1. Beyond Server Selection: Challenges in Multiple-Origin Content Distribution Mostafa H. Ammar College of Computing Georgia Institute of Technology Atlanta, GA ammar@cc.gatech.edu

2. Contributors  Ellen Zegura  Hyewon Jun  Christos Gkantsidis  Pradnya Karbhari  Matt Sanders  Li Zou

3. Multiple-Origin Content Distribution Systems  Content is Replicated  Authoritative  Grass-roots (Peer-to-Peer)  Content is Re-constituted

4. Challenges  Server Selection Benefit of content replication can only be realized with proper selection  Multipoint-to-point sessions … on their way to becoming a dominant communication paradigm in a network that was designed for pt-to-pt connections

5. Talk Outline  Server Selection  Application-Layer Anycasting  Selection vs Binding  Multipoint-to- point sessions  Impact of Parallel Downloading  Per Session Rate Allocation Please forgive lack of references

6. Talk Outline  Server Selection  Application-Layer Anycasting  Application vs Network-Layer Anycasting  Multipoint-to- point sessions  Impact of Parallel Downloading  Per Session Rate Allocation

7. Server Replication  Server Selection Problem How does a client determine which of the replicated servers to access  Interested in Wide-Area Replication

8. Anycasting  Network-Layer Anycasting in RFC 1541  Anycast IP addresses  Network-layer metrics  Per-packet selection

9. Application-Layer Anycasting  Group of servers identified by Anycast Name  Clients request service from group identified by name  Automatic connection to a “good” server

10. An Architecture Resolver Orange Server Group Green Server Group Green Service? Go to server y Server y

11. Resolver  “Close” to client  Maintains  Anycast group membership  Selection-enabling information  Client may provide filter that tells resolver how to select  DNS-like hierarchy of resolvers

12. Web Server Selection  An instantiation of architecture  Criterion: Best Response Time  [client request, last byte received]  includes path and server delays  Problem: Maintaining response time estimate for each server in anycast group at resolver

13. Response Time Estimation Alternatives  Probe  Push  User-Experience  Developed a Hybrid Push/Probe Technique

14. Wide-Area Experiments 4 3 5 3 4 51 5 5 3 UCLA WU UMD GT Servers: UCLA, GTx2, WU, Clients: UMDx4, GTx16, Resolvers: UMD, GT

15. Anycasting VS Random Selection

16. What if Anycasting is popular?

17. Checkpoint  Appropriate guidance of clients to servers is an important infrastructure function  Client-perceived as well as global performance can be improved with the appropriate selection technology  What about a network-layer anycasting infrastructure?

18. Talk Outline  Server Selection  Application-Layer Anycasting  Application vs Network-layer Anycasting  Multipoint-to- point sessions  Impact of Parallel Downloading  Per Session Rate Allocation

19. Selection vs Binding

20.  Selection: A function that returns instantaneous server choice.  Binding: An application-level function which decides on the use a particular server.

21. Spectrum Of Binding

22. Spectrum of Binding (2)  Initial Binding (IB) : Select one server and stay with it during the connection life time  Periodic Binding (PB) : Periodically select a server and switch to the new server.  Continuous Binding (CB) : Select the best server per packet to react fast to the server performance change

23. Design Space App-Layer Anycasting Our Own Server Migration Protocol The desirability of a network-layer anycasting infrastructure depends on whether Continuous Binding can be shown to outperform Initial Binding

24. Migration of a CB Client

25. Simulation Topolgy

26. Initial vs. Continuous Binding Server Rank Change every [1,10] secServer Rank Change everfy [51,60] sec  Despite the overhead of migration, Continuous Binding is able to improve performance when the connection is long-lived.

27. Heterogeneous Binding Increasing use of either scheme over the other by all clients with long-lived connections leads to overall performance degradation!

28. Checkpoint  Network-layer anycasting allows for efficient continuous binding  Continuous binding outperforms initial binding in some long transfer, highly-dynamic situations  Did not account for overhead of selection function  But we have something more sinister to worry about ….

