Encoding at Scale for Live Video Streaming

Transcription

1 WHITE PAPER Encoding at Scale for Live Video Streaming In this whitepaper, discover how to deliver an economical, high quality, and scalable cloud encoding architecture for live video streaming.

2 Introduction 2 The Need for Video Encoding 3 Video Streaming Delivery Architecture 4 Video Source 4 Display Devices 5 Video Encoding and Transcoding 6 Video Delivery and Content Distribution Network (CDN) 7 Alternatives for Encoding Processing 8 Introducing the Codensity T400 Video Transcoding Solution 9 Flexibility and Scalability through U.2 Packaging and NVMe Infrastructure 9 Density from SoC-based Encoding and Transcoding 10 Real-time Encoding Quality 12 Integration through FFmpeg Interface 13 Benefits 14 Conclusions and Summary 15 References and Notes 16 1

3 Introduction The generation, storage, and consumption of streaming video is booming across the Internet. According to Domo s Data Never Sleeps report, 1 Netflix streams 97,222 hours of content every minute, while 400 hours of new fresh video content is uploaded to YouTube every minute. According to the Cisco 2018 VNI report, 2 IP video traffic will be 82 percent of all consumer Internet traffic by 2021, up 73 percent from While VoD growth has been strong, the next frontier of growth for over-the-top (OTT) and direct-to-customer video streaming will be delivering live video content. The Cisco 2018 VNI reports that live video will grow 15-fold from 2016 to With significant growth forecasted for live video streaming content, the industry needs economical and scalable video encoding solutions for live video streaming. This paper will outline an innovative video encoding solution, which combines the performance and densities of System-on-Chip (SoC) encoding with NVMe-based cloud infrastructure, to provide an economical, high quality, and scalable solution to deliver encoding at scale for live video streaming. With significant growth forecasted for live video streaming content, the industry needs economical and scalable video encoding solutions for live video streaming. 2

4 The Need for Video Encoding Raw video files are very large making the transmission and storage of raw video files prohibitively expensive. To help make video content economical to transmit and store, video media is compressed using a video codec (short for coder/decoder) to encode the video data into a much smaller format for more economical transmission to destination or into a much smaller file size for storage. The corresponding decoder at the receiving device or browser-based video player reverses the encoding for playback. As seen below in Figure 1, sourced from Bitmovin s Video Developer Report 2018, 3 the H.264 Advanced Video Coding (AVC) is the most common video codec in use today, achieving ~50x compression ratio compared to raw video file size. Support of H.264 is almost ubiquitous across the full spectrum of video devices, from HDTVs to legacy 3G mobile phones. The second most common codec is the newer H.265 High Efficiency Video Coding (HEVC), which can deliver comparable quality to H.264 using ~50% less bandwidth and thereby reduces video delivery costs by ~50%. HEVC usage is expected to grow in the near term due to Apple s 2017 announcement that H.265 HEVC will be natively supported in ios, tvos, and OSX. VP9 is another current generation video codec supported in Chrome browsers, with similar compression benefits to H.265, however usage of VP9 is lower than H % 95% 92% 80% 60% 40% 28% 42% 20% 0% 10% 11% 6% 6% H.264 / AVC H.265 / HEVC VP9 AV1 Figure 1. Video Codec Usage 3 H.265 encoding requires approximately 10x the processing resources of H.264 encoding. The tradeoff for improving codec compression efficiencies is more processing resources are required. H.265 encoding requires approximately 10x the processing resources of H.264 encoding. Encoding complexity for the newest AV1 video codec is even worse. Interest in AV1 is growing, 3

5 with promises of an additional ~30% compression and bandwidth savings compared to H.265 and VP9, however today s early AV1 encoder implementations deliver glacially slow encoding, 4 making AV1 economically prohibitive for most live content publishers in the short term. So in summary, live streaming content and service providers are advised to focus on solutions that can economically encode and scale H.264 AVC and H.265 HEVC video, which is the majority of the use cases today. Video Streaming Delivery Architecture The objective of a video streaming delivery architecture (Figure 2) is to distribute video content from a source capture device (left side of diagram), to as many subscribers as possible using a variety of playback devices (right side of diagram). In this section, we will present a high-level overview of the key components and functions in a video streaming delivery architecture to help highlight the key requirements for scalable video encoding. Video Source H.264/H.265 H.264/H.265 Encoding Ladder ABR Streaming Display Devices 4K/UHD or 1080p Encoding & Transcoding 4K/UHD 1080p 720p 480p 360p Content Distribution Network Figure 2. Video Streaming Delivery Architecture Video Source Advances in video cameras, video production tools, and live video streaming technologies are fueling an explosion in the variety and quantity of live video content producers on the Internet today. Example use cases include: Advances in video cameras, video production tools, and live video streaming technologies are fueling an explosion in the variety and quantity of live video content producers on the Internet today. Live Sporting Events: Sports fans today pay premium subscription fees to watch their favorite sports teams, FIFA soccer, or Olympic events. Live sporting events are often live streamed to tens of thousands of viewers, hence compressing video streams through efficient encoding can generate significant savings in bandwidth costs. 4

