Performance test results for Kea

  • Generated on - 24/09/2021 11:43:47
  • Generated by - kea-dev/performance
  • Description - Automatically generated on Jenkins
  • Build id - 199
  • Latest Kea version - 1.9.11-isc20210830141610

Baseline performance tests results

Results presented here show the maximum throughput of Kea using:

Kea configuration

Tests here are using simplest configuration possible with default configuration.

Traffic characteristics

ISC's perfdhcp generates simple DORA/SARR exchanges without any additional options or option requests. Normally we configure perfdhcp to simulate up to 500 million different clients (to avoid repeating any client IDs in the course of a single test) unless it's stated differently in the test description.

Different Thread settings

These tests use the basic Kea configuration described in the baseline test report with the simplest traffic characteristics. Other than thread count, the configuration is exactly the same as that used in the "Baseline results" test.

1. The first of these tests (results in the bar charts below) measures how the different Kea backends respond to variation in the number of threads used. The results of this test show that different Kea configurations require different settings for `thread-pool-size` to achieve optimum performance, measured in leases per second.

2. The second test (results in the line charts below) are using the value of `thread-pool-size` which had the highest results for that scenario from the first test. We vary the `packet-queue-size` value and measure the leases per second . This test shows that the optimum queue size depends on the Kea configuration.

We use the thread count settings ("thread-pool-size" and "packet-queue-size") that provide the optimum results from this test to establish the thread count configuration for the other tests included in this report.

Number of threads

Queue pool size per thread

How various ways of host reservations configurations affect Kea performance

Each test has an additional description. Set of bar charts display results comparison of single runs presented in second part of this page.

Compare performance penalty for each backend using different configuration options

All tests:

Scenario: 1. Test key words: multi threading reservations default global 30 mysql v4

How 3000 (30% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 2. Test key words: multi threading reservations default global 30 mysql v6

How 3000 (30% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 3. Test key words: multi threading reservations default global 30 postgresql v4

How 3000 (30% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 70 per thread

Scenario: 4. Test key words: multi threading reservations default global 30 postgresql v6

How 3000 (30% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 6 threads, queue size 70 per thread

Scenario: 5. Test key words: multi threading reservations default global 100 mysql v4

How 10000 (100% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 6. Test key words: multi threading reservations default global 100 mysql v6

How 10000 (100% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 7. Test key words: multi threading reservations default global 100 postgresql v4

How 10000 (100% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 70 per thread

Scenario: 8. Test key words: multi threading reservations default global 100 postgresql v6

How 10000 (100% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 6 threads, queue size 70 per thread

Scenario: 9. Test key words: multi threading reservations default global 150 mysql v4

How 15000 (150% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 10. Test key words: multi threading reservations default global 150 mysql v6

How 15000 (150% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 11. Test key words: multi threading reservations default global 150 postgresql v4

How 15000 (150% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 70 per thread

Scenario: 12. Test key words: multi threading reservations default global 150 postgresql v6

How 15000 (150% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 6 threads, queue size 70 per thread

Scenario: 13. Test key words: multi threading reservations default subnet 30 mysql v4

How 3000 (30% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 14. Test key words: multi threading reservations default subnet 30 mysql v6

How 3000 (30% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 15. Test key words: multi threading reservations default subnet 30 postgresql v4

How 3000 (30% of all clients) subnet reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 70 per thread

Scenario: 16. Test key words: multi threading reservations default subnet 30 postgresql v6

How 3000 (30% of all clients) subnet reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 6 threads, queue size 70 per thread

Scenario: 17. Test key words: multi threading reservations default subnet 100 mysql v4

How 10000 (100% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 18. Test key words: multi threading reservations default subnet 100 mysql v6

How 10000 (100% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 19. Test key words: multi threading reservations default subnet 100 postgresql v4

How 10000 (100% of all clients) subnet reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 70 per thread

Scenario: 20. Test key words: multi threading reservations default subnet 100 postgresql v6

How 10000 (100% of all clients) subnet reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 6 threads, queue size 70 per thread

Scenario: 21. Test key words: multi threading reservations default subnet 150 mysql v4

How 15000 (150% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 22. Test key words: multi threading reservations default subnet 150 mysql v6

How 15000 (150% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 14 threads, queue size 160 per thread

Scenario: 23. Test key words: multi threading reservations default subnet 150 postgresql v4

How 15000 (150% of all clients) subnet reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 70 per thread

Scenario: 24. Test key words: multi threading reservations default subnet 150 postgresql v6

How 15000 (150% of all clients) subnet reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 6 threads, queue size 70 per thread

Scenario: 25. Test key words: multi threading reservations optimized global 30 mysql v4

How 3000 (30% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread

Scenario: 26. Test key words: multi threading reservations optimized global 30 mysql v6

How 3000 (30% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread

Scenario: 27. Test key words: multi threading reservations optimized global 30 postgresql v4

How 3000 (30% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 8 threads, queue size 70 per thread

Scenario: 28. Test key words: multi threading reservations optimized global 30 postgresql v6

How 3000 (30% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 6 threads, queue size 70 per thread

Scenario: 29. Test key words: multi threading reservations optimized global 100 mysql v4

How 10000 (100% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread

Scenario: 30. Test key words: multi threading reservations optimized global 100 mysql v6

How 10000 (100% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread

Scenario: 31. Test key words: multi threading reservations optimized global 100 postgresql v4

How 10000 (100% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 8 threads, queue size 70 per thread

Scenario: 32. Test key words: multi threading reservations optimized global 100 postgresql v6

How 10000 (100% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 6 threads, queue size 70 per thread

Scenario: 33. Test key words: multi threading reservations optimized global 150 mysql v4

How 15000 (150% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread

Scenario: 34. Test key words: multi threading reservations optimized global 150 mysql v6

How 15000 (150% of all clients) global reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread

Scenario: 35. Test key words: multi threading reservations optimized global 150 postgresql v4

How 15000 (150% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 8 threads, queue size 70 per thread

Scenario: 36. Test key words: multi threading reservations optimized global 150 postgresql v6

How 15000 (150% of all clients) global reservations kept in postgresql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 6 threads, queue size 70 per thread

Scenario: 37. Test key words: multi threading reservations optimized subnet 30 mysql v4

How 3000 (30% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread

Scenario: 38. Test key words: multi threading reservations optimized subnet 30 mysql v6

How 3000 (30% of all clients) subnet reservations kept in mysql decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 14 threads, queue size 160 per thread