Baseline performance tests results

Results presented here show the maximum throughput of Kea using:

• A very basic Kea configuration
• The simplest traffic characteristics
• Results presented here showing top performance of single Kea setup (one server) and High Availability setup (two connected servers in the network)
• All tests here are type A - which are calculating top performance. Calculated rate will be used in tests type B.

Kea configuration

Tests here are using simplest configuration possible with default configuration.

• one subnet with one pool (/8 for v4 and /64 for v6)
• no options
• no host reservations
• no client classification
• lease lifetimes are longer than test duration (to prevent renewals)
• default values are used everywhere possible (e.g. congestion control disabled, LFC enabled for memfile)

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.

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.

Scenario: 1. Test key words: reservations default global 30 memfile v4

How 3000 (30% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 4 per thread

Scenario: 2. Test key words: reservations default global 30 memfile v6

How 3000 (30% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 4 threads, queue size 4 per thread

Scenario: 3. Test key words: 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: 4. Test key words: 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: 5. Test key words: 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 20 per thread

Scenario: 6. Test key words: 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 20 per thread

Scenario: 7. Test key words: reservations default global 100 memfile v4

How 10000 (100% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 4 per thread

Scenario: 8. Test key words: reservations default global 100 memfile v6

How 10000 (100% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 4 threads, queue size 4 per thread

Scenario: 9. Test key words: 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: 10. Test key words: 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: 11. Test key words: 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 20 per thread

Scenario: 12. Test key words: 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 20 per thread

Scenario: 13. Test key words: reservations default global 150 memfile v4

How 15000 (150% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 4 per thread

Scenario: 14. Test key words: reservations default global 150 memfile v6

How 15000 (150% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 4 threads, queue size 4 per thread

Scenario: 15. Test key words: 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: 16. Test key words: 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: 17. Test key words: 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 20 per thread

Scenario: 18. Test key words: 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 20 per thread

Scenario: 19. Test key words: reservations default subnet 30 memfile v4

How 3000 (30% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 4 per thread

Scenario: 20. Test key words: reservations default subnet 30 memfile v6

How 3000 (30% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 4 threads, queue size 4 per thread

Scenario: 21. Test key words: 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: 22. Test key words: 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: 23. Test key words: 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 20 per thread

Scenario: 24. Test key words: 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 20 per thread

Scenario: 25. Test key words: reservations default subnet 100 memfile v4

How 10000 (100% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 4 per thread

Scenario: 26. Test key words: reservations default subnet 100 memfile v6

How 10000 (100% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 4 threads, queue size 4 per thread

Scenario: 27. Test key words: 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: 28. Test key words: 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: 29. Test key words: 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 20 per thread

Scenario: 30. Test key words: 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 20 per thread

Scenario: 31. Test key words: reservations default subnet 150 memfile v4

How 15000 (150% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 8 threads, queue size 4 per thread

Scenario: 32. Test key words: reservations default subnet 150 memfile v6

How 15000 (150% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: None, default settings
MT settings: 4 threads, queue size 4 per thread

Scenario: 33. Test key words: 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: 34. Test key words: 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: 35. Test key words: 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 20 per thread

Scenario: 36. Test key words: 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 20 per thread

Scenario: 37. Test key words: reservations optimized global 30 memfile v4

How 3000 (30% of all clients) global reservations kept in memfile 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 4 per thread

Scenario: 38. Test key words: reservations optimized global 30 memfile v6

How 3000 (30% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 4 threads, queue size 4 per thread

Scenario: 39. Test key words: 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: 40. Test key words: 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: 41. Test key words: 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 20 per thread

Scenario: 42. Test key words: 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 20 per thread

Scenario: 43. Test key words: reservations optimized global 100 memfile v4

How 10000 (100% of all clients) global reservations kept in memfile 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 4 per thread

Scenario: 44. Test key words: reservations optimized global 100 memfile v6

How 10000 (100% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 4 threads, queue size 4 per thread

Scenario: 45. Test key words: 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: 46. Test key words: 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: 47. Test key words: 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 20 per thread

Scenario: 48. Test key words: 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 20 per thread

Scenario: 49. Test key words: reservations optimized global 150 memfile v4

How 15000 (150% of all clients) global reservations kept in memfile 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 4 per thread

Scenario: 50. Test key words: reservations optimized global 150 memfile v6

How 15000 (150% of all clients) global reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 4 threads, queue size 4 per thread

Scenario: 51. Test key words: 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: 52. Test key words: 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: 53. Test key words: 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 20 per thread

Scenario: 54. Test key words: 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 20 per thread

Scenario: 55. Test key words: reservations optimized subnet 30 memfile v4

How 3000 (30% of all clients) subnet reservations kept in memfile 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 4 per thread

