Storage Orchestration for Unlimited Cloud-DVR Capacity

1 Storage Orchestration for Unlimited Cloud-DVR Capacity Find out more. imaginecommunications.com

2 Storage Orchestration for Unlimited Cloud-DVR Capacity Abstract A key benefit of network-side recording of linear video content, or Cloud-DVR (cdvr), is the ability to offer multiple storage tiers to subscribers. However, the market pressure to support 200 hours or more of storage, the high aggregate bandwidth of multiple adaptive-bitrate (ABR) mobile formats and compliance with private copy copyrights, combine to make storage cost a major concern. By recognizing that not all DVR content will be consumed in the same way and that multiple types of storage technology can be intelligently deployed on a per-asset basis, service providers can seize the opportunity to architect a solution that reduces storage costs by up to a factor of five. This paper will explore storage optimization via tools that determine the likelihood of asset playback. It will also detail the characteristics of available storage technologies such as flash, spinning disks and tape, and it will discuss optimal storage models for different use cases, in terms of cost, capacity and throughput. Most importantly, the paper will review a storage-orchestration caching algorithm that can lead to cdvr deployments with significantly improved storage capacity. Offer multiple storage tiers to your subscribers with cdvr. Introduction The television industry is undergoing major upheaval. The Internet, nearubiquitous high-speed fixed and mobile broadband, and shifting video consumption patterns are redefining the modern television experience. Consumers now expect the freedom to view video content on their own terms anytime, anyplace and on any device. These technology advances and changing viewer preferences are realigning the competitive landscape. The majority of consumers continue to purchase television content packages from cable operators, satellite providers and other traditional video service providers. ABI Research estimated that the global PayTV market would approach $270 billion in 2014.

3 But the writing is on the wall. Both the number of PayTV alternatives and their ability to attract subscribers are expanding. Netflix, the leader of the SVOD pack, announced the addition of 4.33 million subscribers in 4Q, 2014. That means that more than 57 million subscribers now buy its streaming service, which has been augmented in recent years with original content. Even long-time content providers are encouraging so-called cord cutting. In 2014, CBS, HBO, Sony and others unveiled direct-toconsumer services that enable subscribers to stream programming over the Internet, rather than through traditional content distributors. Though rich with options, today s video consumption landscape is also highly fragmented. Consumers must subscribe to a variety of services to assemble a satisfying mix of the live and linear content that is the staple of PayTV services and the anytime, anyplace personalization of Overthe-Top (OTT) services. This leads to a dissatisfying experience. Each discrete video service comes with a unique user interfaces and control mechanisms. This desire for a unified, personalized television experience has created demand for a network-based service that combines the best attributes of PayTV with the personalization and on-demand characteristics of OTT services. Among the first to satisfy this demand were cable operators, satellite TV services, IPTV providers and other content distributors that began offering cdvr services a few years ago. The home-based DVR was introduced in 2000. DVR form factors have evolved over the past 15 years. Initially housed in standalone devices, DVR functions were eventually integrated into set-top-boxes (STB). More recently, PayTV providers have packaged DVRs as home gateways. These devices typically offer more storage than STB-resident DVRs, as well as the ability to access recorded content from multiple television sets and even mobile devices. Figure 1 cdvr Adoption Projections The latest stage in the evolution of the DVR is the relocation of functionality from on-premises equipment to remote storage in the cloud, ushering a new era of personalized, subscribercontrolled video consumption and creating multiple businesstransforming opportunities for service providers and content distributors. Analyst firm Parks Associates (see Figure 1) predicts that the current cdvr subscriber universe of a few million will grow to roughly 25 million globally by 2018.

