SlideShare uma empresa Scribd logo
1 de 25
TelecomPerformance ManagementSystem:System Description PavelLechenko pavel.lechenko@hpcms.ru October 2010 This document is licensed under CC BY.
Operators need PM system to: Predict, analyze and investigate network and service performance degradations Generate and present network and service performance reports to company management Forecast network and service performance in case of events (Exhibitions/Trade Shows, New Year, Olympic games) or new product launches Control compliance with SLA on outsourced equipment October 2010 2 TPMS: System Description
General requirements for PM system - 1 Near real-time system Support different data sources like performance counters, CDRs, probes, field/drive test results  Scalable for any volumes of input data and retention periods System availability 99,999% Flexible for customization and extension Have open southbound and northbound interfaces Support object-level and domain-level security October 2010 3 TPMS: System Description
General requirementsfor PM system - 2 Support multi-vendor, vendor-dependent, multi-service and service-dependent models for data and hierarchy. Support a service-network relation Keep history of changes of network hierarchy, KPIs and reports Support standard telecom functions and methods like Busy Hour, DAV, Erlang etc. Flexible for extension with user-defined functions. Support data forecasting and profiling October 2010 4 TPMS: System Description
High-levelSystem architecture As most other systems PM system contains: RAW data collection and parsing layer Data storage and managementlayer Application layer Presentation layer (User interface) October 2010 5 TPMS: System Description
RAW data collection and parsing Collect data using FTP, SNMP, CORBA, X.25, SQL, custom scripts Store collected data in input files Unpack files (if needed) Rename files to unified file name (if needed) Identify corrupted files Feed files to parsers Store processed files (may be needed for future data re-load) October 2010 6 TPMS: System Description
RAW data collection and parsing Dump files to unified format Process variable file structure and contents Un-peg data Validate and filter data (formula-based) Normalize data Aggregate, accumulate and enrich data Collect and report it’s own performance counters October 2010 7 TPMS: System Description
Data storage and managementlayer Data warehouse based on industrial standard DBMS (Oracle or Sybase IQ) optimized for VLDB Distributed data storage structure split by source (domain/technology/vendor/version) and location (region) Designed for parallel processing Historical class-object-relation model for all system entities Scalable for network growth and regional splits/merges Secure data storage Flexible for customization and extension Embedded programming language for data access and modification October 2010 8 TPMS: System Description
Application layer Multi-threaded access to DB for parallel processing Provide open integration interface (Web-services, OSS/J, SNMP) Events generation Data aggregation, correlation and profiling Scheduled report generation Store and share generated KPIs and reports Threshold actions (alarms, notifications, etc.) Extendable with optional modules Optional clustered architecture and redundancy Automatic health-check reporting October 2010 9 TPMS: System Description
Presentation layer (User interface) Rich web-based user interface Report and KPI designer/browser for end-users without knowledge of SQL Dashboards and real-time reports Ad-hoc reporting with interactivity and drill-up, drill-down and drill-same capabilities Object-based and domain-based security Export report results to CSV, XML, PDF, etc. Provide an administrative UI for all system components October 2010 10 TPMS: System Description
System architecture in details October 2010 11 TPMS: System Description
Data Collection and Parsing Collect data using FTP, SNMP, CORBA, X.25, SQL, custom scripts Validate data Dump, validate and filter data Normalize, aggregate, accumulate and enrich data October 2010 12 TPMS: System Description