Scenario: 56. Test key words: reservations optimized subnet 30 memfile v6

How 3000 (30% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 4 threads, queue size 4 per thread

Scenario: 57. Test key words: 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: 58. Test key words: 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

Scenario: 59. Test key words: reservations optimized subnet 30 postgresql v4

How 3000 (30% of all clients) subnet 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 20 per thread

Scenario: 60. Test key words: reservations optimized subnet 30 postgresql v6

How 3000 (30% of all clients) subnet 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 20 per thread

Scenario: 61. Test key words: reservations optimized subnet 100 memfile v4

How 10000 (100% of all clients) subnet reservations kept in memfile 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 4 per thread

Scenario: 62. Test key words: reservations optimized subnet 100 memfile v6

How 10000 (100% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 4 threads, queue size 4 per thread

Scenario: 63. Test key words: reservations optimized subnet 100 mysql v4

How 10000 (100% 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: 64. Test key words: reservations optimized subnet 100 mysql v6

How 10000 (100% 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: 65. Test key words: reservations optimized subnet 100 postgresql v4

How 10000 (100% of all clients) subnet 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 20 per thread

Scenario: 66. Test key words: reservations optimized subnet 100 postgresql v6

How 10000 (100% of all clients) subnet 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 20 per thread

Scenario: 67. Test key words: reservations optimized subnet 150 memfile v4

How 15000 (150% of all clients) subnet reservations kept in memfile 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 4 per thread

Scenario: 68. Test key words: reservations optimized subnet 150 memfile v6

How 15000 (150% of all clients) subnet reservations kept in memfile decreases performance (leases in memfile)
Reservation optimization: host-reservation-identifiers set to hw-address, and reservation out-of-pool
MT settings: 4 threads, queue size 4 per thread

Scenario: 69. Test key words: reservations optimized subnet 150 mysql v4

How 15000 (150% 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: 70. Test key words: reservations optimized subnet 150 mysql v6

How 15000 (150% 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: 71. Test key words: reservations optimized subnet 150 postgresql v4

How 15000 (150% of all clients) subnet 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 20 per thread

Scenario: 72. Test key words: reservations optimized subnet 150 postgresql v6

How 15000 (150% of all clients) subnet 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 20 per thread

Resource consumption of single Kea running with different backends

Each test has an additional description.

Scenario: 1. Test key words: resources consumption memfile v6 multi

Checking if memory usage still grows when there are no new clients, just returning. 10mln unique clients. 4 threads, queue size 4 per thread Memory usage should stop growing after ~379.Traffic is generated for 1138 but Kea is monitored for additional 100 seconds.

Scenario: 2. Test key words: resources consumption memfile v4 multi

Checking if memory usage still grows when there are no new clients, just returning. 10mln unique clients. 8 threads, queue size 4 per thread Memory usage should stop growing after ~367.Traffic is generated for 1101 but Kea is monitored for additional 100 seconds.

Scenario: 3. Test key words: resources consumption mysql v6 multi

Checking if memory usage still grows when there are no new clients, just returning. 10mln unique clients. 14 threads, queue size 160 per thread Memory usage should stop growing after ~1252.Traffic is generated for 3758 but Kea is monitored for additional 100 seconds.

Scenario: 4. Test key words: resources consumption mysql v4 multi

Checking if memory usage still grows when there are no new clients, just returning. 10mln unique clients. 14 threads, queue size 160 per thread Memory usage should stop growing after ~1232.Traffic is generated for 3698 but Kea is monitored for additional 100 seconds.

Scenario: 5. Test key words: resources consumption postgresql v6 multi

Checking if memory usage still grows when there are no new clients, just returning. 10mln unique clients. 6 threads, queue size 20 per thread Memory usage should stop growing after ~1305.Traffic is generated for 3917 but Kea is monitored for additional 100 seconds.

Scenario: 6. Test key words: resources consumption postgresql v4 multi

Checking if memory usage still grows when there are no new clients, just returning. 10mln unique clients. 8 threads, queue size 20 per thread Memory usage should stop growing after ~1213.Traffic is generated for 3639 but Kea is monitored for additional 100 seconds.

Scenario: 7. Test key words: resources allocators 10 memfile iterative v4 multi

Check different allocators: iterative
Subnet is /10

Scenario: 8. Test key words: resources allocators 10 memfile iterative v6 multi

Check different allocators: iterative
Subnet is /10

Scenario: 9. Test key words: resources allocators 10 memfile random v4 multi

Check different allocators: random
Subnet is /10

Scenario: 10. Test key words: resources allocators 10 memfile random v6 multi

Check different allocators: random
Subnet is /10

Scenario: 11. Test key words: resources allocators 10 memfile flq v4 multi

Check different allocators: flq
Subnet is /10