1. Locality Aware Network Solutions Dahlia Malkhi The Hebrew University of Jerusalem

2. 2 A Brief Overview of Distributed Computing The 90 ’ s: –Internet activity: Web browsing –Paradigm: Client-server –Techniques: cluster computing, Paxos, group communication

3. 3 A Brief Overview of Distributed Computing The 90 ’ s: 2000- –Internet activity: File sharing –Paradigm: P2P, grid, web-services –Techniques: overlay networks, content distribution networks, resource location

4. 4 Application: IPv6 Routing over IPv4 [van Renesse 02] AF3S:::3FF1:43E4 0001:::3BBB:5555 1111:::7777:7754 2222:::2222:2222 EEE0:::EEEE:EEEE 5151:::6161:6666 567A:::0202:0202 8888:::0909:9999 Distribute Hash Tables (DHT)

5. 5 Application: Content Delivery / Finding Nearest Copies of Data ? ? ?

6. 6 Application: Hyperencryption [Maurer 92, Ding & Rabin 02] Random bits Alice Bob Key Adversary bits

7. 7 Application: A Hyperencryption P2P Network [Rabin 03] Distributed Hash Table (DHT)

8. 8 Application: A Distributed Google? ?

9. 9 ever-growing global scale-free networks, their provisioning, repair and unique functions EVERGROW The Vision ultimate RAID ultimate GNUTELLA ultimate GOOGLE ultimate AKAMAI infrastructure and new methods and systems devoted to measurement, mock-up and and analysis of present and future network traffic, topology and logical structure, to bridge the gap in theory, protocols and understanding to what the Internet can be in 2025. An EC project. Coordinators: SICS (Sweden) and HUJI (Jerusalem)

10. 10 Scalable Network Solutions Overlay networks provide added functionality at the application level –Search, routing, location services Network theory provides the foundations –Possibilities, impossibilities, lower/upper bounds Practical solutions require flexible deployment

11. 11 Distributed Data Structures (DDS) Peers jointly implement a data structure, e.g., hash table Route queries based on data-name (key)

12. 12 DDS Problem Reduced to Routing ?? 00001111 Responsible for 00001111

13. 13 Why classic routing network designs don’t help Static # of nodes a priori known Node labels designated by network designer 000 111 110 101 001 100 011 010

14. 14 DDS Reduced to Routing The problem: Overlay routing network –Variants: labeled routing, name-independent routing, finding nearest copies Dynamic emulation

15. 15 Distributed Hash Tables [Malkhi, Naor, Ratacjzak, PODC 2002] SchemeDegree Route Chord, Tapestry, Pastry [2001] Log n CAN [2001] dd*n (1/d) Viceroy [2002] 5Log n Koorde, D2B, DH, generic [2003] 2Log n [Abraham, Awerbuch, Azar, Bartal, Malkhi, Pavlov, IPDPS 2003 ]

16. 16 Tree View of Dynamic Graphs Leafs of the tree represent current nodes Inner nodes in the tree represent nodes that were split 000 111 110 101 00 110111 001 100 011 010 000001010011100101 00 Example: merge of 000, 001 into 00

17. 17 Locality awareness source target

18. 18 Locality awareness source target

19. 19 Locality Awareness in Overlay Networks Model the network as a weighted undirected graph –c(x, y): cost of shortest path from x to y –c() is a metric An overlay network is a sub-graph Let x=x 0, x 1, …, x t =y be a route in the overlay network Stretch: Ratio between overlay route cost and shortest path cost: ( c(x, x 1 ) + c(x 1,x 2 ) + … c(x t-1, y) ) / c(x,y)

20. 20 Overlay Networks in Growth-Bounded Metrics Previous work : –[Plaxton, Rajaraman, Rica 1997], Tapestry (Berkeley), Pastry (MS UK) –Expected (large) constant stretch –Logarithmic node degree LAND [Abraham, Malkhi, Dobzinski, SODA 2004] : –Guaranteed stretch (1+ε) –Expected logarithmic node degree, constant depends on growth-bound –Simple, intuitive construction and proofs

21. 21 Overlay Networks in Geometric Spaces Modeling the Internet as a geometric space is practical –Ubiquitous GPS devices –Successful embeddings in virtual coordinate-space Problem 1: Locate nodes Problem 2: Route to known coordinates

22. 22 Location Services and Routing in Geometric Spaces LLS: First fully-locality aware location service [Abraham Dolev Malkhi 2004] –bounded stretch lookup –bounded stretch update First constant-degree routing scheme (to known coordinates) [Abraham Malkhi, PODC 2004] –constant node degree, logarithmic hops, 1+ε stretch

23. 23 Routing in Arbitrary Graphs: Lower and upper bounds Name-independent routing: node names are independent of routing scheme [Awerbuch, Bar Noy, Linial, Peleg 1989] Lower bounds: [Gavoille Gengler 2001] –Stretch < 3  O(n) routing information –Stretch < 5  √n routing information Upper bound: [Abraham, Gavoille, Malkhi, Nisan, Thorup, SPAA 2004] –stretch-3 routing with O(√n ) routing information –Stretch 3 is indeed attainable! General upper bound: [Abraham Gavoille, Malkhi, DISC 2004] –Stretch-k routing with memory O(k 2 k √n )

24. 24 Network nodes

25. 25 Nodes’ random identifiers 0111010 0011110 1111110 0001000 1001001 0101001

26. 26 Coloring and Vicinities

27. 27 Coloring and Vicinities ? ? ? ?

28. 28 Stretch 3 ? d ≤ d ≤ 2d

29. 29 The Full Routing Scheme ? 1 2 3 4 5 ? a b c d

30. 30 Locality-Aware, Robust Overlay for Information Lookup and Content Delivery Degree O(√n) Locality awareness: –Formally stretch 3 –For far-apart nodes, lower stretch Mostly two-hop –Whenever full connectivity exists Flexibility –Estimate √n roughly –Cache information on many vicinity nodes –Store information about any known node of same color Fault tolerance: –Multiple route choices –Quick repair –Maintain QoS in face of churn

31. 31