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Workload Model in Airline Operations

Workload Model in Airline Operations. Manoj Raghavendra, TCS. Contents. Introduction of the System Load Model Scenarios & Approach Key Benefits Capacity Planning RAC Configuration. The System. CM. Flight info, Boarding, Conformance, Disruption. Amedeus Global Travel Ops System.

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Workload Model in Airline Operations

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  1. Workload Model in Airline Operations Manoj Raghavendra, TCS

  2. Contents • Introduction of the System • Load Model • Scenarios & Approach • Key Benefits • Capacity Planning • RAC Configuration

  3. The System CM Flight info, Boarding, Conformance, Disruption Amedeus Global Travel Ops System 1A 1F 1B 1D,1E Other Consumer support systems 1C, 1D • EDA Cache for CM means • A system for publishing real-time events • A system to publish timed passenger summary event • A Business data cache that can be substitute back-end calls for a subset of Passenger and Flight data EDA EDA • EDA NFRs • 1200 flight departures per day • 200,000 pax per day • 180 avgpax per flight • 10% growth year-on-year • SLA - 200 ms for Harvesters 100 ms for Event Managers • 400 ms for Data managers • EDA Components contributing to workload • Harvesters – 1A and 1B pattern • Data Enrichers (1B Pattern) • Data Manager (1D, 1E and 1F patterns) • Event Managers – 1A, 1C Pattern XML DB Tech Stack: Auria Sonic ESB, Sonic MQ, DataXtend Semantic Integrator, Oracle11g XML DB EDA Load model was proposed to estimate the saving in the number of calls to Amedeus

  4. The Load Model • Creation of Load Model • Patterns defined for each scenario • Mapped against the Business activity • Production data applied on the Pattern - Business activity • Service level throughput computed - using an custom app • Production Data collected: • Selected the Peak day of the year (15-July -2013): • 1222 flights & 190,000 pax • Selected a Disruption day • (Volcanic Ash Cloud): • 486 flights rescheduled • 350 flights cancelled • 80 flights rerouted before dep • 30 flights rerouted after dep • Data available at Business activity level

  5. Key Benefits – RoI & Capacity sizing Load Model was effective in assessing the RoI and Decision making with changing requirements • Calls to Amedeus reduced from 170 TPS to 150 TPS • Derived the Service level throughput on the XML DB (CRUD ops) ~ 300 TPS • New Requirement – Add Recovery of EDA Cache • Additional throughput of ~400TPS for 60 minutes to recover imminent flight details • Additional throughput of ~100 TPS for 6 hours to recover D+3 days flights • CPU Sizing of the Oracle XML DB ~ 730 TPS at DB level – 100 Cores of HP G7 processor • With Recovery, additional ~400 TPS on the DB – 152 cores of HP G7 processor • Additional CM Applications opted to use EDA Cache • Load on Amedeus reduced further to 140 TPS • Increased the load on EDA DB ~ 900 TPS -200 cores of HP G7

  6. Key Benefits - How much to RAC • Oracle XML DB - # Cores required 100 cores ~ 4 Node RAC (32 G7 core per node) 180 cores ~ 6 Node RAC 200 cores ~ 8 Node RAC • Performance test results showed RAC nodes could not scale linearly for EDA • Prime workload (>10 tps) derived from Load Model constituted 80% of Workload • G8 processors are 1.5 times faster than G7 • G8 v2 processor are 2.25 times faster than G7 Decision made to go for 2 node RAC with G8 v2 processors.

  7. Questions ???

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