4 The virtually unlimited storage capabilities of cdvr create the opportunity for service providers to offer tiered services, charging different fees for each level of storage and functionality. In addition, moving DVR functions into the network enables service providers to significantly reduce capital and operational expenses related to furnishing and maintaining homebased digital recorders. The customer-facing benefits of moving DVR services to a cloud model are extensive. Consumers will no longer miss programming due to storage constraints or limits on the number of shows they can simultaneously record. In addition, content becomes available from any device inside or outside the home through an easy-to-use interface that s consistent across all services. The major drawback of offering a comprehensive cdvr service is massive storage requirements. A single copy of an HD feature-length film can easily consume up to 6 gigabytes of disk space. Though steady breakthroughs in compression technology mitigate this situation to some degree, cost issues related to the storage of video content will only grow more acute as higher resolutions, such as 4K/UHD and, eventually, 8K, gain prominence. But that s only the tip of the storage iceberg. To offer a cloud-based video delivery service that allows subscribers to consume all of their video from any device or any location, service providers must support multiple bit rates and format options for each piece of content. That means having potentially dozens of versions of the same program always at the ready. On top of that, current copyright policies require most content distributors to store a separate copy of programming for each subscriber. The costs and complexities associated with these massive storage requirements have prevented content aggregators from delivering a fully personalized and affordable new television experience. Service providers have had to make sacrifices, including functionality, affordability or the breadth of their content catalogue, to offset storage expenses. This white paper explores the latest storage engineering tools and techniques that, when properly applied, enable service providers to cost efficiently deliver a unique and personalized television experience. Moving DVR functions into the network enables service providers to significantly reduce capital and operational expenses related to furnishing and maintaining home-based digital recorders Content: Trends & Statistics Critical to the design of a storage optimization strategy is thorough knowledge of contemporary video content trends and consumption patterns. The following provides a summary of the key developments, statistics and usage patterns that are influencing the direction of television distribution and consumption. Considered collectively, they provide insight into the design and development of cost- and bandwidth-efficient video storage options. Live Programming Matters Despite the rapid rise of the VoD market, which some sources estimated accounted for more than $25 billion in revenue in 2014, as well as a

5 growing taste for time-shifted content among TV viewers, consumers still maintain a healthy appetite for live programming. News, sports, special events and the first run of linear programming still matter to millions of viewers, as well as accounting for the nearly $300 billion annual PayTV revenue mentioned above. Not surprisingly, one of the latest trends in the VoD space is to augment vast libraries of previously broadcast shows and movies with original content. Amazon, Netflix, HBO, Sony and others are investing millions annually to create original programming. It was no coincidence that HBO timed the 2015 debut of its streaming service with the availability of a new season of the popular show Game of Thrones. And it s live programing, such as the broadcast of special events, that is keeping DVRs both on-premise and cloud-based humming. Must-see events, such as the Oscars, the World Cup and the recent The Walking Dead season finale, are often recorded by close to 90% of subscribers with DVR capabilities. The relevance of this statistic to storage system engineering will be explored later in this document. Relevant Statistics The DVR or cdvr services guarantee the subscriber s acquisition of the most desired content, while providing the on-demand experience. The following content consumption statics have been gleaned through the authors field experience with live DVR/cDVR deployments. The findings are typical of the average service provider. 50% of the recorded content is watched in the first 24 hours 80% of the recorded content is watched in the first 72 hours 7.5% of the recorded content is watched in the next 30 days 7.5% of the recorded content is never watched 5% of the recorded content (typically children s programming) is watched multiple times As would be expected, cdvr consumption is distributed throughout the day, compared to live channel consumption. Time-shifted and live video consumption peak at around the same time each day. But cdvr content playback typically doesn t cross the 20% viewership threshold, compared to average primetime viewership of 40-50%. Obviously, this observation has direct impact on solution engineering practices.

