Microservice performance-b
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This document discusses scaling machine learning in the cloud using microservices and streaming data. It presents a model for processing streaming data with microservices running in parallel on Mesos and communicating through RabbitMQ. Performance is analyzed in terms of time spent on different components in serial versus parallel processing as the number of workers increases.
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Microservice performance-b
- 1. gannon@Indiana.edu Dennis.gannon@outlook.com Microservices and Streaming Data: Scaling ML in the Cloud
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- 24. classify Push to table
- 27. classify Push to table classify Push to table classify Push to table classify Push to table classify Push to table classify Push to table te te te ta ta classify Push to table classify Push to table 𝑡 𝑒 = time spent pulling events and pushing notifications to RabbitMQ 𝑡 𝑐 = time classify documents 𝑡 𝑤 = time to invoke the web service 𝑡 𝑎 = time to put final results into the Azure table service
- 28. 𝑡 𝑠𝑒𝑞 = 𝑓 𝑡 𝑒 , 𝑡 𝑎 𝑡 𝑝𝑎𝑟 = 𝑡 𝑐 + 𝑡 𝑤 𝑇𝑠𝑒𝑟𝑖𝑎𝑙 = 𝑡 𝑠𝑒𝑞 + 𝑡 𝑝𝑎𝑟 𝑇𝑝𝑎𝑟 𝑛 = 𝑡 𝑠𝑒𝑞 + 𝑡 𝑝𝑎𝑟 𝑛 𝑆 𝑛 = 𝑇𝑠𝑒𝑟𝑖𝑎𝑙 𝑇𝑝𝑎𝑟 𝑛 = 1 + 𝑡 𝑝𝑎𝑟 𝑡 𝑠𝑒𝑞 1 + 𝑡 𝑝𝑎𝑟 𝑛 ∗ 𝑡 𝑠𝑒𝑞 lim 𝑛→∞ 𝑆(𝑛) = 1 + 𝑡 𝑝𝑎𝑟 𝑡 𝑠𝑒𝑞 1.00 2.02 3.53 7.91 12.33 13.05