Data Loading & Validation Load parsed data into the DB Validate data gaps and data re-loads Transform and normalize late data Initiate data processing and KPI calculation mechanisms October 2010 13 TPMS: System Description
Data storage Keep RAW and aggregated performance data and KPIs, network hierarchy, KPI and report templates Distributed data storage structure split by source (domain/technology/vendor/version) and location (region) 1 data context = 1 DB instance or schema or database Optimized for parallel processing Designed for very large volumes of data with unstable structure October 2010 14 TPMS: System Description
Data abstraction Provide access to data in different contexts for presentation layer components making the data location-independent. Automatically locates requested data, builds parallelized queries and retrieves collected results. Correctly retrieves data in case of context unavailability October 2010 15 TPMS: System Description
KPI engine Store KPI/PI hierarchy for root-cause analysis Create KPIs by template Calculate KPIs as user-defined formulas or scripts (for complex KPIs) Aggregate KPIs by time and hierarchy Keep history of changes of KPI definitions Create personal and ad-hoc KPIs October 2010 16 TPMS: System Description
Report engine Store reports hierarchy Create reports by template Create batch reports or report chains Create master-detail reports Create personal and ad-hoc reports Calculate reports by request, scheduler, event Support time zones in calculations. Report may be calculated for local or central time zone Save pre-calculated report results for review and investigation without need of recalculation Save report results as XML, CSV, PDF, XLS, etc. Keep history of report definition changes October 2010 17 TPMS: System Description
Inventory Keep hierarchy of network elements (NE) Manage a class-object model Support vendor-specific and vendor-neutral hierarchies Keep history of changes of network hierarchy Manage virtual and logical network elements and groups (like region or data-center) Automatically discover network elements Group NEs by properties (like number of ports) October 2010 18 TPMS: System Description
Security engine Manage users, roles and domains Allow user access to the system functions or objects (NEs, KPIs, Reports) Provide a Single-Sign-On to the system Can be integrated with LDAP, AD, RADIUS, etc. for user authentication and authorization Log all user activities October 2010 19 TPMS: System Description
Alarm engine Automatically calculate KPI thresholds with minimal latency Send threshold alarms to Fault/Event Management Systems Alarms with conditions (alarm is raised in case of 2 or more threshold crosses during 1 hour) Threshold zones for different alarm severities Time-dependent thresholds Automatically clear the alarm in FM system in case of return to normal operation October 2010 20 TPMS: System Description
System administration System is managed from a single user interface as well as from the command line Allow system administrator to manage: Contexts System security Data in DB System components October 2010 21 TPMS: System Description
High-level roadmap October 2010 22 TPMS: System Description
First steps As a first step the Performance Monitoring core functions shall be done: Data Collection and Parsing, Data aggregation and normalization, KPI engine, Reporting (tables and charts) Components to be done first: DB, Report viewer, Report designer, KPI editor, Inventory,  Scheduler,  User GUI October 2010 23 TPMS: System Description
Next steps Following Performance Management functions and components shall be added later: GIS,  Alarm engine,  Northbound interface,  Administration GUI, Collection and parsing visual designer, OLAP,  Profiler,  Decision Support System,  Forecast (What-If), Root-cause analysis October 2010 24 TPMS: System Description
Thank you. October 2010 25 TPMS: System Description PavelLechenko pavel.lechenko@hpcms.ru October 2010 This document is licensed under CC BY.