6 User Experience & Expectations Content may still be king, but user experience is quickly becoming a contender to the throne. We now live in an era of personalized media consumption. Just as the digitizing of music was a catalyst for a dramatic shift in the way audio is consumed and stored, the adoption of digital video consumption and distribution has altered the expectations of video consumers. The following represent baseline capabilities of competitive cdvr services: We now live in an era of personalized media consumption. Navigation: User Interface navigation is expected to be intuitive and responsive. It should support multiple user profiles including separate profiles for kids and parents. Similarly, user-profilebased recommendation is a requirement, as is scheduling, quota management and progress tracking, which allows each user to continue watching from the last position per user profile from the selected device. Access: Recording accessibility is required from any device (STBs, connected TVs, mobile devices, PCs and tablets), at least from inside the house, through a WiFi connection. Storage Capacity: DVR/cDVR storage requirements are measured in hours. Users expect a base package of 100 HD hours, with at least one higher tier of 200 hours made available optionally. Simultaneous Recordings: cdvr systems should be capable of at least four simultaneous recordings and at least two concurrent streams in the basic tier and four to six in higher cdvr service tiers. User Controls: Trick modes, like fast-forward and fast-rewind, are required. Imaged-based skip mode with low delay transitions is becoming a basic requirement. Video Quality: Video quality should be adaptable to the target screen. While most viewers experience sufficient quality with lowerresolutions on mobile devices and tablets, a TV screen should receive and display HD with strong indications that UHD will become a requirement as well. Network vs. On-Premises DVR To support the above-mentioned experience requirements, a premisesbased DVR or Home Gateway needs to have multiple tuners, home networking or WiFi router functionality, at least 1TB of storage, and a dualchannel HD ABR transcoder. These capabilities come with a starting point of around $250 to the operator. Combined with installation costs, truck rolls, additional IP STBs for home networking, and non-enterprise grade hard drives with an MTBF of 3 years, the total cost per subscriber can reach a staggering $400.

7 cdvr vs. npvr Services In the context of this paper, we distinguish between cdvr and npvr services. We define npvr services as the recording of live channels initiated by the operator, whereas cdvr services are initiated by subscribers. Figure 2 shows a hierarchy of services in this organization. The different content options offered through npvr services depend on the distribution rights negotiated between service providers and content providers. They can vary between channels and even programs within a channel. These services include: cdvr (user based) User Initiated EPG Driven cdvr (network based) Time Driven Figure 2 A classification of cdvr and npvr services. Schedule Recording Pause Live TV Start Over Live to VoD Catchup TV Pause/Time- Shifted TV Pause Live TV (PLTV) allows the subscribers to pause a live channel and resume playback during a limited time-window, for example an hour. Playback includes trick modes, such as fast-forward, with an ability to skip forward to the live transmission. TimeShiftTV (TSTV) allows the user to go backward in a live channel within a limited time-window, for example three hours. Unlike a local DVR, the network service supports shifting of content the STB was not tuned to. StartOverTV (SOTV) similarly to TSTV, but limited to playback from the beginning of the currently playing program. Catchup TV (CUTV) (sometimes referred as reverse EPG) allows the subscriber to go backward in time in an electronic program guide (EPG) and view content that was aired within a time-window, for example three months. These npvr services may be available in conjunction to cdvr services, allowing subscribers to make recordings of a program from its beginning, even if the recording command is issued in the middle of the program or after the program ended (a few hours, days, or even weeks ago). Typically, cdvr services are distinguished by a storage allocation quota on a peruser basis. Regardless of private copy or shared copy scenarios, users can schedule recording events and browse through their personal recordings. cdvr Storage Deployment Before analysing storage and networking solutions, it s important to review a few approaches to cdvr storage deployment. Shared copy: In this scenario, each subscriber makes separate recording requests, but every asset (or each ABR fragment of an asset) is stored only once. In contrast to npvr scenarios, fragments or programs that are not

