Exploiting sparse coding: A sliding window enhancement of a random linear network coding scheme

Garrido, Pablo; Gómez, David; Lanza, Jorge; Agüero, Ramón

doi:10.1109/icc.2016.7510783

Cited by 10 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been many other sparse variants of random linear network coding, including chunked codes (e.g., [ 28 , 29 ]), tunable sparse network coding (e.g., [ 30 , 31 ], and sliding-window coding (e.g., [ 32 , 33 , 34 , 35 , 36 ]). While many of these codes can also be applied, BATS codes are more suitable for this distributed computing scenario.…”

Section: Bats-code-based Approachmentioning

confidence: 99%

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

Fan

Tang

et al. 2023

Entropy

View full text Add to dashboard Cite

In wireless distributed computing systems, worker nodes connect to a master node wirelessly and perform large-scale computational tasks that are parallelized across them. However, the common phenomenon of straggling (i.e., worker nodes often experience unpredictable slowdown during computation and communication) and packet losses due to severe channel fading can significantly increase the latency of computational tasks. In this paper, we consider a heterogeneous, wireless, distributed computing system performing large-scale matrix multiplications which form the core of many machine learning applications. To address the aforementioned challenges, we first propose a random linear network coding (RLNC) approach that leverages the linearity of matrix multiplication, which has many salient properties, including ratelessness, maximum straggler tolerance and near-ideal load balancing. We then theoretically demonstrate that its latency converges to the optimum in probability when the matrix size grows to infinity. To combat the high encoding and decoding overheads of the RLNC approach, we further propose a practical variation based on batched sparse (BATS) code. The effectiveness of our proposed approaches is demonstrated by numerical simulations.

show abstract

Section: Bats-code-based Approachmentioning

confidence: 99%

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

Fan

Tang

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…That is, the intermediate nodes first forward a subset of the received systematic packets and interleave coded packets in between the subsets of systematic packets. The source node encodes the original packets using sliding window network coding [69] with a prescribed finite length encoding window [37], [65], [70]- [75], set to w fin = 5.…”

Section: ) Overviewmentioning

confidence: 99%

“…Fig 1 shows an example of the Dec-Rec scheme for two intermediate nodes N 1 and N 2 and destination D.The source node encodes the original packets using sliding window network coding[69] with a prescribed finite length encoding window[37],[65],[70]-[75], set to w fin = 5. The first packets received by N 1 , as displayed in the receiv.…”

mentioning

confidence: 99%

SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

et al. 2021

View full text Add to dashboard Cite

Sparse Random Linear Network Coding (RLNC) reduces the computational complexity of the RLNC decoding through a low density of the non-zero coding coefficients, which can be achieved through sending uncoded (systematic) packets. However, conventional recoding of sparse RLNC coded packets at an intermediate node in a multi-hop network increases the density of non-zero coding coefficients. We develop and evaluate sparsity-preserving recoding (SpaRec) strategies that preserve the low density of non-zero coding coefficients of sparse RLNC with systematic packets. We develop SpaRec strategies with and without decoding at the intermediate nodes, with and without a specified coding rate, as well as with finite and infinite recoding window lengths. We evaluate the SpaRec strategies in multi-hop networks in terms of packet loss, packet delivery delay, as well as recoding and decoding (computation) throughput. We find that the SpaRec strategies substantially improve the RLNC performance compared to conventional recoding.

show abstract

“…Sparse RLNC [73]- [78] has been examined in a range of contexts, including broadcast systems [79]- [81], data compression [82], and secure communications [83]. The decoding probability and delay for sparse RLNC have been analyzed in [84]- [88], while the tuning of the sparsity has been examined in [89]- [92], and sparsity for sliding window RLNC [42]- [47] has been studied in [93], [94]. To the best of our knowledge, sparse RLNC has not yet been examined in the context of the flexible Fulcrum RLNC approach.…”

Section: B Related Workmentioning

confidence: 99%

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

View full text Add to dashboard Cite

Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.

show abstract

Exploiting sparse coding: A sliding window enhancement of a random linear network coding scheme

Cited by 10 publications

References 17 publications

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

Network Coding Approaches for Distributed Computation over Lossy Wireless Networks

SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

Contact Info

Product

Resources

About