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IPLC Analytic Dashboard - Mohd Rizal bin Mohd Ramly


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IPLC Analytic Dashboard

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IPLC Analytic Dashboard - Mohd Rizal bin Mohd Ramly

  2. 2. do analysis on the IPLC, we need to understand the IPLC design and traffic International Private Leased Circuit (IPLC)? IGW IPOP IGW Domestic Internet Cloud KLJ CBJBRFJHB AMS FFT PNH Upstream Customer Backhaul Backhaul- CustomerUpstream Peering Peering Backhaul Transit Provider Public/Private Peering Customer Global IPVPN Overlay Only link highlighted in RED is taken into International cap calculation
  3. 3. Nearly 4.6 millions row of performance data sent to data lake daily & lots of insight can be explored with data science technique
  4. 4. #4 #3 #2 #1 Just traditional tools without integration with any dynamic mathematic tools to do threshold and forecasting No forecasting Do the behavior of traffic based on source or destination? eak same time vs Peak Diff time? No clustering View Unable to do any of advanced analytic such as congestion predictor during failure Advanced analytic Problem Statement Traditional Monitoring Systems not suitable for clustering view – slow/ unpresentable
  5. 5. q Analyse optimum capacity requirement for TM’s internationa traffic ECTIVES q Have sustainable single monitori analytic dashboard for TM’s oper centre for IPLC q To have faster failure analytic dur failure event
  6. 6. s international capacity: ata June 2017 1.XXX Tbps ( XXX Links) IP Leased Circuit (IPLC) XXX Links (10XX Gbps) Backhaul XX Links (XX2.5 Gbps) (ii) Upstream XXX Gbps (3X Links) (ii) Peering (XX0 Gbps) (XX Links) Upstream XX Links (3XX. 5Gbps) Direct Peering XX links (1XX. 6Gbps) Backhaul Customer XX links (4X. 7Gbps)
  7. 7. rder to verify the traffic peak time, we can do further analysis to calculate ther each link peak at the same time on time zone vs Source time zone Europe South Asia Middle East South East Asia North Asia US •  If the peak for each region diff between each other in a day, we cannot take a maximum bandwidth everyday by snapshot time.
  8. 8. m data analysis, the peak time for aggregate link as below and behaviour fo h link k Time Aggregation Link distribution vs Each link peak distribution •  Majority link peak during 10 pm •  Both hour distribution is traffic behaviour and the maximum capacity shall be taken from snapshot time •  Traffic peak behaviour not depending on destination time zone but SOURCE time zone UPLOAD TRAFFIC DOWNLOAD TRAFFIC
  9. 9. ge gap for forecast due to different assumption gap due to different calculation methodology and assumption of destination time zone erational team ISPNM team imum traffic requirement by operational team Diff using max monthly vs max at a time: DEC 2016 Example By using forecast function in Excel, operational team able to do forecasting for 2017 expected traffic BUT Operational team calculate monthly maximum for each link and aggregate 1XX ++ IPLC links to get total bandwidth requirement. R programming able to calculate peak concurrent in terms of time to get the maximum 1XX IPLC links. Multiple more elegant forecast algorithm can be done using R programming and visualize by Shiny R (arima, prophet alg Different about 3X.X% due to different calculation methodology 7XX 5XX time_max -3X.X% month_max TotalBWReq
  10. 10. prevent mistake on utilization capacity calculation, dashboard do the time main aggregation and multiple types of clustering analysis •  Beneficial to TM to do proper monitoring and planning for IPLC.
  11. 11. c: Clustering analytic for selection of link type (2/3)
  12. 12. c: Clustering analytic for selection of link type (3/3)
  13. 13. c: Clustering analytic for selection of cable (1/1)
  14. 14. estion List: List of link with congestion – current hour (1/1)
  15. 15. estion List: List of link with congestion – current day (1/1)