6 Conferences and Breaking News: While sports events might result in hundreds of streams, our conference and news category might have thousands of unique producers. As live streaming content becomes more specialized with fewer viewers for each video source, encoding costs as a percentage of overall costs will grow, hence encoding scaling efficiencies become increasingly important. User-Generated Content, including Social Media and Gamers : With the capabilities of modern smartphones and affordable HD video capture devices, the number of individual video content producers is exploding. Facebook Live is a great example of individuals posting a huge variety of live streaming content to their followers. Another example would be skilled gamers who livestream themselves playing a video game to their followers, as seen on services such as Twitch. Regardless of the content type or business model, the video source is normally captured at the highest resolution possible typically 1080p or 4K UHD resolution and then encoded (usually using H.264) for economical distribution to subscribers or followers. For video content intended for post-production for VOD playback later, the video might be provided as an MP4 file. However, for live video streaming, the video media is typically provided as a continuous Real Time Messaging Protocol (RTMP) video stream. Display Devices At the right side of Figure 2 are the consumers of the video content potentially thousands of subscribers or followers watching each unique live video stream. Consumers want to enjoy their video content from wherever they are, using a variety of playback devices. Consumers want to enjoy their video content from wherever they are, using a variety of playback devices. Consumers able to watch the content from PCs or 4K UHD TV devices with broadband connections will be served a high-quality video stream, delivered at a high bitrate. However, other consumers might be using a Wi-Fi connected tablet watching a 720p video player inside a browser or downloadable application, or possibly even a 3G mobile phone with only 640x360 (360p) resolution. Sending a 4K UHD high resolution video stream to a 3G mobile phone is not only a waste of bandwidth and connectivity expense, but likely to cause buffering and clipping during video playback, resulting in a poor quality of experience (QoE) for the mobile subscriber. For these reasons and others, every source of original video content, whether intended for VOD playback later or a live streaming service, is usually encoded in different bitrates what is sometimes known as an encoding ladder. 5

7 Video Encoding and Transcoding The goal of the video transcoding function is to ingest the live video stream, and generate the encoding ladder for distribution through the Content Distribution Network (CDN) to the end devices. The top step of the encoding ladder is the best quality encoding of the original source material, delivered with the highest bitrate and framerate, intended for high resolution The goal of the video transcoding function is to ingest the live video stream, and generate the encoding ladder for distribution through the Content Distribution Network (CDN) to the end devices. video displays and devices. If the source video can be captured as 4K UHD resolution, then the top step for the ladder can also be a 4K UHD output stream. The bottom step of the encoding ladder would be the exact same original video content, but transrated down to lower bitrates, frame rates, and resolution for worst case scenario streaming conditions to the smallest smartphone. In between are the incremental steps of the encoding ladder, with increasing levels of bitrates and resolutions, based on the variety of target devices or target video players that consumers might use to watch the live streaming content. An H.264 source video file could (for example) also be transcoded to H.265 High Efficiency Video Coding (HEVC) encoding to deliver the same resolution and similar video quality, but with 50% less bandwidth. Resolution Codec FPS Bitrate Target Device 4K UHD (source) 3840 x 2160 H Mbps 4k UHD TV 4K UHD 3840 x 2160 H Mbps 4k UHD TV 1080p 1920 x 1080 H Mbps PC H Mbps Tablet, Smartphone 720p 1280 x 720 H Mbps PC H Mbps Tablet, Smartphone 480p 640 x 480 H Mbps Smartphone H Kbps Smartphone 320p 480 x 320 H Kbps Smartphone H Kbps Smartphone Figure 3. Example Video Encoding Ladder In Figure 3, we have shown a conceptual encoding ladder output from the encoding function. This example assumes that the live video was captured using a 4K UHD camera, and 6

