variety of datastores and any subset of data within each datastore.  A monitoring application relies on an collectors for its data queries, so collectors poll whatever data is necessary to have sufficient data to support the attached monitoring applications (i.e., alerting, reporting, historical analysis, visualization). 
    2020
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    3131
    32 Although there are multiple aggregators, a single monitoring application uses only a single aggregator.
     32Although there are multiple collectors, a single monitoring application uses only a single collector.
    3333
    3434== Use Cases ==
     
    3838Use case description: Track node compute utilization, interface, and health statistics for shared rack nodes, and allow operators to get notifications when they are out of bounds
    3939
    40 Use case implementation story: Node statistics are time-series data, and are either collected on the node and pushed to the compute aggregate, or polled from each node by the compute aggregate (doesn't matter for our purposes). Statistics end up in a local database on each rack. Any group of operators that wants to send notifications on these statistics runs an aggregator, which polls all racks of interest to that group. The aggregator shares current values with an alerting service, which sends alerts.
     40Use case implementation story: Node statistics are time-series data, and are either collected on the node and pushed to the compute aggregate, or polled from each node by the compute aggregate (doesn't matter for our purposes). Statistics end up in a local database on each rack. Any group of operators that wants to send notifications on these statistics runs an collector, which polls all racks of interest to that group. The collector shares current values with an alerting service, which sends alerts.
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    5555Use case description: Find out what slivers will be affected by a maintenance or outage of some resource, and get contact information for the owners of those slivers so targeted notifications can be sent
    5656
    57 Use case implementation story: Aggregates collect up-to-date information about what slivers exist and what resources they have reserved (including sliver details such as expiration time), and make this information available via a local datastore. GENI trust authorities (e.g. clearinghouses) collect up-to-date information about experimenters and their contact information, and make this information available via a local datastore. Operators who want to be able to get this information run an aggregator which can query the relevant datastores (since this is an on-demand real-time query, the aggregator doesn't need to be active all the time, though it may be). The aggregator data is used to run a report listing affected experimenters and their contact info.
     57Use case implementation story: Aggregates collect up-to-date information about what slivers exist and what resources they have reserved (including sliver details such as expiration time), and make this information available via a local datastore. GENI trust authorities (e.g. clearinghouses) collect up-to-date information about experimenters and their contact information, and make this information available via a local datastore. Operators who want to be able to get this information run an collector which can query the relevant datastores (since this is an on-demand real-time query, the collector doesn't need to be active all the time, though it may be). The collector data is used to run a report listing affected experimenters and their contact info.
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    5959#!html