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Experimental Distributed File System

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README.md

ClusterDFS - A Experimental Distributed Storage System

Copyright (C) 2012 Lluis Pamies-Juarez

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Description

ClusterDFS is an experimental distributed storage system implemented in python for fast prototyping of network codes for storage. The main features of ClusterDFS are:

  • Fast I/O and concurrency thanks to the asynchronous network library and lightweight threads offered by gevent.
  • Network buffers are wrapped with GaloisBuffer arrays that allow to perform encoding operations directly over them, without requiring extra memory allocations.

The current implementation allows to execute different DataNodes (bin/datanode) across different computers, allowing to store, retrieve and encode data in each of them.

Basic Operations

The basic operations implemented by now in DataNode are:

  • STORE: Reads a network stream and stores it in the local file-system with a specific ID.
  • RETRIEVE: Serves a stored block from the local file-system.
  • CODING: Executes a coding operation. The ID of the coding operation and its implementation is stored in DataNodeConfig.coding_mod_name. Returns the coding result as a network stream.

Coding Operations

When the DataNode executes a CODING operation, it retrieves a list of operations from DataNodeConfig.coding_mod_name and executes them sequentially for all buffers in a data stream. The list of operations is a combination of the following:

  • LOAD: Loads a file or network buffer under a certain ID. When loading from a network, it can load the result of a remote coding operation.
  • WRITE: Writes a buffer to a file.
  • IADD: Performs an in-place addition of two buffer (xor operation).
  • MULT: Performs an in-place multiplication of two buffer (Galois arithmetic).
  • MULADD: Multiplies two buffers and adds the result to one of the buffers (Galois arithmetic).

RapidRAID Codes

In clusterdfs/rapidraid.py we provide an implementation of a pipelined erasure code scheme that encodes 11 replicated data blocks to generate 16 parity blocks. This RapidRAID implementation is a (16,11) erasure code defined as a set of 16 instruction lists, each to be executed in each of the storage nodes.

Future Directions

  • Addition of a NameNode (possibly distributed in a DHT) to monitor the blocks stored in each of the *DataNode*s.
  • Implement a CODING operation to operate in a PUSH manner, sending/encoding data from a source to different nodes, instead of the PULL strategy implemented now where a node requests encoded data from other nodes.
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