8 encoded at source using H.264. In the followed transcoding process, the stream will be decoded first, then encoded to different resolutions and bitrates to match the different target display devices. The new streams are encoded in both H.264 and H.265 formats, to maximize compatibility with as many devices as possible. The topic of optimizing encoding ladders is complex and beyond the scope of this whitepaper. For example, Netflix uses sophisticated per-title encoding techniques for each movie or TV episode. Regardless of what your encoding experts decide to implement, your encoding function needs to process those encoding specifications at scale to handle the huge forecasted growth in live video streaming volumes. Video Delivery and Content Distribution Network (CDN) For a video streaming service, purchasing the bandwidth to deliver content through the distribution network is one of the top expense line items. Hence, one of the most important roles of video streaming service architects is to find the correct balance between delivering high quality video content, while minimizing connectivity and distribution costs. Live streaming increasingly uses adaptive bitrate streaming (ABR). For each unique video source, the encoding function delivers the encoding ladder of MP4 files (for VOD) or RTMP streams (for live video). ABR technologies manage the encoded ladder streams together as one title. A manifest file is created which lists the different bitrates for a playback device to choose from. ABR technology also segments each One of the most important roles of video streaming service architects is to find the correct balance between delivering high quality video content, while minimizing connectivity and distribution costs. encoded stream into a sequence of files, with each file being 2-6 seconds of video playback, and then sends the stream as a sequence of smaller files compatible with HTTP protocol. The end device would reference the manifest file to determine available encodings and bitrates, and then choose an appropriate encoding to start playing the live stream. During playback, if the end device detects a change in bandwidth quality or availability, the end device can then reference the manifest to step up or step down the encoding ladder to continue the same video stream, but at a higher or lower bitrate. Leading ABR standards include Apple s HTTP Live Streaming (HLS) and MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH). For VOD content, the CDN will also replicate and distribute the video file into many Points of Presence (POPs) in the network, generally as close as possible to the end customers to maximize the viewer s quality of experience. So to summarize this section, each live video stream source needs to be encoded into ~5-10 streams of varying bitrate in an encoding ladder. The encoding ladder set then gets packaged and distributed using ABR technologies to potentially tens of thousands of users through a CDN. By carefully tuning the encodings so that each ladder step maximizes video quality for a specific codec and target bitrate, distribution costs through the CDN can be minimized. 7

9 Alternatives for Encoding Processing It has been estimated that a large majority of the processing required for live video streaming is related to encoding. Hence, improving the efficiencies of the encoding function with minimized latency is a top priority to improve the economics for high volume live video streaming businesses. Encoding processing can be done via either software or hardware. Software encoders offer the flexibility of running on general-purpose CPUs. Software encoders such as x265 can produce high quality results for HD VOD services, but the encoding time often takes longer than real-time on most servers. Software encoding is the most For example, to deliver 1080p x265 real-time encoding for flexible option, but is likely to also live video streaming typically requires multiple compute be the most expensive option. instances running in parallel. These costs would be realized as higher CAPEX for (literally) racks of compute resources only doing encoding, or a large OPEX expense for pay-per-use cloud encoding services. Software encoding is the most flexible option, but is likely to also be the most expensive option. With respect to latency, encoding processing typically is not a significant contributor to the overall end-to-end latency of a video stream from camera to player. However, software encoding is prone to larger variations in latency, especially under high load and CPU utilization. Hardware-based encoding is often viewed as less flexible than software encoding, but delivers superior performance across many dimensions, including more density, less space, less power, and lower per stream encoding costs. Examples of hardware-assist technology includes the use of Graphical Processor Units (GPUs) or Field Programmable Gate Arrays (FPGAs) specialized hardware that offers some level of flexibility as these resources can be reprogrammed for other compute-intensive applications, such as medical research, AI or Big Data. More recently, cloud computing service providers are now also offering GPU-assisted or FPGA-assisted compute instance options for pay-per-use encoding offerings. Amongst encoding professionals, it is commonly understood that the best densities, performance and power efficiencies are achieved with System on Chip (SoC) technology. SoC technology also has the benefit of delivering low and more consistent encoding latency under high load. However, one historical drawback for an SoC approach is that the logic inside the chip can t be changed or reprogrammed easily, thereby limiting flexibility. In addition, SoC-based solutions are usually packaged in large rack-mount encoding products that are expensive to purchase and scale. Amongst encoding professionals, it is commonly understood that the best densities, performance and power efficiencies are achieved with System on Chip (SoC) technology. An ideal encoding processing architecture would achieve the densities and economics of a SoCbased solution, in a deployment model that addresses flexibility and scalability requirements. 8

