Lean and mean: network coding for commercial devices

Paramanathan, Achuthan; Pedersen, Morten Videbæk; Lucani, Daniel E.; Fitzek, Frank H. P.; Katz, Marcos

doi:10.1109/mwc.2013.6664474

Cited by 46 publications

(34 citation statements)

References 9 publications

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“…Several previous works have investigated the performance of RNC on smartphones [13,[17][18][19][20]. Shin et al in [13] showed that parallel processing techniques such as multithreading and SIMD can achieve at least an order of magnitude throughput improvement compared to previous results as reported in [17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

“…Shin et al in [13] showed that parallel processing techniques such as multithreading and SIMD can achieve at least an order of magnitude throughput improvement compared to previous results as reported in [17][18][19][20]. SIMD instructions were used to speed-up a loop-based GF multiplication method in [3] whereas in [13] a table lookup method was used in combination with SIMD.…”

Section: Related Workmentioning

confidence: 99%

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Energy Efficient QoS-Aware Random Network Coding on Smartphones

Shin

Park

2017

Mobile Netw Appl

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Random network coding (RNC) technology is known to benefit various facets of information networks; however, there have been concerns for the computational complexity of RNC since its incipience. For instance, RNC's high complexity can be directly translated into high energy consumption and drain fast smartphone batteries, making it unsuitable for mobile environments. In this paper, we optimize the energy consumption of RNC implementations with a given QoS requirement, especially throughput, for smartphone environments. To this end, we propose a duty cycling approach minimizing the energy consumption of RNC with a given throughput constraint. By manipulating the processor clock frequency controlling mechanism (a.k.a. governor) in Android, our approach can indirectly regulate the processor clock frequency and enhance energy efficiency. Real experiments on Android systems with smartphone application processors such as Samsung's Exynos 5410, show that our method can reduce the energy consumption of RNC by up to 67% compared to a RNC implementation relying on ondemand governor for frequency control. Finally, we argue that our method can be applied to a wide range of applications by implementing it with a fast Fourier transform algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy Efficient QoS-Aware Random Network Coding on Smartphones

Shin

Park

2017

Mobile Netw Appl

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“…This is only possible if the Gaussian elimination is O(g 2 ). A study in [23] for RLNC speeds in commercial devices indicated that this is effectively the case. The reason is that the g 2 scaling factor in the scaling law of the Gaussian elimination is much higher than the g 3 scaling for g < 512.…”

Section: Decodingmentioning

confidence: 99%

“…The reason is that the g 2 scaling factor in the scaling law of the Gaussian elimination is much higher than the g 3 scaling for g < 512. Particularly, this factor relates to the number of field elements in a packet size as mentioned in [23].…”

Section: Decodingmentioning

confidence: 99%

On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi

et al. 2016

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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.

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“…This family of smartphones has also been used in other energy-related studies [19]. We assume that a virtual machine of the same configuration is running on the device.…”

Section: Experimental Environment and Proceduresmentioning

confidence: 99%

CrowdAdaptor: A Crowd Sourcing Approach toward Adaptive Energy-Efficient Configurations of Virtual Machines Hosting Mobile Applications

Kan

Chan

Tse

2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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Abstract-Applications written by end-user programmers are hardly energy-optimized by these programmers. The end users of such applications thus suffer significant energy issues. In this paper, we propose CrowdAdaptor, a novel approach toward locating energy-efficient configurations to execute the applications hosted in virtual machines on handheld devices. CrowdAdaptor innovatively makes use of the development artifacts (test cases) and the very large installation base of the same application to distribute the test executions and performance data collection of the whole test suites against many different virtual machine configurations among these installation bases. It synthesizes these data, continuously discovers better energy-efficient configurations, and makes them available to all the installations of the same applications. We report a multi-subject case study on the ability of the framework to discover energy-efficient configurations in three power models. The results show that CrowdAdaptor can achieve up to 50% of energy savings based on a conservative linear power model.

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Lean and mean: network coding for commercial devices

Cited by 46 publications

References 9 publications

Energy Efficient QoS-Aware Random Network Coding on Smartphones

Energy Efficient QoS-Aware Random Network Coding on Smartphones

On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi

CrowdAdaptor: A Crowd Sourcing Approach toward Adaptive Energy-Efficient Configurations of Virtual Machines Hosting Mobile Applications

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