Leaner and meaner: Network coding in SIMD enabled commercial devices

Although random linear network coding (RLNC) constitutes a highly efficient and distributed approach to enhance communication networks and distributed storage, it requires additional processing to be carried out in the network and in end devices. For mobile devices, this processing translates into energy use that may reduce the battery life of a device. This paper focuses not only on providing a comprehensive measurement study of the energy cost of RLNC in eight different computing platforms, but also explores novel approaches (e.g., tunable sparse network coding) and hardware optimizations for Single Instruction Multiple Data (SIMD) available in the latest generations of Intel and Advanced RISC Machines (ARM) processors. Our measurement results show that the former provides gains of two-to six-fold from the underlying algorithms over RLNC, while the latter provides gains for all schemes from 2× to as high as 20×. Finally, our results show that the latest generation of mobile processors reduce dramatically the energy per bit consumed for carrying out network coding operations compared to previous generations, thus making network coding a viable technology for the upcoming 5G communication systems, even without dedicated hardware.

[1]  Morten Videbæk Pedersen,et al.  Kodo: An Open and Research Oriented Network Coding Library , 2011, Networking Workshops.

[2]  Frank H. P. Fitzek,et al.  Implementation of Random Linear Network Coding Using NVIDIA's CUDA Toolkit , 2009, GridNets.

[3]  Michael Luby,et al.  LT codes , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[4]  Muriel Médard,et al.  XORs in the Air: Practical Wireless Network Coding , 2006, IEEE/ACM Transactions on Networking.

[5]  Frank H. P. Fitzek,et al.  Energy consumption model and measurement results for network coding-enabled IEEE 802.11 meshed wireless networks , 2012, 2012 IEEE 17th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[6]  Morten Videbæk Pedersen,et al.  Decoding Algorithms for Random Linear Network Codes , 2011, Networking Workshops.

[7]  H. Charaf,et al.  Implementation of random linear network coding on OpenGL-enabled graphics cards , 2009, 2009 European Wireless Conference.

[8]  Rudolf Ahlswede,et al.  Network information flow , 2000, IEEE Trans. Inf. Theory.

[9]  Milica Stojanovic,et al.  Random Linear Network Coding for Time Division Duplexing: Energy Analysis , 2009, 2009 IEEE International Conference on Communications.

[10]  Daniel Enrique Lucani,et al.  Lean and mean: network coding for commercial devices , 2013, IEEE Wireless Communications.

[11]  Muriel Médard,et al.  An algebraic approach to network coding , 2003, TNET.

[12]  Ethan L. Miller,et al.  Screaming fast Galois field arithmetic using intel SIMD instructions , 2013, FAST.

[13]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[14]  Daniel E. Lucani,et al.  A Practical View on Tunable Sparse Network Coding , 2015 .

[15]  Tracey Ho,et al.  A Random Linear Network Coding Approach to Multicast , 2006, IEEE Transactions on Information Theory.

[16]  Muriel Medard,et al.  Tunable sparse network coding , 2012 .

[17]  David M Levinson,et al.  Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering , 2009, Complex.

[18]  Muriel Médard,et al.  Tunable sparse network coding for multicast networks , 2014, 2014 International Symposium on Network Coding (NetCod).