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authorMatthewLi <matthew.lijun@huawei.com>2016-02-25 02:05:57 -0500
committerHai Liu <hai.liu@huawei.com>2016-02-25 07:24:07 +0000
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tree6b4666cf62d798bb17b60034a88952afe1525542 /docs/prediction_docs/Section_1_Use_cases_and_scenarios.rst
parent2146927ccbcf5ae03d69b0463b5b1c17a6183925 (diff)
rewrite document format to satisfy sphinxbrahmaputra.1.0stable/brahmaputra
JIRA: PREDICTION-16 Change-Id: I46eef324425ad1058125669e54ac6c9a4b3f8f2e Signed-off-by: MatthewLi <matthew.lijun@huawei.com> (cherry picked from commit a4669a5eff3b0866f9f15346a5a0d4d81af409d7)
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+=========================
+1 Use cases and scenarios
+=========================
+
+Telecom services often have high available requirements. Failure prediction is one of the importance features
+for high available requirements. Operator can handle faults in advance based on failure prediction.
+This project focuses on data collection of failure prediction.
+
+The data collector consists of Ceilometer and Monasca which can be extended to plugin some other open source data collectors,
+e.g. Zabbix, Nagios, Cacti. Based on real-time analytics techniques and machine learning techniques,
+the failure predictor analyses the data gathered by the data collector to automatically determine whether a failure will happen.
+If a failure is judged, then the failure predictor sends failure notifications to the failure
+management module (e.g. the Doctor module), which could handle these notifications.
+
+Use case 1
+==========
+
+Based on infrastructure metrics, it is possible to predict failure of infrastructure, e.g. Nova, Neutron, MQ.
+
+Use case 2
+==========
+
+Based on metrics of infrastructure and VM inside, it is possible to predict failure of VNF.