The fundamental insight of use of coding in computer networks is that information to be transmitted from the source in a session can be inferred, or decoded, by the intended receivers, and does not have to be transmitted verbatim. Several coding techniques have gained popularity over the recent years. Among them is random network coding with random linear codes, in which a node in a network topology transmits a linear combination of incoming, or source, packets to its outgoing links. Theoretically, the high computational complexity of random linear codes (RLC) is well known, and is used to motivate the application of more efficient codes, such as the traditional Reed-Solomon (RS) codes and, more recently, fountain codes (LT codes). Factors like computational complexity, network overhead, and deployment flexibility can make one coding schemes more attractive for one application than the others. While there is no one-fit-all coding solution, random linear coding is very flexible, well known to be able to achieve optimal flow rates in multicast sessions, and universally adopted in all proposed protocols using network coding. However, its practicality has been questioned, due to its high computational complexity. Unfortunately, to date, there has been no commercial real-world system reported in the literature that take advantage of the power of network coding.
This research represents the first attempt towards a high-performance design and implementation of network coding. The objective of this work is to explore the computational limits of network coding in off-the-shelf modern processors, and to provide a solid reference implementation to facilitate commercial deployment of network coding. We promote the development of new coding-based systems and protocols through a comprehensive toolkit with coding implementations that are not just reference implementations. Instead, they have attained high-performance and flexibility to find widespread adoption.
The final work, packaged as a toolkit code-named Tenor, includes high-performance implementations of a number of coding techniques: random linear network coding (RLC), fountain codes (LT codes), and Reed-Solomon (RS) codes in CPUs (single and multi core(s) for both Intel x86 and IBM POWER families), GPUs (single and multiple), and mobile/embedded devices based on ARMv6 and ARMv7 cores. Tenor is cross-platform with support on Linux, Windows, Mac OS X, and iPhone OS, and supports both 32-bit and 64-bit platforms. The toolkit includes some 23K lines of C++ code.
In order to validate the effectiveness of the Tenor toolkit, we build coding-based on-demand media streaming systems with GPU-based servers, thousands of clients emulated on a cluster of computers, and a small number of actual iPhone devices. To facilitate deployment of such large experiments, we develop Blizzard, a high-performance framework, with the main goals of: 1) emulating hundreds of client/peer applications on each physical node; 2) facilitating scalable servers that can efficiently communicate with thousands of clients. Our experiences offer an illustration of Tenor components in action, and their benefits in rapid system development. With Tenor, it is trivial to switch from one coding technique to another, scale up to thousands of clients, and deliver actual video to be played back even on mobile devices.
Online copy of PhD thesis