10 Introducing the Codensity T400 Video Transcoding Solution The Codensity T400 video transcoding solution combines the benefits of two different areas of technology innovation for addressing encoding challenges for live video streaming SoC-based encoding densities and economics, with the flexibility and scalability of NVMe cloud storage infrastructure. Flexibility and Scalability through U.2 Packaging and NVMe Infrastructure To understand how the Codensity T400 solution improves flexibility and scalability, let s first take a moment to review recent trends and innovations in the buildout of storage architectures in cloud datacenters. The demand for cloud storage continues to rise. The megatrend in the cloud storage industry is the migration from legacy Hard Disk Drive (HDD) storage, to newer Solid- State Drive (SSD) storage based on persistent NAND flash memory. As the cost of NAND flash memory continues to fall, storage buildouts have reached a tipping point where the majority of new storage added or purchased into the cloud today is using SSD technology. HDD storage was controlled using SATA and SAS protocols, but these protocols, designed specifically for HDD, were found to be too slow for modern flash memory. To address this issue, the storage industry defined and recently standardized the Non-Volatile Memory express (NVMe) protocol to deliver much higher throughput with low latency. The NVMe command structure is also extendable, allowing non-storage applications like video encoding to benefit from the same high throughput and low latency characteristics. PCI Express (PCIe ) is the leading physical connectivity standard between host computer motherboards and peripheral devices, such as storage drives, graphic cards, or network interface boards. While a variety of PCIe physical form factors have been standardized over the years, it is the 2.5" U.2 PCIe plug-in module form factor that has gained popularity with SSD vendors and cloud data center architects as it provides a basis for easy plug-in provisioning of SSD storage capacity upgrades and maintenance. Figure 4. Codensity T400 Video Transcoder in U.2 Plug-in Module 9

11 Finally, the demand from cloud storage providers for U.2 SSD modules controlled using NVMe protocol is growing, resulting in a variety of IT vendors offering a new class of NVMe storage infrastructure options, including NVMe servers, NVMe storage arrays, and even NVMe over Fabric (NVMeoF) solutions. The Codensity T400 Video Transcoder module uses the exact same 2.5" U.2 form factor and the same NVMe protocol prevalent in SSD design and cloud storage buildouts. The Codensity T400 Video Transcoder module uses the exact same 2.5" U.2 form factor and the same NVMe protocol prevalent in SSD design and cloud storage buildouts. As the foundation for an encoding deployment model, the T400 solution offers the following benefits: Flexibility is achieved using provisionable U.2 plug-in modules, with each T400 module having more encoding capacity, at less cost than an enterprise rack-mount compute server. Scalability is achieved by potentially plugging dozens of T400 modules into NVMe servers, racks, and arrays that exist in modern data centers today, or sourced from a variety of IT infrastructure vendors. Codensity T400 Video Transcoders NVMe Server Figure 5. T400 U.2 Modules Plugging into NVMe Server Density from SoC-based Encoding and Transcoding The core technology inside every T400 video transcoder module is the Codensity G4 System-on-Chip (SoC), which supports a highly optimized silicon-based H.264 and H.265 video processing subsystem. H.264 and H.265 are based on standards, with Because codecs are based on standards, stabilized encoding algorithms, making video encoding the video compression algorithms are not and transcoding using these two codecs a perfect supposed to change, making video encoding application for SoC-based performance optimization. and transcoding a perfect application for As a result, the Codensity G4 SoC can support a SoC-based performance optimization. staggering encoding throughput of 60 frames per second (fps) of 4K UHD 4K UHD live streaming video with low latency. With this level of SoC performance, the Codensity T400 module can support 8x sessions of H.265 encoding. With complex video encoding running on the T400 modules, an additional benefit is very low utilization of the host CPU, allowing a single NVMe host server to support many T400 modules. 5 The result is a significant reduction in the processing hardware and supporting infrastructure compared to alternative solutions. 10

