Fulcrum: Flexible Network Coding for Heterogeneous Devices

TY - JOUR

T1 - Fulcrum: Flexible Network Coding for Heterogeneous Devices

AU - Lucani Rötter, Daniel Enrique

AU - Pedersen, Morten V.

AU - Ruano, Diego

AU - W. Sørensen, Chres

AU - Fitzek, Frank H P

AU - Heide, Janus

AU - Geil, Olav

AU - Nguyen, VU

AU - Reisslein, Martin

PY - 2018

Y1 - 2018

N2 - We introduce Fulcrum, a network coding framework that achieves three seemingly conflicting objectives: 1) to reduce the coding coefficient overhead down to nearly n bits per packet in a generation of n packets; 2) to conduct the network coding using only Galois field GF(2) operations at intermediate nodes if necessary, dramatically reducing computing complexity in the network; and 3) to deliver an end-to-end performance that is close to that of a high-field network coding system for high-end receivers, while simultaneously catering to low-end receivers that decode in GF(2). As a consequence of 1) and 3), Fulcrum has a unique trait missing so far in the network coding literature: Providing the network with the flexibility to distribute computational complexity over different devices depending on their current load, network conditions, or energy constraints. At the core of our framework lies the idea of precoding at the sources using an expansion field GF(2 h),h > 1, to increase the number of dimensions seen by the network. Fulcrum can use any high-field linear code for precoding, e.g., Reed-Solomon or Random Linear Network Coding (RLNC). Our analysis shows that the number of additional dimensions created during precoding controls the trade-off between delay, overhead, and computing complexity. Our implementation and measurements show that Fulcrum achieves similar decoding probabilities as high field RLNC but with encoders and decoders that are an order of magnitude faster.

AB - We introduce Fulcrum, a network coding framework that achieves three seemingly conflicting objectives: 1) to reduce the coding coefficient overhead down to nearly n bits per packet in a generation of n packets; 2) to conduct the network coding using only Galois field GF(2) operations at intermediate nodes if necessary, dramatically reducing computing complexity in the network; and 3) to deliver an end-to-end performance that is close to that of a high-field network coding system for high-end receivers, while simultaneously catering to low-end receivers that decode in GF(2). As a consequence of 1) and 3), Fulcrum has a unique trait missing so far in the network coding literature: Providing the network with the flexibility to distribute computational complexity over different devices depending on their current load, network conditions, or energy constraints. At the core of our framework lies the idea of precoding at the sources using an expansion field GF(2 h),h > 1, to increase the number of dimensions seen by the network. Fulcrum can use any high-field linear code for precoding, e.g., Reed-Solomon or Random Linear Network Coding (RLNC). Our analysis shows that the number of additional dimensions created during precoding controls the trade-off between delay, overhead, and computing complexity. Our implementation and measurements show that Fulcrum achieves similar decoding probabilities as high field RLNC but with encoders and decoders that are an order of magnitude faster.

KW - Decoding probability

KW - random linear network coding (RLNC)

KW - resource-constrained devices

KW - throughput

UR - http://www.scopus.com/inward/record.url?scp=85057772210&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2018.2884408

DO - 10.1109/ACCESS.2018.2884408

M3 - Journal article

SN - 2169-3536

VL - 6

SP - 77890

EP - 77910

JO - IEEE Access

JF - IEEE Access

M1 - 8554264

ER -