Mais conteúdo relacionado

Mais procurados

Nokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentation
Nokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentationNokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentation
Nokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentationmohammed khairy
 
12 gsm bss network kpi (tch assignment success rate) optimization manual
12 gsm bss network kpi (tch assignment success rate) optimization manual12 gsm bss network kpi (tch assignment success rate) optimization manual
12 gsm bss network kpi (tch assignment success rate) optimization manualtharinduwije
 
2 g training optimization
2 g training optimization2 g training optimization
2 g training optimizationAhmed Gad
 
3G ERICSSON COUNTERS spptx
3G  ERICSSON COUNTERS spptx3G  ERICSSON COUNTERS spptx
3G ERICSSON COUNTERS spptxankur tomar
 
52 gsm bss network performance ps kpi (downlink tbf establishment success rat...
52 gsm bss network performance ps kpi (downlink tbf establishment success rat...52 gsm bss network performance ps kpi (downlink tbf establishment success rat...
52 gsm bss network performance ps kpi (downlink tbf establishment success rat...tharinduwije
 
Complete umts call flow
Complete umts call flowComplete umts call flow
Complete umts call flowsivakumar D
 
Lte kpis, counters & amp; timers
Lte kpis, counters & amp; timers Lte kpis, counters & amp; timers
Lte kpis, counters & amp; timers Abhishek Jena
 
Telecom OSS/BSS Overview
Telecom OSS/BSS OverviewTelecom OSS/BSS Overview
Telecom OSS/BSS Overviewmagidg
 
Sharing session huawei network optimization january 2015 ver3
Sharing session huawei network optimization january 2015 ver3Sharing session huawei network optimization january 2015 ver3
Sharing session huawei network optimization january 2015 ver3Arwan Priatna
 
Gsm Frequency Planning
Gsm Frequency PlanningGsm Frequency Planning
Gsm Frequency PlanningDeepak Sharma
 
Common channel Signalling System No 7 ppt
Common channel Signalling System No 7 pptCommon channel Signalling System No 7 ppt
Common channel Signalling System No 7 pptSrashti Vyas
 
Huawei case analysis call drop
Huawei case analysis call dropHuawei case analysis call drop
Huawei case analysis call dropMuffat Itoro
 

Mais procurados (20)

Carrier aggregation
Carrier aggregationCarrier aggregation
Carrier aggregation
 
Zte 3g
Zte 3gZte 3g
Zte 3g
 
Nokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentation
Nokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentationNokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentation
Nokia gsm-kpi-analysis-based-on-daily-monitoring-basis-presentation
 
12 gsm bss network kpi (tch assignment success rate) optimization manual
12 gsm bss network kpi (tch assignment success rate) optimization manual12 gsm bss network kpi (tch assignment success rate) optimization manual
12 gsm bss network kpi (tch assignment success rate) optimization manual
 
2 g training optimization
2 g training optimization2 g training optimization
2 g training optimization
 
Channel element
Channel elementChannel element
Channel element
 
3G ERICSSON COUNTERS spptx
3G  ERICSSON COUNTERS spptx3G  ERICSSON COUNTERS spptx
3G ERICSSON COUNTERS spptx
 
Handover
HandoverHandover
Handover
 
52 gsm bss network performance ps kpi (downlink tbf establishment success rat...
52 gsm bss network performance ps kpi (downlink tbf establishment success rat...52 gsm bss network performance ps kpi (downlink tbf establishment success rat...
52 gsm bss network performance ps kpi (downlink tbf establishment success rat...
 
Complete umts call flow
Complete umts call flowComplete umts call flow
Complete umts call flow
 
Lte kpis, counters & amp; timers
Lte kpis, counters & amp; timers Lte kpis, counters & amp; timers
Lte kpis, counters & amp; timers
 
Telecom OSS/BSS Overview
Telecom OSS/BSS OverviewTelecom OSS/BSS Overview
Telecom OSS/BSS Overview
 
Sharing session huawei network optimization january 2015 ver3
Sharing session huawei network optimization january 2015 ver3Sharing session huawei network optimization january 2015 ver3
Sharing session huawei network optimization january 2015 ver3
 
Telecom BSS
Telecom BSSTelecom BSS
Telecom BSS
 
Gsm Frequency Planning
Gsm Frequency PlanningGsm Frequency Planning
Gsm Frequency Planning
 
UMTS/LTE/EPC Call Flows for CSFB
UMTS/LTE/EPC Call Flows for CSFBUMTS/LTE/EPC Call Flows for CSFB
UMTS/LTE/EPC Call Flows for CSFB
 
Gsm call routing
Gsm call routingGsm call routing
Gsm call routing
 
Common channel Signalling System No 7 ppt
Common channel Signalling System No 7 pptCommon channel Signalling System No 7 ppt
Common channel Signalling System No 7 ppt
 
3 g call flow
3 g call flow3 g call flow
3 g call flow
 
Huawei case analysis call drop
Huawei case analysis call dropHuawei case analysis call drop
Huawei case analysis call drop
 

Destaque

Aggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of dataAggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of dataRostislav Pashuto
 
