Rust implementation of Generalized Deduplication (GD) based on several types of error-correcting codes.
This is an implementation (and somewhat extension) of the novel concept of data deduplication method, called Generalized Deduplication (GD). The original concept of GD was introduced by a group of Aarhus University, Denmark, leaded by Prof. D. E. Lucani.
- Vestergaard, Rasmus, Qi Zhang, and Daniel E. Lucani. "Generalized deduplication: bounds, convergence, and asymptotic properties." 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019.
- Vestergaard, Rasmus, Daniel E. Lucani, and Qi Zhang. "Generalized deduplication: Lossless compression for large amounts of small IoT data." European Wireless 2019; 25th European Wireless Conference. VDE, 2019.
- etc.
Add the following to your Cargo.toml
as imported directly from GitHub:
toml:Cargo.toml
[dependencies]
rust-gd = { git = "https://github.com/junkurihara/rust-gd.git" }
or from crates.io:
toml:Cargo.toml
[dependencies]
rust-gd = "*" // or appropriate version
Then, add use
in your .rs
file.
rust:
use rust_gd::*;
NOTE: The compression rate strongly depends on the data alignment and data structure. So you should carefully choose the parameters according to the characteristics of given data.
```rust: use rust_gd::*;
let tobededuped: &[u8] = "寿限無(じゅげむ)寿限無(じゅげむ)五劫(ごこう)のすりきれ海砂利(かいじゃり)padpadpadpadpadpadpadpad".tostring().repeat(128).asbytes()
let codelen = 128; // codeword length over GF(256), i.e., N in (N, K) RS code let msglen = 124; // message length over GF(256), i.e., K in (N, K) RS code let dict_size = 127; // max entry size of a dictionary used in GD process
// GD instance for deduplication (compress) let mut gddedup = GD::ReedSolomon(codelen, msglen).setup(dictsize).await.unwrap(); // Async API
// GD instance for duplication (decompress) let mut gddup = GD::ReedSolomon(codelen, msglen).setup(dictsize).await.unwrap(); // Async API
// struct Deduped = {pub data: Vec
let duped: Vec
assert_eq!(duped, words); ```
In GD with RS codes, an approach of error-alignment can be employed by
```rust: // Linear transformation matrix used for error-alignment. This must be nonsinglar. let trans: [&[u8; 4]; 4] = [ &[1, 0, 0, 0], &[1, 1, 1, 4], &[1, 1, 3, 0], &[1, 2, 0, 0], ];
// Instantiation let mut gddedup = GD::ReedSolomon(4, 3).setup(15).await.unwrap(); let mut gddup = GD::ReedSolomon(4, 3).setup(15).await.unwrap();
// Set error alignment let resdedup = gddedup.seterroralignment(trans).await; // this simply returns Result<()> let resdup = gddup.seterroralignment(trans).await; // this simply returns Result<()> assert!(resdedup.isok()); assert!(resdup.isok());
// then use gd instances to deduplicate/duplicate data as above. ```
For the detailed design of RS-code based implementation and the basic idea error-alignment, see DESIGN.md.
```rust: let hammingdeg = 4; // Degree m of (2^m - 1, 2^m - m -1) Hamming code let hammingdict_size = 511; // max entry size of a dictionary used in GD process
let tobededuped: &[u8] = "寿限無(じゅげむ)寿限無(じゅげむ)五劫(ごこう)のすりきれ海砂利(かいじゃり)padpadpadpadpadpadpadpad".tostring().repeat(128).asbytes()
// GD instance for deduplication (compress) let mut gddedup = GD::Hamming(hammingdeg).setup(hammingdictsize).await.unwrap(); // Async API
// GD instance for duplication (decompress) let mut gddup = GD::Hamming(hammingdeg).setup(hammingdictsize).await.unwrap(); // Async API
// struct Deduped = {pub data: Vec
let duped: Vec
Currently, our GD implementation is based only on Hamming and Reed-Solomon (RS) codes. The GD based on RS codes processes data chunks as byte stream. On the other hand, Hamming-based GD serves data chunks as bit stream.
For GD implementation using Hamming codes, Hamming code with the degree $m = 3$ of the code works in the internal libecc
library of error-correcting codes, i.e., a case of the code length $n = 2^m - 1 = 7$. However, the Hamming code of $m = 3$ cannot be employed as the underlying linear code of Hamming-based GD. This is because the code length, i.e., $n=7$ bits, is not sufficient to deduplicate a "byte"-based data. In order to reasonably deduplicate byte-based data, byte alignment is needed. So, we omitted $m = 3$ and considers the parameter $m \geq 4$.
Byte alignment: Our implementation employs an encoding method that chunks message sequences in the unit of bytes. For example, if $(15, 11)$ Hamming code is employed, a 2-byte message is divided into two one byte (= 8 bits) sequences, and pads $15-8=7$ bits of zeros to each sequence to deal as a 15-bit codeword of Hamming code.
Following should be considered to be implemented.
Benchmark for the performance of deduplication
Optimization of math operations
Deletion and deviation using PRNG (Yggdrasil paper)
Golomb-Rice codes
At this time this solution should be considered suitable for research and experimentation, further code and security review is needed before utilization in a production application.
Licensed under the MIT license, see LICENSE
file.