29. Talk Outline  Server Selection  Application-Layer Anycasting  Application vs Network-layer Anycasting  Multipoint-to- point sessions  Impact of Parallel Downloading  Fairness

30. Motivation  Traditional data retrieval- over a point- to-point connection from a single server to a single client  Current trend- retrieval over multiple point-to-point connections from multiple servers to a single client  examples: CDNs, replicated servers, caches, parallel file downloads, web- traffic, MD-CDNs

31. What is a Session?  Definition of multipoint-to-point session:  A set of point-to-point connections started from multiple servers to a single client in order to transfer an application-level object

32. Typical Sessions in the Internet

33. Typical Sessions

34. Talk Outline  Server Selection  Application-Layer Anycasting  Application vs Network-layer Anycasting  Multipoint-to- point sessions  Impact of Parallel Downloading  Per Session Rate Allocation

35. Impact of Parallel Downloading Question 1: How much can a single user gain by parallel downloading? Question 2: What happens if all users perform parallel downloading? Question 3: How do parallel downloading users affect single downloading users?

36. Aggressiveness pays off. Number of servers Time (in sec) For a ~7MB file: Best rate: ~3Mbps. 4x faster than single server.

37. Wide deployment of Parallel Downloading  More Connections  Number of competing flows increases.  More requests at the server (but, for a shorter period of time).  More Overhead  Fixed overhead is paid multiple times: Cost of a request = {size, rate, etc.}-Dependent cost + Fixed Cost.

38. Many aggressive clients are harmful!

39. Aggressive clients can hurt simple clients

40. Summary  There is strong local incentive for a client to use parallel downloading.  But if every one does it there is evidence global performance suffers  We need a per session rate allocation.

41. Talk Outline  Server Selection  Application-Layer Anycasting  Application vs Network-layer Anycasting  Multipoint-to- point sessions  Impact of Parallel Downloading  Per-Session Rate Allocation

42. Our Goal  To develop algorithms to achieve rate allocations which are fair to all sessions  Some challenges:  Data path of each session forms a tree  Every session has multiple bottlenecks  Partial sharing of bottlenecks between sessions  Inter-session and Intra-session fairness

43. Focus on Static Sessions  For purposes of rate allocation, connections start and terminate at approximately the same time  Examples: parallel file downloads, multimedia streaming using MD-CDNs

44. Current Rate Allocation Approach  Max-min fairness, TCP fairness  Problems with allocating rate on a per- connection basis:  sessions with more connections get higher rate allocation than sessions with fewer connections  this is not a fair rate allocation from a session point of view

45. Proposed Session Fair Approaches (1)  Normalized rate session fairness  rate allocation is based on weight of each connection  weights w i,j are assigned to each connection j in each session i, subject to the constraint:  this constraint ensures that total session rates are fair with respect to each other

46. Proposed Session Fair Approaches (2)  Per-link session fairness  rate allocation at each link on a per-session basis  each session then allocates this rate amongst the connections that traverse that link  this ensures fair allocation of session rates

47. Example- Connection fair

48. Example - Normalized rate session fair

49. Example- Per-link session fair

50. Simulation Model and Fairness Measures  100,600-node topologies using GT-ITM  varying percentages of clients and servers  sessions with 1,4,15 connections with varying percentages  fairness measures: variance, mean, maximum, minimum of session rates and fairness index

51. Evaluation- fairness index  criterion: fairness index-  fairness index of 1 implies a very fair (equal) distribution  session fair rate allocations achieve a better fairness index than connection- fair rate allocations

52. Fairness indices of session rates for different algorithms

53. Variance of session rates

54. Checkpoint  Multipoint to point sessions are increasingly a predominant mode of communication in the Internet.  Per-Session rate allocation seems a natural response to better control sharing behavior.  To DO:  Implement the protocols and architecture for realizing session-fair rate allocations  Extend this framework to dynamic sessions with multiple connections starting and ending at different times

55. Concluding Remarks  Moving content around is the primary function of wide-area networks today  Emerging services and paradigms provide new challenges  Content Replication  Server Selection  Multipoint-to-point sessions  Resource sharing questions  Peer-to-Peer  that’s another story …