Monitoring for service delivery
Monitoring for service deliveryMonitoring for service delivery
Monitoring for service deliveryIRC
 
Managed Service Overview
Managed Service OverviewManaged Service Overview
Managed Service Overviewanwarizal
 
Telecom due diligence & benchmark in developing countries
Telecom due diligence & benchmark in developing countriesTelecom due diligence & benchmark in developing countries
Telecom due diligence & benchmark in developing countriesSokrates advisors
 
Customer Churn, A Data Science Use Case in Telecom
Customer Churn, A Data Science Use Case in TelecomCustomer Churn, A Data Science Use Case in Telecom
Customer Churn, A Data Science Use Case in TelecomChris Chen
 
Telecom Subscription, Churn and ARPU Analysis
Telecom Subscription, Churn and ARPU AnalysisTelecom Subscription, Churn and ARPU Analysis
Telecom Subscription, Churn and ARPU AnalysisAnurag Shandilya
 
Bi in telecom through kpi’s
Bi in telecom through kpi’sBi in telecom through kpi’s
Bi in telecom through kpi’sSai Venkatesh
 
Unit- 3. Performance Management and strategic Planning
Unit- 3.	Performance Management and strategic PlanningUnit- 3.	Performance Management and strategic Planning
Unit- 3. Performance Management and strategic PlanningPreeti Bhaskar
 
Unit- 2. Performance Management Process
Unit- 2.	Performance Management ProcessUnit- 2.	Performance Management Process
Unit- 2. Performance Management ProcessPreeti Bhaskar
 
Chapter 2: Performance Management Process
Chapter 2: Performance Management ProcessChapter 2: Performance Management Process
Chapter 2: Performance Management ProcessHRM751
 
Big Data Meetup: Data Science & Big Data in Telecom
Big Data Meetup: Data Science & Big Data in TelecomBig Data Meetup: Data Science & Big Data in Telecom
Big Data Meetup: Data Science & Big Data in TelecomProvectus
 
Airbnb Pitch Deck From 2008
Airbnb Pitch Deck From 2008Airbnb Pitch Deck From 2008
Airbnb Pitch Deck From 2008Ryan Gum
 

Destaque (15)

Scalable Real-time analytics using Druid
Scalable Real-time analytics using DruidScalable Real-time analytics using Druid
Scalable Real-time analytics using Druid
 
Aggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of dataAggregated queries with Druid on terrabytes and petabytes of data
Aggregated queries with Druid on terrabytes and petabytes of data
 
Monitoring for service delivery
Monitoring for service deliveryMonitoring for service delivery
Monitoring for service delivery
 
Managed Service Overview
Managed Service OverviewManaged Service Overview
Managed Service Overview
 
Telecom due diligence & benchmark in developing countries
Telecom due diligence & benchmark in developing countriesTelecom due diligence & benchmark in developing countries
Telecom due diligence & benchmark in developing countries
 
Vodafone KPIs
Vodafone KPIsVodafone KPIs
Vodafone KPIs
 
Telecommunications Kpi
Telecommunications  KpiTelecommunications  Kpi
Telecommunications Kpi
 
Customer Churn, A Data Science Use Case in Telecom
Customer Churn, A Data Science Use Case in TelecomCustomer Churn, A Data Science Use Case in Telecom
Customer Churn, A Data Science Use Case in Telecom
 
Telecom Subscription, Churn and ARPU Analysis
Telecom Subscription, Churn and ARPU AnalysisTelecom Subscription, Churn and ARPU Analysis
Telecom Subscription, Churn and ARPU Analysis
 
Bi in telecom through kpi’s
Bi in telecom through kpi’sBi in telecom through kpi’s
Bi in telecom through kpi’s
 
Unit- 3. Performance Management and strategic Planning
Unit- 3.	Performance Management and strategic PlanningUnit- 3.	Performance Management and strategic Planning
Unit- 3. Performance Management and strategic Planning
 
