1. Piotr Srebrny 1

2.  Problem statement  Packet caching  Thesis claims  Contributions  Related works  Critical review of claims  Conclusions  Future work 2

3.  Internet is a content distribution network  P2P, file hosting, and streaming account for more than 80% of the Internet traffic (“Internet study”, Ipoque, 2009)  Single source multiple destination transport mechanism is fundamental  At present, Internet does not provide efficient multi-point transport mechanism 3

4.  Server transmitting the same data to multiple destinations is wasting the Internet resources  The same data traverses the same path multiple times D C S A B PDPCPBPA 4

5. The goal of this work is to remove the Internet redundancy in a minimal invasive way 5

6.  “Datagram routing for internet multicasting”, L. Aguilar, 1984 – explicit list of destinations in the IP header  “Host groups: A multicast extension for datagram internetworks”, D. Cheriton and S. Deering, 1985 – destination address denotes a group of host  “A case for end system multicast”, Y. hua Chu et al., 2000 – application layer multicast 6

7.  Consider two packets A and B that carry the same content and travel the same few hops PA A B PPP 7

8. PB A B PPP B PB 8

9. I. Packet Caching system can achieve near multicast bandwidth savings II. Packet Caching system requires server support III. Packet Caching system is incrementally deployable IV. Packet Caching system preserves fairness in Internet 9

10.  Principles  Feasibility  Environmental impact 10

11. I. Contribution 11

12. Network elements:  Link  Medium transporting packets  Very deterministic  Throughput limited in bits per second  Router  Switches data packets between links  Very unpredictable  Throughput limited in packets per second 12 (I) Cache payloads on links

13.  Caching is done on per link basis  Cache Management Unit (CMU) removes payloads that are stored on the link exit  Cache Store Unit (CSU) restores payloads from a local cache 13

14.  Link cache processing must be simple  ~72ns to process a minimum size packet on a 10Gbps link  Modern memory r/w cycle ~6-20ns  Link cache size must be minimised  At present, a link queue is scaled to 250ms of the link traffic  Difficult to build! 14 (II) A source of redundant data must support caching!

15.  Server can transmit packets carrying the same data within a minimum time interval  Server can mark its redundant traffic  Server can provide additional information on packet content 15

16. Two components of the CacheCast system  Server support  Distributed infrastructure of small link caches D C S A B DCB PA CMUCSUCMUCSU 16

17.  Cacheable packet carries a metadata describing packet payload  Payload ID  Payload size  Index  Only packets with the metadata are cached 17

18. Packet train  Only the first packet carries the payload  The remaining packets truncated to the header 18

19.  Packet train duration time  It is sufficient to hold payload in the CSU for the packet train duration time  What is the maximum packet train duration time? 19

20. Back-of-the-envelope calculations  ~10ms caches are sufficient 20

21. II. Contribution 21

22. Two aspects of the CacheCast system I. Efficiency  How much redundancy CacheCast removes? II. Computational complexity  Can CacheCast be implemented efficiently with the present technology? 22

23.  CacheCast and ‘Perfect multicast’  ‘Perfect multicast’ – delivers data to multiple destinations without any overhead  CacheCast overheads I. Unique packet header per destination II. Finite link cache size resulting in payload retransmissions III. Partial deployment 23

24.  Example L m – total amount of multicast links L u – total amount of unicast links 24

25. CacheCast unicast header part (h) and multicast payload part (p) Thus: E.g.: using packets where s p =1436B and s h =64B, CacheCast achieves 96% of the ‘perfect multicast’ efficiency 25

26. System efficiency δ m for 10ms large caches 26

27. S CMU and CSU deployed partially 123456 27

28. 28

29.  Considering unique packet headers  CacheCast can achieve 96% of the ‘Perfect multicast’ efficiency  Considering finite cache size  10ms link caches can remove most of the redundancy generated by fast sources  Considering partial deployment  CacheCast deployed over the first five hops from a server achieves already half of the maximum efficiency 29

30.  Computational complexity may render CacheCast inefficient  Implementations  Server support – a Linux system call and an auxiliary shell command tool  Link cache elements – implemented with Click Modular Router Software as processing elements 30

31.  New system call msend()  msend() compared with the standard send() system call 31

32.  Server transmitting to 100 destinations using  Loop of send() sys. calls  A single msend() sys. call  msend() system call outperforms the standard send() system call when transmitting to multiple destinations 32

33.  Click Modular Router Software  CMU and CSU implemented as processing elements  CacheCast router 33

34.  Due to CSU and CMU elements CacheCast router cannot forward packet trains at line rate 34

35.  When compared with a standard router CacheCast router can forward more data 35

36.  Testbed setup  Clients from machines A and B gradually connect to server S 36

37.  Original paraslash server can only handle 74 clients  CacheCast paraslash server can handle 1020 clients and more depending on the chunk size  Server load is reduced when using large chunks 37

38. III. Contribution 38

39.  Internet congestion avoidance relies on communicating end-points that adjust transmission rate to the network conditions  CacheCast transparently removes redundancy increasing network capacity  It is not given how congestion control algorithms behave in the CacheCast presence 39

40.  CacheCast implemented in ns-2  Simulation setup:  Bottleneck link topology  100 TCP flows and 100 TFRC flows  Link cache operating on a bottleneck link 40

41.  TCP flows consume the spare capacity  TFRC flows increase end-to-end throughput  CacheCast preserves the Internet ‘fairness’ 41

42.  J. Santos and D. Wetherall, “Increasing effective link bandwidth by suppressing replicated data,” USENIX’98  A. Anand, A. Gupta, A. Akella, S. Seshan, and S. Shenker, “Packet caches on routers: the implications of universal redundant traffic elimination,” SIGCOMM’08  A. Anand, V. Sekar, and A. Akella, “SmartRe: an architecture for coordinated network-wide redundancy elimination,” SIGCOMM’09 42

43. I. Packet caching system can achieve near multicast bandwidth savings II. Packet caching system requires server support III. Packet caching system is incrementally deployable IV. Packet caching system preserves fairness in Internet 43

44. I. Packet caching system can achieve near multicast bandwidth savings II. Packet caching system requires server support III. Packet caching system is incrementally deployable IV. Packet caching system preserves fairness in Internet 44

45. I. Packet caching system can achieve near multicast bandwidth savings II. Packet caching system requires server support III. Packet caching system is incrementally deployable IV. Packet caching system preserves fairness in Internet 45

46. I. Packet caching system can achieve near multicast bandwidth savings II. Packet caching system requires server support III. Packet caching system is incrementally deployable IV. Packet caching system preserves fairness in Internet 46

47.  Getting CacheCast into the real world  Server support  Link cache  II and III generations of router 47

48. 48

49.  msend() system call and an auxiliary system command tool  Implemented in Linux  Simple API  msend() compared with the standard send() system call for a various destination group size and payload size msend(fd_set *fds_write, fd_set *fds_written, char *buf, int len) 49

50. Index 0 1 2 Index 0 1 2 Payload ID 0 0 0 Payload - AP1x A 0 Cache miss CMU tableCache store CMUCSU 50

51. Index 0 1 2 Index 0 1 2 Payload ID P1 P2 P3 Payload - BP2x P1 B1 P2 P3 Cache hit P2 CMU tableCache store CMUCSU 51