Abstract. This paper introduces the Kodo network coding library. Kodo is an open source C++ library intended to be used in practical studies of network coding algorithms. The target users for the library are researchers working with or interested in network coding. To provide a research friendly library Kodo provides a number of algorithms and building blocks, with which new and experimental algorithms can be implemented and tested. In this paper we introduce potential users to the goals, the structure, and the use of the library. To demonstrate the use of the library we provide a number of simple programming examples. It is our hope that network coding practitioners will use Kodo as a starting point, and in time contribute by improving and extending the functionality of Kodo.

Energy consumption has been mostly neglected in network coding (NC) research so far. This work investigates several different properties of NC that influence the energy consumption and thus are important when designing NC systems for batterydriven devices. Different approaches to the necessary implementation of coding operations and Galois fields arithmetic are considered and complexity expressions for coding operations are provided. We also benchmark our own mobile phone implementation on a Nokia N95 under different settings. Several NC strategies are described and compared, furthermore expressions for transmission times are developed. It is also shown that the use of NC introduces a trade off between reduction in transmission time and increase in energy consumption.

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We consider three types of application layer coding for streaming over lossy links: random linear coding, systematic random linear coding, and structured coding. The file being streamed is divided into sub-blocks (generations). Code symbols are formed by combining data belonging to the same generation, and transmitted in a round-robin fashion. We compare the schemes based on delivery packet count, net throughput, and energy consumption for a range of generation sizes. We determine these performance measures both analytically and in an experimental configuration. We find our analytical predictions to match the experimental results. We show that coding at the application layer brings about a significant increase in net data throughput, and thereby reduction in energy consumption due to reduced communication time. On the other hand, on devices with constrained computing resources, heavy coding operations cause packet drops in higher layers and negatively affect the net throughput. We find from our experimental results that lowrate MDS codes are best for small generation sizes, whereas systematic random linear coding has the best net throughput and lowest energy consumption for larger generation sizes due to its low decoding complexity.

Abstract-Distributed storage is usually considered within a cloud provider to ensure availability and reliability of the data. However, the user is still directly dependent on the quality of a single system. It is also entrusting the service provider with large amounts of private data, which may be accessed by a successful attack to that cloud system or even be inspected by government agencies in some countries. This paper advocates a general framework for network coding enabled distributed storage over multiple commercial cloud solutions, such as, Dropbox, Box, Skydrive, and Google Drive, as a way to address these reliability and privacy issues. By means of theoretical analysis and reallife implementations, we show not only that our framework constitutes a viable solution to increase the reliability of stored data and to ensure data privacy, but it also provides a way to reduce the storage costs and to increase the download speed significantly. Our measurements show that the download time could be reduced up to six fold in some scenarios exploiting four commercial cloud solutions.