Unit- 2. Performance Management Process
Unit- 2.	Performance Management ProcessUnit- 2.	Performance Management Process
Unit- 2. Performance Management Process
 
Chapter 2: Performance Management Process
Chapter 2: Performance Management ProcessChapter 2: Performance Management Process
Chapter 2: Performance Management Process
 
Big Data Meetup: Data Science & Big Data in Telecom
Big Data Meetup: Data Science & Big Data in TelecomBig Data Meetup: Data Science & Big Data in Telecom
Big Data Meetup: Data Science & Big Data in Telecom
 
Airbnb Pitch Deck From 2008
Airbnb Pitch Deck From 2008Airbnb Pitch Deck From 2008
Airbnb Pitch Deck From 2008
 

Semelhante a Telecom Performance Management System: Overview

An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3
An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3
An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3Marco Gralike
 
IBM Cognos Mashup Service Overview
IBM Cognos Mashup Service OverviewIBM Cognos Mashup Service Overview
IBM Cognos Mashup Service OverviewIBM
 
Environment Canada's Data Management Service
Environment Canada's Data Management ServiceEnvironment Canada's Data Management Service
Environment Canada's Data Management ServiceSafe Software
 
Microsoft SQL Server - StreamInsight Overview Presentation
Microsoft SQL Server - StreamInsight Overview PresentationMicrosoft SQL Server - StreamInsight Overview Presentation
Microsoft SQL Server - StreamInsight Overview PresentationMicrosoft Private Cloud
 
Monitoring your data center with scom
Monitoring your data center with scomMonitoring your data center with scom
Monitoring your data center with scomMojammel Hossain
 
trisulnsm_6.5_datasheet
trisulnsm_6.5_datasheettrisulnsm_6.5_datasheet
trisulnsm_6.5_datasheettrisulnsm
 
061206 Ua Huntsville Seminar
061206 Ua Huntsville Seminar061206 Ua Huntsville Seminar
061206 Ua Huntsville SeminarRudolf Husar
 
SmartCloud Monitoring and Capacity Planning
SmartCloud Monitoring and Capacity PlanningSmartCloud Monitoring and Capacity Planning
SmartCloud Monitoring and Capacity PlanningIBM Danmark
 
Towards a REST architecture for networked vehicles and sensors
Towards a REST architecture for networked vehicles and sensorsTowards a REST architecture for networked vehicles and sensors
Towards a REST architecture for networked vehicles and sensorsJosé Pinto
 
NMS Projects and POCs completed and ongoing for OSS NAM v 1.5 Linkedin
NMS Projects and POCs completed and ongoing for OSS NAM v 1.5 LinkedinNMS Projects and POCs completed and ongoing for OSS NAM v 1.5 Linkedin
NMS Projects and POCs completed and ongoing for OSS NAM v 1.5 LinkedinJavier Guillermo, MBA, MSc, PMP
 
Big data & hadoop framework
Big data & hadoop frameworkBig data & hadoop framework
Big data & hadoop frameworkTu Pham
 
Prototype Implementation of a Demand Driven Network Monitoring Architecture
Prototype Implementation of a Demand Driven Network Monitoring ArchitecturePrototype Implementation of a Demand Driven Network Monitoring Architecture
Prototype Implementation of a Demand Driven Network Monitoring ArchitectureAugusto Ciuffoletti
 
Saying goodbye to SQL Server 2000
Saying goodbye to SQL Server 2000Saying goodbye to SQL Server 2000
Saying goodbye to SQL Server 2000ukdpe
 
Distributed Systems: How to connect your real-time applications
Distributed Systems: How to connect your real-time applicationsDistributed Systems: How to connect your real-time applications
Distributed Systems: How to connect your real-time applicationsJaime Martin Losa
 
Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...
Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...
Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...Motadata
 
IoT Analytics from Edge to Cloud - using IBM Informix
IoT Analytics from Edge to Cloud - using IBM InformixIoT Analytics from Edge to Cloud - using IBM Informix
IoT Analytics from Edge to Cloud - using IBM InformixPradeep Muthalpuredathe
 