  16. 16. HANCEMENT: CABLE FAILURE ANALYTIC ata, we able to identify each Point of Presence (PoP) router bandwidth requirement connect Topology to US IGWSY.LA IGWSY.LA2 IGWXY.SJ 02.CBJ02.JRC 01.MCCS 02.KLJ IGWSY.PA 01.CBJ SY.SJ 01.JRCBRF 21.MECS 22.MECS PartnerA CustA 10G PartnerD CustC 30G 10G 10G PartnerC 20G PartnerD 10G PartnerE 10G PartnerB G 20G PartnerF CustB 20G 10G PartnerB 10G PartnerA 30G 4G 3.8G 4.7G 1.1G 2.5G 0% 2.6G 9.1G 7G 9.4G 9.4G 3G 1.5G 1.6G 1.5G 100G6.5G 2.3G G 3G 1G 1.5G3.2G 9.8G 23G 4.5G4.6G 0.9G 4.3 G 8.4 G •  If the routing do not change from the previous da & the data traffi have daily seasonal behaviour, we able to predict which link will b congested durin peak hour •  Easy to network engineer to do route optimization.
  17. 17. le system fault simulator application – (1/4) 1 2Select region to simulat Select Failure Analysis 3 15 interconnect from Malaysia Node to POI node 4 Interconnect between TM Malaysia nodes to POI at each region 5 Click same tab as [2] to view interconnect diagram
  18. 18. 6 Link for interconnect failure can be highlight by clicking each row le system fault simulator application – (2/4)
  19. 19. 7 Simulation for link down as highlighted 8 of gested W router rtner umn] 9 Historical daily maximum traffic for (n-3) where n is current date 10 Maximum traffic for IGW router for past 3 days 11 Capacity available for IGW backhaul le system fault simulator application– (3/4)
  20. 20. 12 Select Global Inventory 13 Barplot for capacity each cable systems to each region le system fault simulator application – (4/4)
  21. 21. Supplement Facts Serving Size: 1,XXX Gbps. Servings Per Container: 1XX Links Amount Per Serving %Daily Value Tukang Fikir Mohd Rizal 30% Tukang Tulis Mohd Rizal 10% Tukang Jaga Nur Fadzlina 10% Tukang Analisis Mohd Rizal / Mohd Akram Akmal 30% Penasihat M Haikal/Mohd Izni Zuhdi/Amzari 5% Tukang Komen Semua di atas 5% ** Daily Value (DV) not established T H A N K Y O U Tukang Present Mohd Rizal 10%
  22. 22. cess Improvement: Benefit of the IPLC Analytic Dashboard (before & after) Breakdown Happen & fault management system triggered Link Utilization Check using performance monitoring and router Data Extraction and Analysis Action and Reporting Breakdown Happen & fault management system triggered Link Utilization and analytic. Action and reporting via Dashboard •  Link loss triggered by NOC •  Link utilization check per link. •  Manually at router and monitoring tools •  Data extraction using performance monitoring •  Manual analysis using excel •  Manual reporting a presentation prepa to management Immediate 1 hours 3 hours 2 hours •  Link loss triggered Immediate •  Automate in analytic and report 15 minutes ith analytic dashboard, the reporting will be more consistent due to eliminate manual process be with improvement about 95% from previous process.
  23. 23. ng Six Sigma, we can understand and come out with correct mechanism to rol 6σ Understand current process and methodology of IPLC monitoring Measure overall traffic requirement concurrent monthly maximum Analyse traffic behaviour per region to identify traffic pattern in 24H. Improve calculation methodology and traffic behaviour recommendation Develop Analytic Dashboard for IPLC for easy control and monitor IPLC Bandwidth
  24. 24. en the performance monitoring nually check each links before alyse (performance monitoring rtal) aggregate view for each region or ble me consuming hat is our EFFECTIVE ANDWIDTH blem Statement Submarine Cable Down Manual Check for all links one by one Manual Analysis 1 2 3 Time consuming!!! Manual checking using fault management system & performance monitoring!!! Cable Down!!! v  Submarine cable may be dow due to many reasons. v  Traffic will overflow to another submarine cable v  Loss of capacity will affected customers experience twork engineer will check fault management tem for any alarm and performance monitoring traffic performance (utilization). overall aggregation view. Manual and prone to or without automation