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Yamila Comejo
I pulled this chapter together from dozens of sources that were at times somewhat contradictory. Facts on the ground change over time and depend who is telling the story and what audience they're addressing. I tried to create as coherent a narrative as I could. If there are any errors I'd be more than happy to fix them. Keep in mind this article is not a technical deep dive. It's a big picture type article. For example, I don't mention the word microservice even once :-)
Given our discussion in the What is Cloud Computing? chapter, you might expect Netflix to serve video using AWS. Press play in a Netflix application and video stored in S3 would be streamed from S3, over the internet, directly to your device.
Another relevant factoid is Netflix is subscription based. Members pay Netflix monthly and can cancel at any time. When you press play to chill on Netflix, it had better work. Unhappy members unsubscribe.
The client is the user interface on any device used to browse and play Netflix videos. It could be an app on your iPhone, a website on your desktop computer, or even an app on your Smart TV. Netflix controls each and every client for each and every device.
Everything that happens before you hit play happens in the backend, which runs in AWS. That includes things like preparing all new incoming video and handling requests from all apps, websites, TVs, and other devices.
In 2007 Netflix introduced their streaming video-on-demand service that allowed subscribers to stream television series and films via the Netflix website on personal computers, or the Netflix software on a variety of supported platforms, including smartphones and tablets, digital media players, video game consoles, and smart TVs.
Netflix succeeded. Netflix certainly executed well, but they were late to the game, and that helped them. By 2007 the internet was fast enough and cheap enough to support streaming video services. That was never the case before. The addition of fast, low-cost mobile bandwidth and the introduction of powerful mobile devices like smart phones and tablets, has made it easier and cheaper for anyone to stream video at any time from anywhere. Timing is everything.
Building out a datacenter is a lot of work. Ordering equipment takes a long time. Installing and getting all the equipment working takes a long time. And as soon they got everything working they would run out of capacity, and the whole process had to start over again.
The long lead times for equipment forced Netflix to adopt what is known as a vertical scaling strategy. Netflix made big programs that ran on big computers. This approach is called building a monolith. One program did everything.
What Netflix was good at was delivering video to their members. Netflix would rather concentrate on getting better at delivering video rather than getting better at building datacenters. Building datacenters was not a competitive advantage for Netflix, delivering video is.
It took more than eight years for Netflix to complete the process of moving from their own datacenters to AWS. During that period Netflix grew its number of streaming customers eightfold. Netflix now runs on several hundred thousand EC2 instances.
The advantage of having three regions is that any one region can fail, and the other regions will step in handle all the members in the failed region. When a region fails, Netflix calls this evacuating a region.
The header image is meant to intrigue you, to draw you into selecting a video. The idea is the more compelling the header image, the more likely you are to watch a video. And the more videos you watch, the less likely you are to unsubscribe from Netflix.
The first thing Netflix does is spend a lot of time validating the video. It looks for digital artifacts, color changes, or missing frames that may have been caused by previous transcoding attempts or data transmission problems.
A pipeline is simply a series of steps data is put through to make it ready for use, much like an assembly line in a factory. More than 70 different pieces of software have a hand in creating every video.
The idea behind a CDN is simple: put video as close as possible to users by spreading computers throughout the world. When a user wants to watch a video, find the nearest computer with the video on it and stream to the device from there.
In 2007, when Netflix debuted its new streaming service, it had 36 million members in 50 countries, watching more than a billion hours of video each month, streaming multiple terabits of content per second.
At the same time, Netflix was also devoting a lot of effort into all the AWS services we talked about earlier. Netflix calls the services in AWS its control plane. Control plane is a telecommunications term identifying the part of the system that controls everything else. In your body, your brain is the control plane; it controls everything else.
In 2011, Netflix realized at its scale it needed a dedicated CDN solution to maximize network efficiency. Video distribution is a core competency for Netflix and could be a huge competitive advantage.
The number of OCAs on a site depends on how reliable Netflix wants the site to be, the amount of Netflix traffic (bandwidth) that is delivered from that site, and the percentage of traffic a site allows to be streamed.