Mainframe Architecture & Product Overview
Mainframe Architecture & Product OverviewMainframe Architecture & Product Overview
Mainframe Architecture & Product Overviewabhi1112
 
Unify Analytics: Combine Strengths of Data Lake and Data Warehouse
Unify Analytics: Combine Strengths of Data Lake and Data WarehouseUnify Analytics: Combine Strengths of Data Lake and Data Warehouse
Unify Analytics: Combine Strengths of Data Lake and Data WarehousePaige_Roberts
 
Tems discovery 4.0.8 release note
Tems discovery 4.0.8 release noteTems discovery 4.0.8 release note
Tems discovery 4.0.8 release noteFahd Salim Abbas
 

Semelhante a Telecom Performance Management System: Overview (20)

An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3
An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3
An introduction into Oracle Enterprise Manager Cloud Control 12c Release 3
 
IBM Cognos Mashup Service Overview
IBM Cognos Mashup Service OverviewIBM Cognos Mashup Service Overview
IBM Cognos Mashup Service Overview
 
Environment Canada's Data Management Service
Environment Canada's Data Management ServiceEnvironment Canada's Data Management Service
Environment Canada's Data Management Service
 
Microsoft SQL Server - StreamInsight Overview Presentation
Microsoft SQL Server - StreamInsight Overview PresentationMicrosoft SQL Server - StreamInsight Overview Presentation
Microsoft SQL Server - StreamInsight Overview Presentation
 
Monitoring your data center with scom
Monitoring your data center with scomMonitoring your data center with scom
Monitoring your data center with scom
 
trisulnsm_6.5_datasheet
trisulnsm_6.5_datasheettrisulnsm_6.5_datasheet
trisulnsm_6.5_datasheet
 
061206 Ua Huntsville Seminar
061206 Ua Huntsville Seminar061206 Ua Huntsville Seminar
061206 Ua Huntsville Seminar
 
SmartCloud Monitoring and Capacity Planning
SmartCloud Monitoring and Capacity PlanningSmartCloud Monitoring and Capacity Planning
SmartCloud Monitoring and Capacity Planning
 
Towards a REST architecture for networked vehicles and sensors
Towards a REST architecture for networked vehicles and sensorsTowards a REST architecture for networked vehicles and sensors
Towards a REST architecture for networked vehicles and sensors
 
NMS Projects and POCs completed and ongoing for OSS NAM v 1.5 Linkedin
NMS Projects and POCs completed and ongoing for OSS NAM v 1.5 LinkedinNMS Projects and POCs completed and ongoing for OSS NAM v 1.5 Linkedin
NMS Projects and POCs completed and ongoing for OSS NAM v 1.5 Linkedin
 
Big data & hadoop framework
Big data & hadoop frameworkBig data & hadoop framework
Big data & hadoop framework
 
Prototype Implementation of a Demand Driven Network Monitoring Architecture
Prototype Implementation of a Demand Driven Network Monitoring ArchitecturePrototype Implementation of a Demand Driven Network Monitoring Architecture
Prototype Implementation of a Demand Driven Network Monitoring Architecture
 
Saying goodbye to SQL Server 2000
Saying goodbye to SQL Server 2000Saying goodbye to SQL Server 2000
Saying goodbye to SQL Server 2000
 
Distributed Systems: How to connect your real-time applications
Distributed Systems: How to connect your real-time applicationsDistributed Systems: How to connect your real-time applications
Distributed Systems: How to connect your real-time applications
 
Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...
Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...
Product Presentation - Motadata Unified Platform for IT Monitoring, flow anal...
 
