CAN

1. CAN • Distributed Hash Tables • DHT recap • Uses • Example – CAN

2. What are DHTs? • A DHT is a topology that provides similar functionality to a typical hash table. • put(key, value) • get(key) • Peers are buckets in the table • with their own local hash tables • Allows a peer to publish a resource onto a network using a key to determine where the data will be stored (i.e. which peer will receive the data). • Using keys presupposes a logical ‘space’ which the keys map onto. • The key is mapped to the space using a hashing function to ensure equal distribution of resources across the network. • Nodes are responsible for sections of this space.

3. Why DHTs? • Address the flooding issue without resorting to centralized/decentralized architecture. • no super peers, no power law distribution • Typically search can be achieved in O(logn) hops where n is the number of nodes in the network. • only a few neighbors need to be known – typically O(logn) • small neighborhoods and flat topology makes for a robust network, easy to handle churn. • DHTs guarantee locating a file (or where it should be) • deterministic • unlike unstructured systems such as Gnutella, KaZaA

4. DHTs Uses • Not just file sharing • BitTorrent trackers in a DHT • super node-like caching (Skype possibly does this) • location independent naming service • typically DHTs are used as a backbone of relatively powerful nodes supporting weaker nodes • because DHTs are flat and presume equality of capability.

5. Content Addressable Network - CAN • CAN stands for ‘content-addressable network’. • A network that provides a routing overlay on top of a physical network to optimise publishing and searching for data. • Addresses the ‘flooding’ issue. • CAN is based on the Distributed Hash Table (DHT) concept.

6. How does CAN work? • The CAN space is defined as a d-dimensional Cartesian coordinate space. • At any given time the entire coordinate space is divided amongst the nodes in the system. • Each node owns its own distinct zone within the overall space. • A uniform hashing algorithm is used to map a key to coordinate space • k -> coord{x, y, z} • not specified by CAN • as long as it has the properties of a uniform hash, i.e., even distribution. • messages contain the destination coordinates. • and are routed to the peer whose zone contains the coordinates

7. Example 2-D Space 0,1 1,1 B (0-0.5,0.5-1) D (0.5-0.75, 0.5-1) E (0.75-1, 0.5-1) A (0-0.5,0-0.5) C (0.5-1,0-0.5) 1,0 0,0

8. Neighbours • A node is considered a neighbour if its zone overlaps along d-1 dimensions and abuts along one dimension. • A node maintains info about its neighbours – a contact address and its zone coordinates. • An evenly divided space means each node has 2d neighbours

9. Neighbours(torus - the space wraps) B (A, D, E) D (B, E, C) E (B, D, C) A (B, C) C (A, D, E)

10. Routing • Routing happens by following a straight line path through the Cartesian space from source to destination coordinates. • A message with destination point P is routed to the neighbour whose zone coordinates are closest to P. • There are multiple paths available at any point.

11. Routing P(x,y) to P(x,y)

12. Joining the CAN • To join the CAN, as with many other systems, a node needs a bootstrap node, i.e. the address and coords of a node already in the system. • When a new node wants to join it randomly chooses a point in the coordinate system. • The message is routed to the node whose coordinate space contains the point. • That node splits its space in half, keeping one half and handing over the other to the new node.

13. …Joining 6 2 9 chooses a point (x,y) In the space. The bootstrap node initiates the Routing. Node 1 splits and hands Over half its zone and Relevant neighbours to 9. 9 wants to join. It Finds a bootstrap Node (out of bounds). Let’s say it’s 5. Node 1’s zone Contains point (x,y) 1 9 3 1 (x,y) 4 5 8 9 Bootstrap node

14. CAN Structure • When a node wants to leave the network it finds a neighbour it can merge its zone with. • Because the coordinate space is recursively divided in half, the network can be though of as a binary tree in which every network node/zone is a leaf on the tree. • Vertices are previously partitioned zones in a particular dimension. • If a node takes over a sibling’s zone both child nodes of the tree are merged and become their parent.

15. CAN Seen as a Tree 1 3 4 2

16. Scalability • The number of neighbours maintained by a node is a function of the amount of dimensions not the overall size of the CAN – so more dimensions – more neighbours. • However, more dimensions means shorter routing paths. As the node number increases, the routing paths grow as O(n1/d). • i.e. tradeoff between neighbour numbers (and hence maintenance overhead) and path length

17. Maintenance of the CAN • Nodes send each other periodic update messages. • zone coords, list of neighbour nodes and their coords • If a node doesn’t hear from a neighbour after a given amount of time, it initiates a TAKEOVER. • starts a timer that is relative to its zone size • the bigger its zone size, the longer the timer • All the neighbours of the dead node are doing this. • The one with the shortest timer times out, and tells the dead node’s neighbours • It then tries to merge its zone with the dead node’s zone. • If a complete zone cannot be made through merging, the smallest known node temporarily looks after two zones • A zone can be merged with another if itscoords in d-1 dimensions overlap and the remaining dimensions abut and have an equal width.

18. CAN Repair Algorithm • Easiest envisaged using the binary tree conception. • If a node finds itself with a zone(call it zone B) that it cannot merge with its existing zone (A) : • check if B has a sibling that is also a leaf • this is easy - merge • if not, perform a depth first search down the subtree rooted at B, until two siblings are found. • then merge those as usual • (leaves one node without a zone) • hand over B to the node with no zone after merging

19. Tree Again depth first search for node with sibling B A

20. Tree Again merge siblings into parent node B A

21. Tree Again assign empty node to unoccupied zone B A

22. Resource Availability • If a node fails, then the resources go with it. • So entities that publish need to periodically refresh the data. • Resource duplication is another mechanism. This can be done in two ways: • Overloading coordinate zones • multiple nodes share zones and the resources mapped to them. • They know about each other • data can be either replicated or partitioned • new peers can choose neighbours with a lower latency • Multiple ‘realities’…

23. Realities Realities can be ‘stacked’ so a node maintains different zones in different realities concurrently. This allows for duplication of resources and shorter routing paths – i.e. in a 2-D coordinate system routing can happen ‘horizontally’ and ‘vertically’.

24. Further Design Extensions • Caching • because of multiple paths to a key, caches can grow with the popularity of a key • i.e. if I route a particular key many times, I can decide to get the data and store it myself. • Location awareness • use landmarks, e.g. DNS root name servers • measure Round Trip Time (RTT) • order nodes according to RTT • when joining, choose a node whose landmark ordering is similar • means close nodes make up neighbourhoods