Within a location, a popular video like House of Cards is copied to many different OCAs. The more popular a video, the more servers it will be copied to. Why? If there was only one copy of a very popular video, streaming the video to members would overwhelm the server. As they say, many hands make light work.
Right now, up to 100% of Netflix content is being served from within ISP networks. This reduces costs by relieving internet congestion for ISPs. At the same time, Netflix members experience a high-quality viewing experience. And network performance improves for everyone.
What may not be immediately obvious is that the OCAs are independent of each other. OCAs act as self-sufficient video-serving archipelagos. Members streaming from one OCA are not affected when other OCAs fail.
Even before millions were confined to their homes by a global pandemic, improvements in internet connections and service offerings had led to an exponential increase in the use of streaming video around the world. With few options left for entertainment, streaming services are taking off. In this commentary, we examine the carbon footprint of these services.
Streaming services are associated with energy use and carbon emissions from devices, network infrastructure and data centres. Yet, contrary to a slew of recent misleading media coverage, the climate impacts of streaming video remain relatively modest, particularly compared to other activities and sectors.
Drawing on our analysis and other credible sources, we expose the flawed assumptions in one widely reported estimate of the emissions from watching 30 minutes of Netflix. These exaggerate the actual climate impact by up 90 times.
The relatively low climate impact of streaming video today is thanks to rapid improvements in the energy efficiency of data centres, networks and devices. But slowing efficiency gains, rebound effects and new demands from emerging technologies, including artificial intelligence (AI) and blockchain, raise increasing concerns about the overall environmental impacts of the sector over the coming decades.
Update 11/12/2020: The energy intensity figures for data centres and data transmission networks were updated to reflect more recent data and research. As a result, the central IEA estimate for one hour of streaming video in 2019 is now 36gCO2, down from 82gCO2 in the original analysis published in February 2020. The updated charts and comparisons also include the corrected values published by The Shift Project in June 2020, as well as other recent estimates quoted by the media.
Looking at electricity consumption alone, the original Shift Project figures imply that one hour of Netflix consumes 6.1 kilowatt hours (kWh) of electricity. This is enough to drive a Tesla Model S more than 30km, power an LED lightbulb constantly for a month, or boil a kettle once a day for nearly three months. The corrected figures imply that one hour of Netflix consumes 0.8 kWh.
With 167 million Netflix subscribers watching an average of two hours per day, the corrected Shift Project figures imply that Netflix streaming consumes around 94 terawatt hours (TWh) per year, which is 200 times larger than figures reported by Netflix (0.45TWh in 2019).
The assumptions behind the Shift Project analysis (largely based on a 2015 paper, whose assumptions have been significantly revised in 2019 and 2020) contain a series of flaws, which, taken together, seriously exaggerate the electricity consumed by streaming video.
This difference stemmed from a stated assumption of 3Mbps apparently being converted in error to 3 megabytes per second, MBps, with each byte equivalent to eight bits. The Shift Project corrected this error in their June 2020 update, but did not revise any of their other assumptions, discussed below.
The Shift Project analysis overestimates the energy intensity of data centres and content delivery networks (CDNs) that serve streaming video to consumers by around 35-fold, relative to figures derived from 2019 Netflix electricity consumption data and subscriber usage data.
My original February 2020 analysis showed that the Shift Project assumptions for data transmission energy intensity (0.15-0.88 kWh/GB) were much higher than more recent estimates (0.025-0.23kWh/GB). However, the latest research shows that these data-based intensity values (kWh/GB) are not appropriate for estimating the network energy use of high bitrate applications such as streaming video. Instead, experts advise using time-based energy intensity values (kWh per viewing hour). Therefore, my assumptions for data transmission energy use have been updated with time-based energy intensity values.
Taken together, my updated analysis suggests that streaming a Netflix video in 2019 typically consumed around 0.077 kWh of electricity per hour, some 80-times less than the original estimate by the Shift Project (6.1 kWh) and 10-times less than the corrected estimated (0.78 kWh), as shown in the chart, below left. The results are highly sensitive to the choice of viewing device, type of network connection and resolution, as shown in the chart, below right.
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