IoT Analytics from Edge to Cloud - using IBM Informix
IoT Analytics from Edge to Cloud - using IBM InformixIoT Analytics from Edge to Cloud - using IBM Informix
IoT Analytics from Edge to Cloud - using IBM Informix
 
Mainframe Architecture & Product Overview
Mainframe Architecture & Product OverviewMainframe Architecture & Product Overview
Mainframe Architecture & Product Overview
 
Unify Analytics: Combine Strengths of Data Lake and Data Warehouse
Unify Analytics: Combine Strengths of Data Lake and Data WarehouseUnify Analytics: Combine Strengths of Data Lake and Data Warehouse
Unify Analytics: Combine Strengths of Data Lake and Data Warehouse
 
Tems discovery 4.0.8 release note
Tems discovery 4.0.8 release noteTems discovery 4.0.8 release note
Tems discovery 4.0.8 release note
 
SAST Interface Management for SAP systems [Webinar]
SAST Interface Management for SAP systems [Webinar]SAST Interface Management for SAP systems [Webinar]
SAST Interface Management for SAP systems [Webinar]
 

Último

The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxBkGupta21
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 

Último (20)

The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptx
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 

Telecom Performance Management System: Overview

  • 1. TelecomPerformance ManagementSystem:System Description PavelLechenko pavel.lechenko@hpcms.ru October 2010 This document is licensed under CC BY.
  • 2. Operators need PM system to: Predict, analyze and investigate network and service performance degradations Generate and present network and service performance reports to company management Forecast network and service performance in case of events (Exhibitions/Trade Shows, New Year, Olympic games) or new product launches Control compliance with SLA on outsourced equipment October 2010 2 TPMS: System Description
  • 3. General requirements for PM system - 1 Near real-time system Support different data sources like performance counters, CDRs, probes, field/drive test results Scalable for any volumes of input data and retention periods System availability 99,999% Flexible for customization and extension Have open southbound and northbound interfaces Support object-level and domain-level security October 2010 3 TPMS: System Description
  • 4. General requirementsfor PM system - 2 Support multi-vendor, vendor-dependent, multi-service and service-dependent models for data and hierarchy. Support a service-network relation Keep history of changes of network hierarchy, KPIs and reports Support standard telecom functions and methods like Busy Hour, DAV, Erlang etc. Flexible for extension with user-defined functions. Support data forecasting and profiling October 2010 4 TPMS: System Description
  • 5. High-levelSystem architecture As most other systems PM system contains: RAW data collection and parsing layer Data storage and managementlayer Application layer Presentation layer (User interface) October 2010 5 TPMS: System Description
  • 6. RAW data collection and parsing Collect data using FTP, SNMP, CORBA, X.25, SQL, custom scripts Store collected data in input files Unpack files (if needed) Rename files to unified file name (if needed) Identify corrupted files Feed files to parsers Store processed files (may be needed for future data re-load) October 2010 6 TPMS: System Description
  • 7. RAW data collection and parsing Dump files to unified format Process variable file structure and contents Un-peg data Validate and filter data (formula-based) Normalize data Aggregate, accumulate and enrich data Collect and report it’s own performance counters October 2010 7 TPMS: System Description
  • 8. Data storage and managementlayer Data warehouse based on industrial standard DBMS (Oracle or Sybase IQ) optimized for VLDB Distributed data storage structure split by source (domain/technology/vendor/version) and location (region) Designed for parallel processing Historical class-object-relation model for all system entities Scalable for network growth and regional splits/merges Secure data storage Flexible for customization and extension Embedded programming language for data access and modification October 2010 8 TPMS: System Description
  • 9. Application layer Multi-threaded access to DB for parallel processing Provide open integration interface (Web-services, OSS/J, SNMP) Events generation Data aggregation, correlation and profiling Scheduled report generation Store and share generated KPIs and reports Threshold actions (alarms, notifications, etc.) Extendable with optional modules Optional clustered architecture and redundancy Automatic health-check reporting October 2010 9 TPMS: System Description
