Abstract
Given that next generation networks are expected to be populated by a large number of devices, there is a need for quick deployment and evaluation of alternative mechanisms to cope with the possible generated traffic in large-scale distributed data networks. In this sense, the Raspberry Pi has been a popular network node choice due to its reduced size, processing capabilities, low cost and its support by widely-used operating systems. For information transport, network coding is a new paradigm for fast and reliable data processing in networking and storage systems, which overcomes various limitations of state-of-the-art routing techniques. Therefore, in this work, we provide an in-depth performance evaluation of Random Linear Network Coding (RLNC)-based schemes for the Raspberry Pi Models 1 and 2, by showing the processing speed of the encoding and decoding operations and the corresponding energy consumption. Our results show that, in several scenarios, processing speeds of more than 80 Mbps in the Raspberry Pi Model 1 and 800 Mbps in the Raspberry Pi Model 2 are attainable. Moreover, we show that the processing energy per bit for network coding is below 1 nJ or even an order of magnitude less in these scenarios.
Highlights
Due to the advent of the Internet of Things (IoT), approximately 50 billion devices ranging from sensors to phones are expected to be connected through data networks in a relatively short period of time [1]
In this work, we provide detailed measurements of the goodput and energy consumption of Raspberry Pi (Raspi) Models 1 and 2, when performing network coding operations with different codecs based on Random Linear Network Coding (RLNC) such as: full dense RLNC, multi-core enabled RLNC, sparse RLNC
We review the energy consumption of the Raspi, since this platform is deployed at a large scale in scenarios where (i) energy is constrained to the lifetime of the device battery and (ii) the devices could be established in locations that are unavailable for regular maintenance
Summary
Due to the advent of the Internet of Things (IoT), approximately 50 billion devices ranging from sensors to phones are expected to be connected through data networks in a relatively short period of time [1]. In this work, we provide detailed measurements of the goodput (processing speed) and energy consumption of Raspi Models 1 and 2, when performing network coding operations with different codecs based on RLNC such as: full dense RLNC, multi-core enabled RLNC, sparse RLNC and tunable sparse RLNC. For these coding schemes, the encoder and decoder implementations from Kodo are able to detect and make use of the SIMD through the NEON instruction set of the Raspberry Pi by recognizing the ARM architecture with its multicore capabilities.
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