Efficient Parallelized Network Coding for P2P File Sharing Applications

Abstract

Abstract.In this paper, we investigate parallel implementation techniques for network coding to enhance the performance of Peer-to-Peer (P2P) file sharing applications. It is known that network coding mitigates peer/piece selection problems in P2P file sharing systems; however, due to the decoding complexity of network coding, there have been concerns about adoption of network coding in P2P file sharing systems and to improve the decoding speed the exploitation of parallelism has been proposed previously. In this paper, we argue that naive parallelization strategies of network coding may result in unbalanced workload distribution and thus limiting performance improvements. We further argue that higher performance enhancement can be achieved through load balancing in parallelized network coding and propose new parallelization techniques for network coding. Our experiments show that, on a quad-core processor system, proposed algorithms exhibit up to 30% of speed-up compared to an existing approach using 1 Mbytes data with 2048×2048 coefficient matrix size.

Reconfigurable and Parallelized Network Coding Decoder for VANETs

“…The decoding performance has been improved with progressive decoding as the decoding time has been overlapped with the time required for data transmission. Park et al have demonstrated that the strategy of parallelization in network coding gives significant impact to the decoding performance [28]. They have emphasized balanced parallelization for both the decoding performance and the scalability of the number of processors.…”

“…Therefore, considering the vehicular environment, conventional processors are not recommended for practical use. In fact, several recent researches have shown the feasible software implementations with the environment of conventional processors [7,28,34].…”

Network Coding on Heterogeneous Multi-Core Processors for Wireless Sensor Networks

“…Park et al . suggested enhanced forms of parallelization network coding algorithms with reduced computational complexity [ 31 , 51 ]. Whereas, our work is focused on improving decoding performance via the adoption of algorithms for use in a heterogeneous processor, referred to as the Cell BE.…”