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An introduction to Workload Modelling for Cloud Applications

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A high-level overview of Workload Modelling as a part of Performance Testing Life Cycle with focus on the challenges faced in Cloud environment relative to traditional IT infrastructure.

Publicada em: Tecnologia
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An introduction to Workload Modelling for Cloud Applications

  1. 1. An Introduction to Workload Modelling for the Cloud Infrastructure 0 Ravi Yogesh Web Performance Engineer, Wells Fargo September 12, 2017
  2. 2. What is Workload Modelling ?  Workload modeling is an attempt to create a generalized model that can be used to generate synthetic workloads, using measured data from the real system 1
  3. 3. Why to do ?  To ensure correct scope coverage  To simulate realistic user load pattern in Application Under Test  To identify performance bottlenecks  To identify scalability of the system (1X,2X..)  Capacity Planning to meet anticipated loads 2
  4. 4. When to do ?  During NFR gathering for a new application  Every major release for existing applications 3
  5. 5. How to do 4
  6. 6. How: Things to Consider - Scaling  Production to Test Env. Scale Factor (No. of Servers)  Hardware Configuration Scale Factor (CPU, Instances)  Business Hours (Assume/Derive)  Peak Volume Days (Black Fridays, Christmas) 5 PRODUCTION TEST ENV
  7. 7. How: Little’s Law N = λ*(Rt+TT) Where, N is Number of users. λ is Arrival Rate. Rt is Response Time, TT is Think Time. 6 (
  8. 8. Workload Modelling in the Cloud:  Need / Criticality  Challenges over traditional infrastructure  Solutions and way forward
  9. 9. Workload Modelling in the Cloud: Need: 1. End to End Performance is not Guaranteed !! 2. Difficulties with virtual resource upscaling and downscaling to accommodate workload changes (elasticity) can lead to performance issues (risk of failed transactions/latencies for end user, agility to spin up before crash ??)
  10. 10. Workload Modelling in the Cloud: Need: 3. To maximize the utilization of resources and minimize running costs while maintaining Service Level Agreements (SLAs).  CIOs only use about half of the cloud capacity they've bought ! (An independent survey of 200 UK-based CIOs, by ElasticHost)  Cloud Capacity worth over $ 2 billion is wasted every year on ideal hosts.
  11. 11. Workload Modelling in the Cloud: Challenges:  Highly Distributed and Dynamic Infrastructure : (variable number of servers -> difficult to assess load/machine)  Insufficient Trace-logs for Performance Metrics (business and confidentiality reasons)  Hardware platforms heterogeneity (non-identical physical resources)  Complex Workload (resource sharing by multiple services)
  12. 12. Workload Modelling in the Cloud : Way Forward: • Too Many Variables ?? Automated predictive analytics backed with AI can help by maintaining a balance between cost and performance (3rd party tools: Stacktical, Galileo, TeamQuest) • Application elasticity testing (Single Tenancy for thresholds vs. Multi Tenancy Testing for elasticity and smoothness of spinning up/down) • AWS Tools: Trusted Advisor, Monthly Calculator • Amazon uses ML to do capacity planning for AWS
  13. 13. 12 Questions
  14. 14. References: 1. Performance and Capacity Themes for Cloud Computing, Redpaper IBM 2. How to choose the right cloud model with a workload analysis, IBM 3. Workloads in the Clouds Maria Carla Calzarossa, Marco L. Della Vedova, Luisa Massari, Dana Petcu, Mo’min I.M. Tabash, Daniele Tessera

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