Peer-to-Peer Filesystems

Tom Roeder

CS414 2005sp

Nature of P2P Systems

We discussed this a little in 415 on Friday P2P: communicating peers in the system normally an overlay in the network

In some sense, P2P is older than the name many protocols used symmetric interactions not everything is client-server

What’s the real definition? no-one has a good one, yet depends on what you want to fit in the class

Nature of P2P Systems

Standard definition symmetric interactions between peers no distinguished server

Minimally: is the Web a P2P system? We don’t want to say that it is but it is, under this definition I can always run a server if I want: no asymmtery

There must be more structure than this Let’s try again

Nature of P2P Systems

Recent definition No distinguished initial state Each server has the same code servers cooperate to handle requests clients don’t matter: servers are the P2P system

Try again: is the Web P2P? No, not under this def: servers don’t interact

Is the Google server farm P2P? Depends on how it’s set up? Probably not.

Overlays

Recall: two types of overlays Unstructured

No infrastructure set up for routing Random walks, flood search

Structured Small World Phenomenon: Kleinberg Set up enough structure to get fast routing We will see O(log n) For special tasks, can get O(1)

Overlays: Unstructured

From Gribble a common unstructured overlay look at connectivity more structure than it seems at first

Overlays: Unstructured

Gossip: state synchronization technique Instead of forced flooding, share state Do so infrequently with one neighbor at a time Original insight from epidemic theory

Convergence of state is reasonably fast with high probability for almost all nodes good probabilistic guarantees

Trivial to implement Saves bandwidth and energy consumption

Overlays: Structured

Need to build up long distance pointers think of routing within levels of a namespace eg. namespace is 10 digit numbers base 4

0112032101 then you can hop levels to find other nodes

This is the most common structure imposed

Distributed Hash Tables

One way to do this structured routing Assign each node each node an id from space eg. 128 bits: SHA-1 salted hash of IP address build up a ring: circular hashing assign nodes into this space

Value diversity of neighbors even coverage of space less chance of attack?

Distributed Hash Tables

Why “hash tables”? Stored named objects by hash code Route the object to the nearest location in space key idea: nodes and objects share id space

How do you find an object without its name? Close names don’t help because of hashing

Cost of churn? In most P2P apps, many joins and leaves

Cost of freeloaders?

Distributed Hash Tables

Dangers Sybil attacks: one node becomes many id attacks: can place your node wherever Solutions hard to come by

crytpo puzzles / money for IDs? Certification of routing and storage?

Many routing frameworks in this spirit Very popular in late 90s early 00s Pastry, Tapestry, CAN, Chord, Kademlia

Applications of DHTs

Almost anything that involves routing illegal file sharing: obvious application backup/storage filesystems P2P DNS

Good properties O(log N) hops to find an id Non-fate-sharing id neighbors Random distribution of objects to nodes

Pastry: Node state

Pastry: Node Joins

Find another geographically nearby node Hash IP address to get Pastry id Try to route a join message to this id get routing tables from each hop and dest select neighborhood set from nearby node get the leaf set from the destination Give info back to nodes so they can add you

Assuming the Pastry ring is well set up, this procedure will give good parameters

Pastry: Node Joins

Consider what happens from node 0 bootstraps itself next node to come adds itself and adds this node Neighborhood information will be bad for a while

need a good way to discover network proximity This is a current research problem

On node leaves, do the reverse If a node leaves suddenly, must be detected removal from tables by detecting node

Pastry: Routing

The key idea: grow common prefix given an object id, try to send to a node with at

least one more digit in common if not possible, send to a node that is closer

numerically if not possible, then you are the destination

Gives O(log N) hops Each step gets closer to destination Guaranteed to converge

Pastry: Routing

PAST: Pastry Filesystem

Now a simple filesystem follows: to get a file, hash its name and look up in Pastry to store a file, store it Pastry

Punt on metadata/discovery Can implement directories as files Then just need to know the name of root

Shown to give reasonable utilization of storage space

PAST: File Replication

Since any one node might fail, replicate Uses the neighbor set for k-way storage Keeps the same file at each neighbor Diversity of neighbors helps fate-sharing

Certification Each node signs a certificate

Says that it stored the file Client will retry storage if not enough certificates

OK guarantees

PAST: Tradeoffs

No explicit FS structure: Could build any sort of system by storing files Basically variable-sized block storage mechanism This buys simplicity at the cost of optimization

Speed vs. storage See Beehive for this tradeoff Makes it an explicit formula; can be tuned

Ease of use vs. security Hashes make file discovery non-transparent

Rationale and Validation

Backing up on other systems no fate sharing automatic backup by storing the file

But Cost much higher than regular filesystem Incentives: why should I store your files? How is this better than tape backup? How is this affected by churn/freeloaders Will anyone ever use it?

PAST: comparsion to CFS

CFS: a filesystem built on Chord/DHash Pastry is MSR, Chord/DHash is MIT Very similar routing and storage

PAST: comparison to CFS

PAST stores files, CFS blocks Thus CFS can use more fine-grained space lookup could be much longer

get each block: must go through routing for each CFS claims: ftp-like speed

Could imagine much faster: get blocks in parallel thus routing is slowing them down Remember: hops here are overlay, not internet, hops

Load balancing in CFS predictable storage requirements per file per node

References

A. Rowstron and P. Druschel, "Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems". IFIP/ACM International Conference on Distributed Systems Platforms (Middleware), Heidelberg, Germany, pages 329-350, November, 2001.

A. Rowstron and P. Druschel, "Storage management and caching in PAST, a large-scale, persistent peer-to-peer storage utility", ACM Symposium on Operating Systems Principles (SOSP'01), Banff, Canada, October 2001.

Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, and Hari Balakrishnan, Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications, ACM SIGCOMM 2001, San Deigo, CA, August 2001, pp. 149-160.

References

Frank Dabek, M. Frans Kaashoek, David Karger, Robert Morris, and Ion Stoica, Wide-area cooperative storage with CFS, ACM SOSP 2001, Banff, October 2001.

Stefan Saroiu, P. Krishna Gummadi, and Steven D. Gribble. A Measurement Study of Peer-to-Peer File Sharing Systems, Proceedings of Multimedia Computing and Networking 2002 (MMCN'02), San Jose, CA, January 2002.Kleinberg

C. G. Plaxton, R. Rajaraman, and A. W. Richa. Accessing nearby copies of replicated objects in a distributed environment. In Proceedings of the 9th Annual ACM Symposium on Parallel Algorithms and Architectures, Newport, Rhode Island, pages 311-320, June 1997.

Conclusions

Tradeoffs are critical Why are you using it? What sort of security/anonymity guarantees?

DHT applications Think of a good one and become famous

PAST caches whole files Save some routing overhead Harder to implement true filesystem