1. Overhaul: Extending HTTP to Combat Flash Crowds Jay A. Patel & Indranil Gupta Distributed Protocols Research Group Department of Computer Science University of Illinois at Urbana-Champaign (UIUC) Urbana, Illinois, USA

2. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign2 Introduction  Flash crowd: A stampede of unexpected visitors  Occurs regularly due to linkage from popular news feeds, web logs, etc. Popularly termed “Slashdot effect”  Victim sites become unresponsive Perception of dysfunction

3. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign3 Example: MSNBC MSNBC home page December 14, 2003

4. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign4 Motivation  Problem Unpredictable, yet frequent Brief period of time Thousand-fold increase in traffic  Two naïve solutions Overly insure on resources Shut down web site

5. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign5 Current Solutions  Architectural Changes SEDA Capriccio ESI  Protocol Modifications DHTTP Web Booster  Cooperative Sharing Squirrel Kache Backslash BitTorrent

6. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign6 HTTP: Regular Interaction Client Server GET Request Response Document Header

7. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign7 Overhaul: Overview  Protocol change HTTP extension, no modification  5 new tags added, 1 slightly modified Backwards compatible  Key concept: chunking Characteristic of the web applied to individual documents m chunks per document  P2P distribution framework Voluntary Ad hoc, not DHT based Key benefit: parallel resource discovery

8. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign8 Overhaul: Design Client Server Client #1 #2 #4 #3 HTTP Request with Overhaul support tag Chunked Response with Overhaul headers Peers exchange chunks to fetch the complete document Ad hoc peer network

9. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign9 Details: Client/Server Interaction  Initial request by client Supports: Overhaul $port $speed  Response by server in Overhaul mode i th chunk transmitted in sequential order Signatures of other m-1 chunks for verification Initial Overhaul network membership list  n most-recent Overhaul clients  List maintained at server (updated with every request)

10. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign10 Details: Peer Clients’ Interaction  Clients contact other peer members To fetch remaining chunks To discover new peers  Aggregate membership list by swapping information  1-hop random walk discovery process  Resource discovery Lookup documents on a busy Overhaul server  Contact peers randomly on membership list  INFO $host.tld

11. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign11 Implementation  Server Apache/2.0 HTTP server Module: mod_overhaul  Client Java HTTP Proxy Cross platform Universal client support

12. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign12 Testing Methodology: Server  Server machine 2.5 GHz AMD Athlon XP+ 1 GB RAM  Client machine 650 MHz Pentium III 320 MB RAM  Same network equipment  25 concurrent fetches  ApacheBench utility

13. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign13 Results: Chunking (Fixed Size) Document: 10 KB Concurrency: 25 Regular HTTP 512-byte chunks 2048-byte chunks Overhaul mode requires the server to send only a single chunk

14. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign14 Results: Chunking (Maximum Count) Regular HTTP 6 chunks 12 chunks 24 chunks Document: 50 KB Concurrency: 25

15. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign15 Results: Overhaul vs. Regular Regular HTTP 6 chunks 12 chunks Concurrency: 25 Minimum chunk size: 512-bytes

16. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign16 Testing Methodology: Client  Cluster of workstations 25 homogenous PCs  2.8 GHz Intel Pentium 4  1 GB RAM Same network equipment  Two experiments Concurrent: single document Staggered: multiple documents

17. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign17 Results: Single Document  Large document: 50 KB (12 chunks)  Server condition: 150- 250 concurrent fetches + competition  Overhaul requests: concurrently only using 24 Overhaul-aware clients Regular requests Overhaul mode Fastest1 sec6 secs Slowest32 secs9 secs Average9 secs7 secs Server bandwidth usage in Overhaul mode: 1/12 th of regular requests

18. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign18 Results: Multiple Documents  8 documents: 110 KB total (12 chunks)  Server condition: 150-250 concurrent fetches + competition  Overhaul requests staggered 1 st stage: 12 concurrent fetches, fetch all documents 2 nd stage: 12 concurrent fetches, fetch index document only Regular requests Overhaul mode Fastest1 sec14 secs Slowest∞28 secs Average23 secs * 18 secs Server bandwidth usage in Overhaul mode : 1/18 th of regular requests * indicates completed requests

19. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign19 Limitations  Both client and server must be Overhaul aware  Requires critical mass to be maintained to remain effective n clients > m chunks  More responsibilities for the client  Possible security implications

20. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign20 Conclusion  Saves resources Bandwidth  The bigger the crowd, the lower the per capita usage Response time  Faster turnaround for both server and client  Getting wide spread acceptance Marginal cost Protocol extension requires industry and standards push

21. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign21 Overhaul Vs. BitTorrent  Specifically intended for flash crowds Feasible for short durations Small document size  Tightly integrated for HTTP Another server/software not required Resource discovery: built-in notion of related documents

22. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign22 Regarding Greedy Clients  Voluntary network  Must increase membership list to fetch document(s) faster Forces communication and sharing  Future work Trust score matrix based on sharing

23. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign23 Heterogeneous Networks  Problem Connections are heterogeneous  Solutions Clustering of clients Super nodes Client

24. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign24 Document Selection  Only a partial set of documents are affected by a flash crowd  Must implement selective Overhaul mode  Automatic selection Active monitoring Server Large collection of documents reside on the server Documents fetched by a flash crowd

25. Distributed Protocols Research Group, Department of Computer Science, University of Illinois at Urbana-Champaign25 Dynamic Documents  Flash crowds especially frequent during big events and news  Characteristic: rapidly, changing data  Solutions Time stamping Expiration of chunks Inter-network refresh from peers