8 recorded by any user are not stored. Each user has a quota, for example 100 hours of recordable programs; programs may be automatically or manually deleted from each user s list of recordings. Once a program or a fragment is not referenced by any subscriber, it s deleted. The viewing statistics mentioned earlier in this document validate that caching servers can provide significant network efficiency in this model. Immediate Private Copy: In this scenario, every subscriber s recording creates an asset copy within a delay that can be as short as 1.2 seconds, per the so-called CableVision ruling. For example, if millions of subscribers schedule a record of the Oscars, an equal number of separate copies are created and created immediately, with almost no delay. Any subscriber s pause or playback of the private copy is done directly from the subscriber s unique copy. Eventual Private Copy: There are multiple variations of this deployment method, depending on the service provider s preferences. In these scenarios, an actual private copy per subscriber is still created, but within much longer delay than 1.2 seconds. This delay can vary widely from 10s of seconds (corresponding to the lifetime of a fragment in a linear ABR CDN origin buffer) to three days (the so-called C3 window during which recorded ad playback is still counted toward viewership analytics). During this delay, any playback is done from a shared copy. One variation on this approach is to quickly create a de-duplicated, private copy reference a unique filename pointing initially to the same physical bits in storage with the actual copying of the physical bits occurring later. Since peak ingest usage is as high as 90%, delaying the creation of the physical private copy reduces the system performance requirements significantly. Adaptive Bitrate Considerations Another important aspect of cdvr deployment is the required bitrate and format support for the growing number of multiscreen targets, such as mobile devices, tablets, PCs, game consoles, IP STBs and connected TVs. Available formats include HTTP Live Streaming (HLS) from Apple, Smooth Streaming (HSS) from Microsoft, HTTP Dynamic Streaming (HDS) from Adobe, and Dynamic Adaptive Streaming over HTTP (DASH) from MPEG. Several formats come in multiple variations, for example HLSv2, where audio and video are muxed, versus HLSv4, where video and audio may be separated. All of these formats must be supported. Profile TS BitRate (Mbps) Audio_AAC LDC Video BitRate Resolution Frame Rate Aspect Ratio STB-HD 6.9 80 kbps x2 48Khz 6.5 1920x1080p 30 16:09 High 4.8 56 kbps x2 48Khz 4.5 1280x720p 30 16:09 High 3.8 56 kbps x2 48Khz 3.5 1280x720p 30 16:09 Main 2.8 56 kbps x2 48Khz 2.5 864x486p 30 16:09 Main 1.8 56 kbps x2 48Khz 1.5 864x486p 30 16:09 Main 1.1 56 kbps x2 48Khz 0.8 640x360p 30 16:09 Main 0.8 56 kbps x2 48Khz 0.5 640x360p 30 16:09 1 Frames Only 0.3 56 kbps x2 48Khz 0.04 640x360p 0.5 16:09 Table 1 Bitrates and Resolutions for a typical ABR service

9 HTTP adaptive streaming formats are designed to accommodate dynamic network bitrates and client resolution and presentation capabilities. A typical high definition (HD) ABR service, for example, requires four-toeight different bit rates. Table 1 provides a detailed view of various bitrates. Every service provider will employ a slightly different ABR strategy, varying the number of profiles and bitrates. Where the service includes streaming to large TV screens, a higher bitrate first profile is typically used for 1080i/1080p30/720p60. The introduction of new devices supporting HEVC will require support for additional profiles, as illustrated in Table 2. Profile TS BitRate (Mbps) Audio_AAC LC Video BitRate Resolution Frame Rate Aspect Ratio Compression Format STB-UHD 18.4 80 kbps x2 48Khz 18 3840x2160p 60 16:09 HEVC STB-1080p60 6.4 80 kbps x2 48Khz 6 1920x1080p 60 16:09 HEVC STB-HD 7.0 80 kbps x2 48Khz 6,6 1920x1080p 30 16:09 AVC High 5.0 56 kbps x2 48Khz 4.7 1280x720p 30 16:09 AVC High 3.5 56 kbps x2 48Khz 3.2 1280x720p 30 16:09 AVC Main 2.5 56 kbps x2 48Khz 2.2 864x486p 30 16:09 AVC Main 1.8 56 kbps x2 48Khz 1.5 864x486p 30 16:09 AVC Main 1.1 56 kbps x2 48Khz 0.8 640x360p 30 16:09 AVC Main 0.8 56 kbps x2 48Khz 0.5 640x360p 30 16:09 AVC 1 Frames Only 0.3 56 kbps x2 48Khz 0.04 640x360p 0.5 16:09 AVC Table 2 Bitrates and Resolutions for an ABR service with UHD Storage Optimization Techniques The following section details several approaches optimizing storage for cdvr services. Just-In-Time (JIT) Packaging A cdvr deployment requires every channel to be transcoded according to one of the above ABR tables and all profiles to be stored to enable clients to adapt to changing network conditions. As mentioned above, in order to support multiple playback clients, content must be recorded in multiple formats: HLS to support ios devices, HSS to support Xbox and PC clients, HDS for both STB and PC clients, etc. Storing each recorded asset in multiple formats is a huge multiplier of needed storage. Instead, we suggest, and assume for this paper, storing the segments in a single mezzanine format, such as DASH-TS or HLSv4, while using Just-intime-Packaging (JITP) technology to re-package the mezzanine ABR fragments to the clients requested delivery format at the time a fragment (or manifest) is requested. Using a JIT packager also allows application of session-based encryption, where it s required. Variable Range Example Library size (hours/sub) 20-200 40 MBR bitrate (Mbps) 10 to 20 20 Number of subscribers 10K-10M 50,000 Peak concurrency 5%-20% 20% Cost of storage ($/TB) $200-$500 $250 Number of ABR formats 1 to 4 3 Cost of JITP Server $8,000 JITP Sessions per server 2500 Table 3 Variables affecting storage and JITP cost