12 The SoC-based architecture delivers another key benefit very low power consumption. Each Codensity T400 Video Transcoder module consumes approximately 6W of power at full load, resulting in lower power and cooling requirements in the encoding data center. Figure 6 highlights two different Codensity T400 equipment configurations sized for 80 and 1,000 concurrent sessions of H.265 encoding, including power requirements and bitrate savings. For this example, the bitrate savings of 4Mbps per stream is based on streaming H.265 4Mbps, rather than H.264 through the CDN. Codensity T sessions H p30 f H p30 Codensity T400 1,000 sessions H p30 f H p30 T400 Modules 10 = 80/8 125 = 1,000/8 1RU NVMe Host Servers (10 U.2 slots per server) 1 = 10 NVMe bays in 1 server 13 = 125x T400 / 10 U.2 slots Power Consumption (estimate) 10x 6W per T x 200W per NVMe server, = 260 Watts 125x 6W per T x 200W per NVMe server, = 3,350 Watts Bitrate Savings (i.e. sending H.265 vs H.264) 320 Mbps = 80x 4Mbps 4 Gbps = 1,000x 4Mbps Figure 6 Codensity T400 Video Transcoding Equipment, Power, and Bandwidth Savings In summary, the savings in CDN and distribution bandwidth costs associated with the bitrate savings is significant, and outweighs any costs associated with a T400 deployment for the majority of live video streaming use cases. Chief Financial Officers (CFOs) will appreciate that deploying a Codensity T400 encoding solution delivers a solid ROI, with a rapid payback period. Chief Financial Officers (CFOs) will appreciate that deploying a Codensity T400 encoding solution delivers a solid ROI, with a rapid payback period. 11

13 Real-time Encoding Quality In a VOD business model, content producers have the luxury of time to use slow presets or double-pass encoding to achieve very high quality encodings with minimal file size and bitrates. For example, x265 software encoding on general purpose CPUs in very slow preset can produce high quality results, but encoding times often take longer than real-time constraints. For live video streaming content, content producers need to encode in real-time, hence software encoding solutions often break the processing down into parallel tasks running on multiple cores, thereby increasing compute resource requirements and complexity. As volumes grow, hardware-assisted transcoding becomes almost a necessity to achieve the density, performance, and economics required by the live streaming industry. Meanwhile, the encoding architect still needs to ensure that high quality is maintained at scale Encoding Quality Comparison 1080p25 Pedestrians and Cyclists mid-complexity test clip 41 PSNR, db Codensity T400 SoftwareEncode x265 faster Nvidia GPU (GTX 1080 Ti) fast Intel QSV medium Intel QSV faster Bitrate, Mbps Figure 7. Encoding Quality Comparison 6 The Codensity T400 is designed to deliver high quality H.264 and H.265 encodings and transcodings of high resolution live streaming video. Figure 7 above summarizes an encoding quality comparison, measured using a Peak Signal Noise Ratio (PSNR) analysis of the same mid-complexity 1080p25 video test clip, across five different encoding configurations. An x265 software encoding delivered a good quality score, however even when configured in faster mode, the encoding took longer than real-time on a single server. Intel s hardware assist solution is its Quick Sync Video (QSV) technology which is now a 12

14 standard feature in Intel processors. With QSV, real-time H p encoding on a single server is achievable, however quality results decreased. GPUs can support higher density of real-time encoding with better quality compared to QSV. However for this test clip, the best PSNR quality score for real-time encoding was achieved with the Codensity T400 video transcoder. The Codensity T400 is designed to deliver high quality H.264 and H.265 encodings and transcodings of high resolution live streaming video. Integration through FFmpeg Interface Many video encoding architects and developers are familiar with using FFmpeg, an open source software library with a vast suite of video processing functions. The Codensity T400 solution includes a highly-efficient FFmpeg compatible SDK, requiring operators or developers to simply apply a libavcodec patch in the host NVMe server to complete the integration. The libavcodec patch on the host NVMe server functions between Codensity T400 NVMe protocol and the FFmpeg software layer, exposing broad control of the T400 encoder configuration through FFmpeg command line options. The FFmpeg integration allows an encoding designer full flexibility to define their encoding ladder specifications using the Codensity T400 solution. Figure 8. FFmpeg Software Integration on NVMe Host Server, with Codensity T400 Video Transcoder 13