  • 10. Presentation layer (User interface) Rich web-based user interface Report and KPI designer/browser for end-users without knowledge of SQL Dashboards and real-time reports Ad-hoc reporting with interactivity and drill-up, drill-down and drill-same capabilities Object-based and domain-based security Export report results to CSV, XML, PDF, etc. Provide an administrative UI for all system components October 2010 10 TPMS: System Description
  • 11. System architecture in details October 2010 11 TPMS: System Description
  • 12. Data Collection and Parsing Collect data using FTP, SNMP, CORBA, X.25, SQL, custom scripts Validate data Dump, validate and filter data Normalize, aggregate, accumulate and enrich data October 2010 12 TPMS: System Description
  • 13. Data Loading & Validation Load parsed data into the DB Validate data gaps and data re-loads Transform and normalize late data Initiate data processing and KPI calculation mechanisms October 2010 13 TPMS: System Description
  • 14. Data storage Keep RAW and aggregated performance data and KPIs, network hierarchy, KPI and report templates Distributed data storage structure split by source (domain/technology/vendor/version) and location (region) 1 data context = 1 DB instance or schema or database Optimized for parallel processing Designed for very large volumes of data with unstable structure October 2010 14 TPMS: System Description
  • 15. Data abstraction Provide access to data in different contexts for presentation layer components making the data location-independent. Automatically locates requested data, builds parallelized queries and retrieves collected results. Correctly retrieves data in case of context unavailability October 2010 15 TPMS: System Description
  • 16. KPI engine Store KPI/PI hierarchy for root-cause analysis Create KPIs by template Calculate KPIs as user-defined formulas or scripts (for complex KPIs) Aggregate KPIs by time and hierarchy Keep history of changes of KPI definitions Create personal and ad-hoc KPIs October 2010 16 TPMS: System Description
  • 17. Report engine Store reports hierarchy Create reports by template Create batch reports or report chains Create master-detail reports Create personal and ad-hoc reports Calculate reports by request, scheduler, event Support time zones in calculations. Report may be calculated for local or central time zone Save pre-calculated report results for review and investigation without need of recalculation Save report results as XML, CSV, PDF, XLS, etc. Keep history of report definition changes October 2010 17 TPMS: System Description
  • 18. Inventory Keep hierarchy of network elements (NE) Manage a class-object model Support vendor-specific and vendor-neutral hierarchies Keep history of changes of network hierarchy Manage virtual and logical network elements and groups (like region or data-center) Automatically discover network elements Group NEs by properties (like number of ports) October 2010 18 TPMS: System Description
  • 19. Security engine Manage users, roles and domains Allow user access to the system functions or objects (NEs, KPIs, Reports) Provide a Single-Sign-On to the system Can be integrated with LDAP, AD, RADIUS, etc. for user authentication and authorization Log all user activities October 2010 19 TPMS: System Description
  • 20. Alarm engine Automatically calculate KPI thresholds with minimal latency Send threshold alarms to Fault/Event Management Systems Alarms with conditions (alarm is raised in case of 2 or more threshold crosses during 1 hour) Threshold zones for different alarm severities Time-dependent thresholds Automatically clear the alarm in FM system in case of return to normal operation October 2010 20 TPMS: System Description
  • 21. System administration System is managed from a single user interface as well as from the command line Allow system administrator to manage: Contexts System security Data in DB System components October 2010 21 TPMS: System Description
  • 22. High-level roadmap October 2010 22 TPMS: System Description
  • 23. First steps As a first step the Performance Monitoring core functions shall be done: Data Collection and Parsing, Data aggregation and normalization, KPI engine, Reporting (tables and charts) Components to be done first: DB, Report viewer, Report designer, KPI editor, Inventory, Scheduler, User GUI October 2010 23 TPMS: System Description
  • 24. Next steps Following Performance Management functions and components shall be added later: GIS, Alarm engine, Northbound interface, Administration GUI, Collection and parsing visual designer, OLAP, Profiler, Decision Support System, Forecast (What-If), Root-cause analysis October 2010 24 TPMS: System Description
  • 25. Thank you. October 2010 25 TPMS: System Description PavelLechenko pavel.lechenko@hpcms.ru October 2010 This document is licensed under CC BY.