COMPAÑEROS DE TRABAJO

In this chapter, I presented an early concept towards enabling energy-aware adaptation for video -GreenDASH. I discussed how GreenDASH extends the current MPEG-DASH standard to include functionality of energy-awareness, using metrics and methodologies previously introduced in this thesis.GreenDASHis backwards- compatible with the existing MPEG-DASH standard, and may be implemented by re-using existing. The work presented here is preliminary, and there is a large scope for future work.

The work presented here on GreenDASH, and the state of the art of energy-aware video adaptation in general, is preliminary. A considerable amount of work, in terms of software development, testing and end-to-end deployment, as well as further research and experiments will need to be done to bring this to reality. For instance, future work could extend this work presented here and investigate how values for the energy- and quality-aware attributes introduced in this Chapter can be accurately and effectively generated (e.g. through widespread benchmarking efforts or through statistical analysis of video files). While the work presented in this Chapter was performed on desktop hardware, such adaptation could also be beneficial towards extending battery life on mobile devices.

Future work could also include the development and evaluation of GreenDash- complaint, or energy-aware video players. On the network end, future work could also examine what the effect of using newer transport layer protocols such as HTTP QUIC and TCP-Hollywood[95], on energy usage at the network.

8

CHAPTER

EIGHT

CONCLUSION

The work presented in this thesis is within the larger field of Green ICT. Green ICT encompasses a wide spectrum of efforts and focus areas, which considers the environmental impact of various aspects of the ICT life-cycle. This includes the manufacture, design and usage of computing systems and sub-systems (e.g. servers, networks, software, hardware etc), as well as other pertinent issues. In Chapter 2, I discussed some of the extensive work that has been done on investigating the energy usage and environmental impact of these various ICT systems and sub-systems. The bulk of the traffic on the Internet today is video. This means it is arguably the Internet’s most popular use case, and at least a very popular use-case of ICT systems in general. As the concern of the energy usage of these systems and sub-systems grows, it follows that the energy usage of Internet video should also be investigated. However, as I highlight in Chapter 3, very little work has specifically investigated the energy usage of Internet video on a global scale. Most of the work that has looked at energy usage of video has done so in the context of mobile devices, where energy supply is constrained by a battery.

The thesis presents some of the earliest work done towards enabling energy- awareness for Internet video. Through this thesis, I have shown that it is feasible for energy-aware users to make small individual savings in energy usage which can combine significantly when considered on a global scale, but without a significant impact on their perceived Quality of Experience (QoE). Towards achieving this conclusion, this thesis formulated and answered the following research questions: • Q1: How the energy consumption (and resultant carbon footprint) of global

Internet video usage be measured and quantified.

154 CHAPTER 8. CONCLUSION

• Q2: How the users of Internet video can be enabled towards making conscious decisions to improve their energy awareness.

• Q3: How application-layer or software techniques can be used to enable this energy awareness for Internet video, with a minimal impact on the perceived Quality of Experience (QoE).

8.1

Measuring the energy usage of Internet Video

Towards addressing Q1, I presented the first empirical investigation into the energy usage and objective quality of seven popular video codecs (FLV, H.264, H.265 (HEVC), MPEG4-II, Microsoft MPEG4-II (MSMPEG4), VP8 and VP9) on an experimental test-bed in Chapter 4. I used a simple, measurement-based methodology to show significant differences in energy usage between these codecs. For instance, I observed a difference of 300% in energy usage between decoding with the FLV codec and H.265 codec, at a large picture size (1080p). For encoding, the differences were even more significant. I observed a difference of a factor of 10 between the FLV and H.265 codecs.

Based on these empirical measurements, I made extrapolations to a global scale to show the significant energy savings possible by accumulating small individual client- side modifications. Using the number of hours of video streamed from YouTube annually in 2014 (72 billion hours), I made Fermi estimates which considered the accumulation of small, individual energy savings at global scale. Savings of just 1

J/sv(Joule per second of video playback) for all the hours of video streamed from Youtube annually, would be enough to power over 18,000 homes in the UK for a year. This would be equivalent to∼£10.8M (∼US$17.8M) per year in terms of financial cost, and 12.2 million KgCO2in terms of carbon emissions annually. Interestingly, I

observed much higher differences in the experiments. For instance, the difference between using 480p instead of 1080p with the H.265 codec was around 40J/svon the experimental testbed. This was just an approximation exercise to show the potential for significant savings when considered at global scale. Real savings would depend on many factors, such as the choices users make and the specific software, hardware and video sources.

Based on the empirical work in this chapter, I also presented new metrics for energy- aware video playback. The key metric I introduced is the Energy-Video Quality Index (EnVI). This novel metric is essentially a weighted mean which can combine

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energy usage of a video stream with any number of objective or subjective video quality metrics such as SSIM, PSNR or MOS.

The results from this chapter motivated the work done in the rest of the thesis. For instance, the results from this single test-bed environment highlighted the need for a system benchmarking tool capable of working across various combinations of video configurations such as device types and capabilities, codec, picture size, video player etc. This chapter also highlighted how suitably informed users could be empowered to make choices which could provide economic and environmental benefits after their regular usage of Internet video.

I presentedvEQ-benchmark, an open-source tool for benchmarking energy-usage, resource usage and objective quality of diverse Internet video in Chapter 5. There are a number of similar benchmarking tools in existence, such as Microsoft Windows Assessment Console, FutureMark and Phoronix Test Suite, which consider the energy usage of video streams in one way or another. However, they are quite limited in the scope and functionality. Specifically, they only consider the energy usage of the playback of a single hard-coded video stream, mostly on battery powered devices like phones or laptops. Internet video today is very diverse, and will be presented to users in a wide array of formats, resolutions, picture sizes, codecs and devices.vEQ-benchmarkwas designed to be flexible and modular, and takes this video diversity into consideration. I presented a high-level description of the tool’s design and architecture showcasing this modular design.

I also highlighted the capabilities ofvEQ-benchmarkthrough an experiment-based evaluation on a range of desktop computing hardware. I demonstrated the tool’s capabilities of benchmarking both local video files (stored on disk), and online video streams (Youtube streams) as an example. However, the tool can be used for any online video content, through its web browser interface.

vEQ-benchmarkgenerates EnVI metric scores using these video workloads. This is useful for observing the energy characteristics of the videos themselves, as well as for benchmarking the energy and performance of a range of desktop playback devices. The version presented in this thesis was tested on Ubuntu Linux, Mac OSX and Microsoft Windows environments. Future work could involve development for mobile platforms such as Android and iOS. As data is collected,vEQ-benchmark

trains three statistical linear models of energy usage for benchmarked systems from the collected data. The appropriate model to use will depends on nature of the data collected.

156 CHAPTER 8. CONCLUSION