Adaptation of video encoding to address dynamic thermal management effects

Mirtar, Ali; Dey, Sujit; Raghunathan, Anand

doi:10.1109/igcc.2012.6322294

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…The energy consumption of a computing device is not only determined by the efficiency of its physical devices, but it is also dependent on resource management and on applications usage patterns [37], [34], [42], [5], [43], [44], [45], [46].…”

Section: Power and Energy In Computing Systemsmentioning

confidence: 99%

Towards Greener Computing Systems For Video Compression

Fonseca

Queiroz

2015

Journal of Communication and Information Systems

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Over the past years, multimedia communication technologies have demanded higher computing power availability and, therefore, higher energy consumption. In order to meet the challenge to provide software-based video encoding solutions with reduced consumption, we adopted a software implementation of a state-of-the-art video encoding standard and optimized its implementation in the energy (E) sense. Thus, besides looking for the coding options which lead to the best fidelity in a rate-distortion (RD) sense, we constrain the video encoding process to fit within a certain energy budget i.e., an RDE optimization. We considered energy by integrating power measurements from the system power supply unit. We present an RDE-optimized framework which allows for software-based real-time video compression, meeting the desired targets of electrical consumption, hence, controlling carbon emissions. The system can be made adaptive, dynamically tracking changes in image contents and in energy demands. We show results of energy-constrained compression wherein one can save as much as 31% of the power consumption with small impact on RD performance.

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Section: Power and Energy In Computing Systemsmentioning

confidence: 99%

Towards Greener Computing Systems For Video Compression

Fonseca

Queiroz

2015

Journal of Communication and Information Systems

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“…3) Workload management or task scheduling based techniques [56][57][58][59][60][61][62][63]. 4) Thermal-aware or thermal-management techniques which take into account the thermal properties [56,59,61,[64][65][66][67][68][69][70]. Further, some techniques address the issues related to cooling in data centers [21,59,71].…”

Section: Overview Of Power Management Techniquesmentioning

confidence: 99%

Power Management Techniques for Data Centers: A Survey

Mittal¹

2014

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With growing use of internet and exponential growth in amount of data to be stored and processed (known as "big data"), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.

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“…Although this technique can reduce the overall temperature for the encoder, it results in more data to be transmitted between the source (encoder) and receiver (decoder) that leads to increased communication power/energy. The work in [17] selects a video quality degradation mode to compensate for the quality degradation effects of different DTM policies. However, it neither addresses temperature management nor explores the impact of different video properties on the generated temperature.…”

Section: Introductionmentioning

confidence: 99%

Tone

Palomino

Shafique

Susin

et al. 2014

Proceedings of the 2014 International Symposium on Low Power Electronics and Design

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This paper presents an adaptive temperature optimization technique for the next generation video encoders. It exploits both applicationspecific knowledge (i.e. video encoding configurations) and video content properties in order to efficiently manage the temperature of advanced video coding systems at the software layer. For designing an efficient technique, we perform an extensive offline analysis to understand the impact of different video properties and configurations on the CPU thermal profiles when processing the next generation video encoder. Our temperature optimization technique performs an application-level prediction of the temperature trend followed by an application-level thermal management policy. The policy dynamically manages the temperature by performing an adaptive encoder configuration selection while providing minimum penalties in terms of bit rate and video quality. The experimental results show that our policy meets temperature constraints with negligible encoding performance loss. Moreover, when compared to state-of-the-art techniques, our policy provides a relatively reduced video quality loss while still meeting the temperature constraints.

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Adaptation of video encoding to address dynamic thermal management effects

Cited by 4 publications

References 19 publications

Towards Greener Computing Systems For Video Compression

Towards Greener Computing Systems For Video Compression

Power Management Techniques for Data Centers: A Survey

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