Latency Distributions and Micro-Benchmarking to Identify and Characterize Kernel Hotspots

Latency Distributions and Micro-Benchmarking to Identify and Characterize Kernel Hotspots

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Task clone and exit bottleneck (Linux)

15 of 17

15 of 17

Task clone and exit bottleneck (Linux)

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Latency Distributions and Micro-Benchmarking to Identify and Characterize Kernel Hotspots

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  1. 1 Intro
  2. 2 Why Large Bare Metal Boxes? • Faster local communication UNIX Domain Sockets Shared Memory
  3. 3 The Scale in our Department • 100K processes across hundreds of physical machines
  4. 4 SysV semaphore bottleneck (AIX)
  5. 5 Observations and Findings AIX CPU measurement when hyper-threading is very misleading No 'out of the box metrics on SysV IPC operations Sporadic slowness (depending on concurrency/contention)
  6. 6 SysV shared memory bottleneck (Linux) • Low-level application infrastructure code dropping messages Messaging leverages a form of "zero copy" IPC using Sysv
  7. 7 SysV shared memory bottleneck (Linux RHEL 6) The micro-benchmark
  8. 8 Case #2: Observations and Findings • No 'out of the box metrics on SysV IPC operations
  9. 9 UNIX domain socket bottleneck (Solaris) • Critical software infrastructure experiencing timeouts on load Identity management with very strict SLOS Narrowing down the problem A key SLI for the service…
  10. 10 An Aside: Histograms and Distributions are Useful! • More representative of the data set
  11. 11 An Aside: A Histogram Example
  12. 12 Early Observations • No out of the box metrics on socket operations
  13. 13 Case #3: UNIX domain socket bottleneck (Solaris) The micro-benchmarkt-testing against size
  14. 14 Case #3: Conclusions • Solaris 11.3 is limited to a max of 256K UDS sockets
  15. 15 Task clone and exit bottleneck (Linux)
  16. 16 More Summary (Plea to Kernel Folks) • The Prime Directive of Monitoring: Non-interference
  17. 17 References

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