15 The FFmpeg integration allows an encoding designer full flexibility to define their encoding ladder specifications using the Codensity T400 solution, including quality presets suitable for both VOD high quality slow encodings on input MP4 files, along with real-time fast encoding presets against RTMP streaming video inputs. Using FFmpeg, the encoding engineer would configure the T400 modules to generate the multiple encoded output streams, at varying bitrates, to generate the encoding ladder set. Once an encoding ladder set is generated by the T400 solution, a separate downstream function would package the streams into a manifest file (the part of the adaptive stream that lists the different bitrates for the playback device to choose from), allowing ABR distribution of the encoded video streams through the CDN to the subscriber s end devices. Benefits The Codensity T400 Video Transcoding Solution offers many benefits to help video streaming content producers and encoding service providers. Benefit Capital Cost Savings High Density High Scalability Encoding Quality Low Latency Low Power Consumption Upgrade Flexibility Integration Flexibility Comments Fraction of the equipment cost compared to alternative solutions Realized as lower CAPEX for purchased encoding capacity, or lower OPEX for cloud encoding pay-per-use models 8x H.265 encoding sessions in T " U.2 module Leverages NVMe infrastructure used in cloud storage buildouts 10x T400 U.2 installed in a typical 1RU NVMe Server supports 80x H.265 encoding sessions Delivers high quality encodings using real-time settings Can also use slow presets for higher quality encoding Delivers predictable low latency video transcoding, even under high load across simultaneous sessions Approximately 6 Watts per T400 U.2 module Compact, provisionable U.2. plug-in modules allow flexible upgrades Firmware can also be upgraded for T400 modules deployed in field Available SDK with FFmpeg facilitates integration of T400 densities, capabilities, and benefits 14

16 Conclusions and Summary The ongoing growth in broadband and mobile bandwidth, high-resolution devices, and consumer demand for video content, is driving OTT video consumption. VOD streaming is the top category of traffic on the Internet today, while live video streaming is forecasted to grow even faster. Encoding and compression of video content using high-efficiency codecs is essential to minimize CDN distribution costs for a live streaming business. Video source content needs to be encoded in a variety of resolutions, quality, and bitrates into encoding ladders. ABR technologies package the encoding ladders, to stream the most suitable bitrate to a variety of target devices, from 4K UHD TVS to PCs, laptops, tablets, and smartphones. H.264 AVC and H.265 HEVC are the top two codecs in use, and will stay in those positions for the near term. Optimizing encoding economics across these two top codecs at scale could lead to substantial infrastructure cost savings compared to alternative encoding solutions. The Codensity T400 solution provides a new encoding The Codensity T400 solution provides architecture specifically designed to scale live video a new encoding architecture specifically streaming services with better economics. The SoCbased encoder has been shown to deliver equivalent designed to scale live video streaming services with better economics. or better quality to alternative solutions configured for real-time encoding delivery, but at much higher densities, lower power, and lower CAPEX and OPEX. The Codensity T400 solution also achieves flexibility and scalability by embracing innovations from the cloud storage industry, including provisionable 2.5" U.2 plug-in modules compatible with NVMe infrastructure being deployed today for cloud storage buildouts. Using the FFmpeg integration, encoding professionals can rapidly adopt and benefit from the Codensity T400 solution architecture. Live video streaming is growing rapidly, however encoding costs will also continue to grow rapidly using software-based encoding. By deploying a Codensity T400 solution, video streaming content producers and service providers can deliver high quality encoding at scale for live video streaming with improved performance, density, and economics. 15

17 References and Notes 1. Domo.com, Data Never Sleeps 6.0 (2018 Report) 2. Cisco Networks, Cisco Visual Networking Index (VNI) Bitmovin, Video Developer Report Streamingmedia.com, AV1: A First Look (August 31, 2018) 5. Planned T400 capacity using Codensity G4 System-on-Chip RevB available 1Q19. Codensity T400 is available today using Codensity G4 SoC RevA with lower capacities for early customer trials. 6. NETINT Technologies internal benchmarking and testing (August 2018) 16

18 ACCELERATING INTELLIGENCE NETINT Technologies is a developer of intelligent silicon solutions for data-intensive applications. Its Codensity portfolio enables enterprise, cloud data centers and content providers to deploy high performance applications that they can scale without limit, while minimizing their data storage and processing costs. NETINT, founded by an experienced team of storage SoC veterans, is a Canadian venture-funded startup with R&D facilities in Vancouver and Toronto. info@netint.ca For more information, visit NETINT, Codensity, and NETINT logo are trademarks of NETINT Technologies Inc. All other trademarks or registered trademarks are the property of their respective owners. NETINT may make changes to specifications and product descriptions at any time, without notice. This document may contain forward-looking features. The information presented in this document is for information purposes only and may contain technical inaccuracies, omissions, or typographical errors NETINT Technologies Inc. All rights reserved. PN 18WP001-02