2.  +284,000,000,000 requests  5 different use cases  Workload characteristics, locality, cache effectiveness 1

3. 2 Cache Servers Web Servers Database

4.  Understand workload characteristics  Identify factors affecting performance  Provide a benchmark for future studies 3

5.  Distributed memory caching system  Key-value store for small objects 4 Hash Function Memcached Servers Key

6.  Capture traces through a Linux Kernel Module (LKM)  Process traces with Hive 5 Memcached Transport (TCP/UDP) Network Ethernet LKM

7. 6 PoolSizeDescription USRFewUser-account status information APPDozensObject metadata of a popular application SYSFewSystem data on service location VARDozensServer-side browser information ETCHundredsNonspecific, general purpose Contains server related information Anything that doesn’t belong to a specific pool goes to ETC

8.  Workload Characteristics  Locality, Cache Behavior 7

9. 8 > 99.8% GET GET:UPDATE = 30:1

10. 9 90% of VAR keys are 31B USR keys are 16B or 21B ETC is heterogeneous

11. 10 USR values are only 2B 90% of values are smaller than 500B

12. 11 90% of data is generated by values of 500B or smaller except ETC 90% is 10KB or smaller values for ETC

13. 12 All pools show diurnal pattern except SYS

14. 13 Night time in Western Semiphere North America starts its day

15.  Workload Characteristics  Locality, Cache Behavior 14

16. 15 0.0003% of keys in 10% of requests in ETC 1% of keys in 55% of requests in ETC Least frequent 50% of keys in 1% of requests in ETC

17. 16

18. 17 99.9% of SYS keys are reused in 1hr 88.5% of ETC keys are reused in 1hr 96.4% of ETC keys are reused in 6hr

19. 18 98.2%92.9%81.4% 93.7%98.7% Why?

20. 19 CompulsoryCapacityInvalidation 70%22%8%

21.  Analyzed 5 different memcached use cases  Different applications of memcached have extreme variations in access patterns  Answered pertinent questions to improve Facebook’s memcached usage 20

22.  Questions? 21