Technologien und Mobilkommunikation Self-Healing in Self-Organising - - PowerPoint PPT Presentation

Lehrstuhl Netzarchitekturen und Netzdienste

Institut für Informatik Technische Universität München

Seminar Innovative Internet Technologien und Mobilkommunikation

Self-Healing in Self-Organising Networks Oliver Scheit

Self-Healing in Self-Organising Networks

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Self-Organising Networks

 Self-Organising Networks in Mobile Communications

• Motivation
• Self-Configuration
• Self-Optimization
• Self-Protection

 Self-Healing

• Detection
• Diagnosis
• Problems

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Motivation

 Tendencies from macro to micro/pico/femto cells in

networks

• To overcome the bandwidth problems
• More and smaller cells needed for coverage
• Paradigm change for management purposes:
• Network configuration:

– Move from centralized planning and configuration to decentralized Self Organizing Networks

• Network maintenance and optimization:

– Cells autonomously gather information and provide it to a management system – This cannot be done manually by personnel any longer

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Motivation

 Heterogenous networks with GSM, HSPA, LTE

• Interference

 Knowledge of the network often only by experience

• Dependency on individual experts

 Typical operater use cases:

• Planning
• Deployment
• Optimisation
• Maintenance
• Find a cost efficient way to manage the network tasks

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Self-Management Domains

• Error detection

and recovery

• Protection

against attacks

• Faulty
• peration
• Resource

monitoring

• Parameter

tuning

• Network

planning

• Configuration
• f network

parameters

Self- Configurati

• n

Self- Optimisatio n Self-Healing Self- Protection

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Self-Configuration

 System can add and configure new elements/features in

run-time

• Reduce time until first operation
• Improves deployment of new network elements

 Automatically install new software  Reduce human involvement

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Self-Optimisation

 Improve efficiency of the network over manual

configuration

 Adapting to changing environment

• Handover parameters, cell-individual parameters
• Neighbor cell list

 Monitor the system parameters

• Automated parameter tuning

 Mobility Load-Balancing

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Self-Healing

 Detection

• Sleeping cells
• Performance indicators

 Diagnosis

• Use network knowledge
• Prevent false alarms
• Recover/reduce impact on network performance

 Cell Outage Compensation

• Reconfigure neighbor cells to compensate cell outage

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Network Architecture

Network Element Domain Management Network Management

NM DM NE NE DM NE

Where to implement which SON function ?

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Challenges of SON-Architecture

 Centralised vs distributed functions

• Processing power vs bandwidth
• Centralised Decision making (more knowledge)

vs distributed (deadlocks, scaling)

• Reliability & availability
• Management

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Self-Healing

 Mobile networks are large, complicated systems

• Prone to faults and inefficient behaviour

 Most critical part is the Radio Access Network

• Little to no redundancy in base stations
• No service for users if one station fails completely

 High amount of network elements

• Degradation possible in each element
• Identification of root cause for triggered alarms is hard

 Often requires manual troubleshooting

• Long time period of degradation
• Significant costs

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3GPP Self-Healing

 Use-cases derived by 3rd Generation Partnership Project

(3GPP)

• Self-recovery of NE-software
• Self-healing of board faults
• Cell Outage Detection
• Cell Outage Recovery
• Cell Outage Compensation
• Return of Cell Outage Compensation

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3GPP Self-Healing Process

 Self-Healing process

• Input monitoring checks for pre-defined conditions,

triggers self-healing process

• Self-healing process gathers additional system

information

• Diagnosis of the root cause with the provided

information

• If root cause can be solved automatically:
• recovery action performed
• Back up configuration data
• Evalution of the new state
• Attempt new self-healing iteration
• Report the result of the self-healing process
• Fall back to backup configuration

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Cell Degradation Management

 Detection of Cell degradations partly automated

• Check for alarms and faults
• Reset the cell
• After too many resets, operational personnel investigates the cell
• If it can‘t be fixed remotely, engineers investigate on site
• Often multiple visits requiered

 KPI based detection

• Investigate key performance indicators (KPI)
• E.g. Top 10 cells with dropped calls, change of dropped call rate
• KPIs often indicate external influences like interference
• Faults also typically related to hardware