10 The use of JITP trades storage savings against additional computation (for the additional re-packaging). A model can be used to determine whether the additional cost of computation offsets the reduced storage cost. The cost of storing the complete library in multiple formats depends on multiple factors listed in Table 3. The table includes typical ranges of values and an example value. The cost of storage of all formats compared to the cost of storing the mezzanine format and the cost of the JITP servers for the example value is shown in Table 4. Because the JITP and storage costs both scale linearly with subscribers, library size and bitrate, only the number of ABR profiles, the concurrency, and the cost of storage affect the cost trade-off. But even with two formats, 100% concurrency, and a ridiculously low $10/TB storage cost, storage is still more expensive than JITP. Storage Cost for all formats $13,500,000 Cost of JITP Servers $32,000 Cost of JITP Server + Mezzanine $4,532,000 JITP brings additional future-proofing benefits by providing a simple mechanism to introduce new formats (such as the emerging DASH format) and to support feature evolution in existing formats. Table 4 Cost of Storage: All Formats vs. Mezzanine JIT Transcoding Transcoding technology has reached a point where in many situations, it is more cost-effective to store only the highest bitrate profile in the ABR table and then transcode to other profiles at the time of the client request. The storage efficiency of using Just-In-Time Transcoding (JITT), shown below, is proportional to the reduction in storage cost. JITT storage efficiency = For example, the JITT storage efficiency of ABR Table 1 is 66%. (Note that there are two audio channels and eight video streams in this table.) In the Immediate Private Copy cdvr deployment method, storage is typically the highest-cost component. As with JITP, JITT offsets reduction in storage with additional computational cost. JITT costs today are still too high to allow its use for all storage, so in order to balance storage costs with JITT costs, it s necessary to limit the JITT operation to a portion of the streams. Optimization can be achieved by limiting JITT to assets with a certain streaming probability. Cost analysis shows that limiting JITT resources to 20% of the streams saves around 54% of total storage and about 50% of the total infrastructure cost of storage and JITT servers. Reduction in total storage also reduces space and operating expense related to power consumption and HDD replacements. Streaming Probability Streaming probability, calculated for each asset, helps the cdvr recording system decide whether to store all profiles or just one profile, streaming it through the JITT servers for playback. The streaming probability can also be used to determine where to store an asset. For example, if the streaming probability of a shared copy asset is very high, it is best to store

11 it in edge servers. If it s very low, archiving it to tape, where storage cost is very low but streaming throughput is also low, becomes a possibility. The caching algorithm in a Shared Copy deployment method is an effective way to calculate an asset s streaming probability. Caching servers have limited storage. Least-viewed assets are dropped to make space for new, more-viewed assets. Caching algorithms maintain viewing statistics on each asset in order to know which assets to erase from storage. These statistics directly reflect the streaming probability for each asset. Caching algorithms are well known in the industry and are optimized in most HTTP servers and Content Delivery Networks (CDN). Caching, however, doesn t apply to cdvr private copies. Analysing content consumption statistics shows that, unlike the shared copy usecase, where an asset being viewed indicates it s likely to be viewed again, a private copy has only a 5% chance of being viewed more than once (for example, as is often the case with children s shows). Another statistical input relates to private copies that are never watched. If an asset isn t viewed within 72 hours of being recorded, the chance it will be watched on the 4th day is roughly 20%, diminishing with time, as illustrated in Figure 4. Figure 3 Seven days Streaming Probability of an asset recorded at 10pm

