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www.rizotec.com
IPA
Bridging the Gap between Fault and Performance Management
IPA Bridging the Gap between Fault and Performance Management - - PowerPoint PPT Presentation
IPA Bridging the Gap between Fault and Performance Management - - PowerPoint PPT Presentation
IPA Bridging the Gap between Fault and Performance Management www.rizotec.com What do mobile operators need to do? Monitor their networks carefully Understand growth trends in their network Identify anomalies across the network in real-time
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What do mobile operators need to do?
Monitor their networks carefully Understand growth trends in their network Identify anomalies across the network in real-time Locate problems before they start affecting the network
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Fault Management Systems Offer comprehensive visibility to the health status of network elements Answers simple binary questions (fault / no fault) Output: Real-time alerts Drawback: Alerts only on fault (no trends/anomalies, no performance)
Current solutions
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Performance Management Systems Enable managers to prepare the network for the future and determine its efficiency Uses complex KPIs to identify trends and anomalies in the network Based on historical data Output: performance report Drawback: not in real-time, no alerts
Current solutions
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The Gap Identify problems in real-time before they occur Alerts based on complex performance indicators Real-time trends and anomalies
The Gap Between Fault and Performance Management
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The Gap Between Fault and Performance Management
Historical Reports Trends
PERFORMANCE
Alerts Fault / No Fault Real-time
FAULT
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Bridging the Gap
IPA
Performance Anomalies
Historical Reports Trends
PERFORMANCE
Alerts Fault / No Fault Real-time
FAULT
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Bridges the gap between fault and performance management Identify performance deterioration Near real-time alerts based on compound performance data analysis Identifying problems before they start affecting the network Reduce and prevent downtime
IPA - Intelligent Performance Analyzer
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Technology
IPA collects large amounts of data from various sources (OSS, BSS, CRM, DWH…) Real-time analysis Multi-dimensional model
> Entity > Time > Configuration
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How it Works?
Connect to data sources Define KPIs and Rules Tuning Deploy real-time analysis
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IPA Studio
Loader Builder Defines the connection to the various data sources in order to replicate the data to IPA’s internal database
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IPA Studio
KPI Builder Defines the KPI’s that will be used in the performance data analysis.
> Nominal KPI’s > Historical KPI’s > Time aggregated KPI’s > Configuration aggregated KPI’s
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Rules Builder Rules are created by using the KPI’s to define logical conditions for raising alarms.
> Static threshold > Dynamic threshold > Configuration threshold
IPA Studio
An alarm can be a collection of multiple thresholds
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Alarms Builder Defines when to run the rules and where to send the alarms.
> SNMP > Email > SMS
IPA Studio
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Simulator
> Before deploying a rule it can be
tested and fine-tuned on historical data.
> Optimization of existing rules. > Try and go, Try and change, Fine-
tuning, Optimization
IPA Studio
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Runtime
Data Loader
> Connects to the various data sources
at predefined intervals
> Replicate the data to IPA’s internal
database
> Independent, non-intrusive
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Runtime
Real-time Analyzer
> Rules Engine – Analyzes in real-time
the constant stream of data, based on the predefined rules
> Alarms Engine - Alarms are raised
based on the rules, while notifying the relevant stakeholders
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Runtime
Reporter
> Generates textual and graphical
reports based on the KPIs
> Predefined reports > User-defined reports
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The goal locate technical problems in the network by identifying abnormal patterns in the release rate of session. The Rule comparison between abnormal release rate during the last 15 minutes to the average on the same period (e.g. Monday 09:45 AM) during the last 4 weeks. Alarm will be sent after the rule is triggered 3 times in a row.
Example - Abnormal Release Rate
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Example - Abnormal Release Rate
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Results
> On average each month between 15 and 20 customers problems
were located by IPA
> Early identification allowed the MVNO to fix the problems. > $2500 on average saved each month for uncharged traffic.
Example - Abnormal Release Rate
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Example – Traffic Degradation
The goal to locate technical problems in the network by identifying abnormal capacity degradation. The Rule identify a decrease in traffic of over 30% by comparing real-time traffic rate and base station’s average for the past 10 weeks. Alarm is raised
- n a certain threshold.
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Example – Traffic Degradation
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Results
> Prevented decrease in consumption > Reduced the length of such problems by 80%
Example – Traffic Degradation
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The goal to identify non-optimal roaming configuration on cellular devices in order to
- ptimize configuration (increase preferred network usage).
The Rule identify customers with a total usage more than x MB in a unit time (for example 1 hour) and more than y% of their traffic served by non-preferred
- perators
Example – Non-optimal Configuration
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Example – Non-optimal Configuration
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Example – Non-optimal Configuration
Results
> Corrected configuration reduces the end-customer
costs and at the same time raises the MVNO’s profit.
> Approximately 1.5% of roaming usage is corrected > on average $Saved 1 per event
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> Connects to DB/Data warehouse, Files, etc. > GUI-based rules builder (no developers needed) > Real-time analysis of compound performance data > Real-time alarms based on complex rules > Real-data simulator > Graphical and textual reports
Summary
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