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Cell Degradation Detection

 Detecting unusual behavior of performance indicator

• No diagnosis yet

 Decide if performance is „healthy“  Performance indicators aquired from

• Base station
• User equipment
• Neighbor cells
• Core network elements

 KPIs are stochastic variables

• Absolute decision what is „healthy“ or not impractical
• Use profiles to evaluate operation

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Profiles

 Absolute Threshold

• Indicator should not exceed or fall below certain threshold

[1] Figure 1: absolute Threshold

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Profiles

 Statistical

• Values that should be around the statistical mean of the

indicater +/- a threshold defined by the operator

[2] Figure 2: statistical thresholds

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Profiles

 Time dependend

• Indicators fluctuate time dependend, usually these are

dependend of user behaviors

[3] Figure 3: absolute Threshold

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Cell Degradation Detection

 The performance of the Detection method can be

evaluated by

• The delay between a degradation and its detection
• The accurancy false postive/false negative
• The processing Overhead

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Cell Degradation Diagnosis

 Diagnosis usually done manually  Requires knowledge of typical symptom-cause relation

• This is often refered to as expert knowledge

 SONs have to map the observations to the most possible

root causes similar to a way a human solves this problem

 different approaches to this problem possible

• Rule based systems
• Bayesian Networks
• Case based reasoning
• Neural Networks

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Rule Based Systems

 If(a)-then(b) style of implementation  Easy to implement  Easy to train/add new rules  Only effective in small deterministic networks  Complex networks require a complex set of rule  Uncertainty to performance indicators

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Bayesian Networks

 Includes the uncertainty of the performance indicators into

the analysis of the root cause

 Based on Bayes Theorem P(A|B) = (P(B|A)*P(A)) / P(B)  Can be depicted as an acyclic graph with nodes on

different hierarchies

• Conditions (global properties)
• Root causes
• Sypmptoms

 Weighted edges represent the probability that a fault

causes an observed symptom

 Can be build top down with knowledge of the operator  Can evolve over time, starts with limited knowledge

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Bayesian Networks

Condition Root Cause Symptom Symptom Root Cause Symptom

 Faults are rare, their statistical distribution hard to define  Size of conditional probability table grows exponentially with the number of nodes in the network

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Case Based Reasoning

 Map KPI anomalies to Fault cases

• Initially requires expert knowledge

 To solve a new problem, knowledge based on the similarity

to other already known faults

 If the fault could be resolved, add this new fault with the

corresponding KPI mapping to the database

 If it can‘t be solved, manual troubleshooting is done  The new case will be added to the knowledge base  CBR does not rely on probability

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Conclusion

 SON are an efficient way to deal with increasingly complex

networks

 SON Domains cover deployment, configuration and self-

healing of network elements

 The SON Task can be done centralized by an higher

Network Management element or decentralized by the cells themselves

 Detection is linked to alarms and performance indicators

• Dependent on probabilities

 Finding the root cause of a problem is a machine learning

problem

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Sources

 Christoph Frenzel, Tsvetko Tsvetkov, Henning Sanneck, Bernhard

Bauer, and Georg Carle. Detection and Resolution of Ineffective

• Function. Behavior in Self-Organizing Networks. In IEEE International

Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), Sydney, Australia, June 2014.

 Chris Johnson. Long Term Evolution IN BULLETS. Edition 2, version 1.

ISBN 9781478166177. Amazon Distribution GmBH, Leipzig, 2012

 [1][2][3]Seppo Hämäläinen, Henning Sanneck, Cinzia Sartori. LTE Self-

Organising Networks (SON): Network Management Automation for Operational Efficiency. ISBN 9781119970675. John Wiley & Sons, 2012.

 Péter Szilágyi, Szabolcs Nováczki. An Automatic Detection and

Diagnosis Framework for Mobile Communication Systems .IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT,

• VOL. 9, NO. 2, JUNE 2012.

 Szabolcs Nováczki. An Improved Anomaly Detection and Diagnosis

Framework for Mobile Network Operators

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QUESTIONS ?

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Many Thanks for your attention !