12 The stream viewing statistics discussed above roughly a geometric progression in which half of viewed content is less than a day old and half is older - can be used to model the optimal time to save strict private copy storage by deleting the lower profiles of an asset and utilizing JITT to reconstruct those profiles when necessary. Deleting the profiles quickly reduces storage cost but increases JITT cost, as played assets are more likely to require JITT reconstruction of lower profiles in order to be played back. Conversely, keeping all profiles of all assets for longer increases storage cost but reduces JITT cost. A minimal combined cost is shown for a cost model in Figure 3. What might cause this optimal value to shift? In the last 30 years, storage costs have been halving roughly every 14 months, and compute costs have been halving roughly every 18 months. So for the next few years, we can expect reductions in storage and JITT costs to be proportionally the same, meaning the optimal minimum won t shift much due to changes in compute or storage costs. For example, later this year 8TB NL-SAS drives will be released, and they will quickly reach today s price for 6TB drives and the price of 4TB drives 6 months ago. So over the next 14 months, roughly, disk storage will double from 4TB to 8TB at a about the same price. At the same time, in the next two years we anticipate that AVC compression prices will continue to improve due to Moore s law and that HEVC compression will get closer to current AVC compression pricing, introducing an additional factor of 2 reduction in computational cost. Figure 4 xjitt vs. Storage, cost analysis The graph minimum changes with both peak playback concurrency and daily quantity of newly stored content, but it doesn t depend on the storage allocated per subscriber. The ratio of JITT to storage pricing, however, is sensitive to the amount of storage allocated per subscriber. More storage means more content with low streaming probability, making JITT savings even better. When the streaming probability is low enough, it s beneficial to store the highest profile of assets on the next tier of storage technology tape. The next section reviews the cdvr network topology and analyses the different cost-performance considerations of storage technologies.

13 The cdvr Storage-Network Diagram Figure 5 shows the network diagram for a cdvr deployment, highlighting the storage tiers and different application points for JITP and JITT. Content is transcoded and made available to both a shared-copy and private-copy storage system. An ingest orchestrator is used to create assets, whether private or shared, for users to stream. JITT and JITP processes make efficient use of storage, while edge servers optimize shared copy distribution. A private-copy repository can be used to enable multiple tiers of storage for content with different streaming probabilities. Ingest Orchestrator Shared Copy Origin Servers JITP Shared Copy Origin Servers ABR Transcoder JITP JITP Private Copy Origin Servers Client Private Copy Origin Servers The role of the ingest orchestrator is to implement the policy decisions of the deployment for shared, immediate private, and eventual private copies. It also plays a significant role in load balancing file copies between the different origin servers and the asset life cycle based on the streaming probability. Figure 5 Network diagram for cdvr deployments.

Storage Orchestration Technologies for Unlimited Cloud-DVR Analysis Capacity Table 5 provides a detailed assessment and comparison of four types of cdvr solutions, including a Total Cost of Ownership (TCO) analysis. 14 Shared Copy Edge Shared Copy Origin Private Copy Origin Private Copy Repository Server (Flash) Server (NL SAS) Server (NL SAS) (Tape) Ingest type By usage Immediate Immediate Delayed1 Scalability By subscribers By channels By subscribers By subscribers Stored formats Used segments, mezzanine packaging format All profiles, mezzanine packaging format Mostly top profile2, mezzanine packaging Top profile, mezzanine packaging format format Video Technology JITP - JITP, JITT - Typical system size3 2RU / server 1 Rack 1 Rack 14 Racks Storage type MLC NAND Flash NL SAS NL SAS E. Tape Storage protection - RAID-50 Predictive Failure4 - Practical MTBF5 5 years 3 years 3 years 10 years Storage Capacity 20 TB 2 PB 2 PB 65 PB Effective/Usable 20 TB 1.7 PB 5.5 PB 6 195 PB 6 Throughput 7 140 Gbps 80 Gbps 140 Gbps 360 Gbps Practical ingress 7 10 Gbps 1 Gbps 100 Gbps (to disks) 330 Gbps Practical egress 7 100 Gbps 79 Gbps 10 Gbps 30 Gbps Number of subs 8 12,000 500,000 6,000 210,000 5-years TCO est. 9 Per TB (storage capacity) $5,500 $600 $250 $21 Per HD hour10 (storage) $50 $2 $5 $0.2 Per Gbps (throughput) $1,400 $9,000 $12,000 $12,000 Per sub (100h, ABR t-1) $10 $2 $200 $20 1. Archiving on tape allows up to 10x reduction in the 5-year total cost of ownership (TCO). The challenges with tape technology are throughput and loading time. The loading time can be less than 10 seconds and is mitigated by keeping the first 10 seconds of an asset at the Private Copy Origin Server. The throughput challenge is met by only storing assets with low streaming probability on tape. 2. Balance storage and JITT cost by storing all profiles for content with high Streaming Probability. Cost analysis shows that limiting JITT resources to 20% of the streams saves around 54% of total storage and about 50% of the total infrastructure cost of storage and JITT servers. It is done by removing the lower bit rate profiles when the streaming probability drops below 20%. 3. There are obviously different product options. Product assumptions were made for this paper, but the fundamental performance analysis is based on the common storage building blocks (Flash, SAS/SATA, and Tape). 4. Since cdvr Private Copy is very sensitive to storage pricing, disk predictive failure analysis is a better approach for content protection. 5. The practical MTBF represents observations for the average time to replace a failed storage device under the network and streaming conditions of a cdvr application. 6. The values reflect compression gains related to storing the top ABR profile only and using JITT technology to recreate the other profiles during play-back. Values are based on ABR table 1. 7. Throughput is the actual system capacity, while egress and ingress relate to practical maximum usage of each system in its cdvr function. 8. Based on ABR table 1, 200 channels, 100 hours per sub. 9. Estimation based on general assumptions of some hardware and software prices, including local networking and compute, disks MTBF, SW annual maintenance, power and cooling costs and facility/space cost. 10. A different way to look at storage pricing for cdvr is the price per HD hour. It s an efficient metric for evaluating total cost in environments with a mix of shared and private: content Shared = # of channels x CDN average TTL x Price per HD Hour (Edge + Origin) Private = # of subscribers x quota in hours per sub x Price per HD Hour (Origin + tape)

15 Cloud And CDN For cdvr Public cloud and CDN may significantly accelerate cdvr deployments. It is possible to set up a service and scale it for a few thousands subscribers in a matter of days. There are two fundamental challenges with this approach when scaling beyond the initial pilot. One is storage cost and the second is the Cloud/CDN pricing, especially for egress. Cloud and CDN storage is designed to support high throughput from general compute platforms and typically relies on multiple copies for resiliency to loss. This approach may fit the cdvr Shared Copy deployment method, but it s impractical for cdvr private copies at large scale. The main problem is network peering cost or cloud/cdn egress pricing. With a typical range of 1-2 per GB delivered, this cost represents more than 80% of the overall cost and multiplies the estimated 5-years TCO by a high factor. Conclusions A careful application of the technologies presented in this paper: JITP, JITT and storage tiers with streaming probability analysis can significantly reduce not just the cost of storage, but the overall cost of deployment. JITP can reduce overall costs somewhere between two-to-three times, and a further 50%-70% storage reduction via JITT makes these technologies indispensable for service providers wishing to deploy cdvr services. Storage orchestration by streaming probability allows asset and sub asset placement between the edge servers, the Origin servers and the archive, while guaranteeing content delivery and providing unlimited capacity. JITP can reduce overall costs somewhere between two-to-three times, and a further 50%-70% storage reduction via JITT

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