© 2013 Qwilt 1

6 Key Factors to Consider When Choosing a Transparent Caching Solution

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OVERVIEW Transparent caching solutions help carriers reduce the impact of over-the-top (OTT) video traffic on their networks, improve quality of service for their end users, and prepare their networks for the future of online video. Selecting the right solution can be an overwhelming task - not all solutions are created equal, and there is a wide range of products on the market. The optimal solution must meet operators’ operational and strategic goals, and conform to specific network infrastructure layout requirements.

NOT YOUR FATHER’S CACHE Network architectures and traffic management needs have changed drastically over the past several years, and caching technology has evolved to meet those new, more demanding requirements. The traditional cache was a fairly simple construct: it sat in the doorway of the ISP network and acted as a proxy to the Internet, caching content based on information in the Layer 3 and 4 headers (IP, TCP, UDP). As the web changed and most of the content migrated to HTTP, simply relying on Layer 3 parameters became an ineffective and inaccurate approach. The rapid growth of online video content consumption introduced another layer of complexity with varying file sizes and bit rates, media types, streaming protocols, and the mission-critical requirement of reliable and immersive subscriber experience. Legacy caching solutions repurposed to deliver video are bulky, archaic systems that combine several generic point products. The “bolted-together” approach creates a number of insertion, maintenance, performance, and availability-related issues for carriers. With the dramatic and constant increase of video traffic in carrier networks, operators need a compact, integrated, carrier-grade platform to effectively cache and deliver video at the network edge in close proximity to the subscriber. Operators must thoroughly consider the following factors to determine the best transparent caching solution for their unique needs in order to achieve a successful carrier-grade deployment:

Total Cost of Ownership (TCO) Cache Efficiency and ABR Performance Video Network Intelligence Network Insertion Configuration Pre-requisites

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TOTAL COST OF OWNERSHIP (TCO) As with any large scale network solution, cost is always one of the fundamental criteria. When comparing the costs of different caching solutions, network operators need to take into account all of the extra cost features needed to effectively cache and deliver a high volume of video content in their unique network environments. Caching products that may look like the most cost-effective solutions based on list price for the software or appliance package will likely end up costing much more when all of the required components, necessary licenses, and add-on modules or services are acquired. Carriers should carefully identify and quantify any hidden deployment and network reconfiguration costs and possible upgrade requirements to existing infrastructure. Operators need to look at the total cost to classify and deliver the required amount of video traffic. For example, while some of the required components do not directly connect to the network infrastructure, they ultimately add unforeseen equipment acquisition and deployment costs to the implementation. It is imperative to aggregate the costs of such components in order to accurately determine the overall cost per video delivery throughput. Unlike other solutions comprised of multiple generic point products, Qwilt’s unified, integrated platform approach makes the deployment and management simple and efficient, lowering the total cost of ownership (TCO) by reducing the maintenance burden on network administration teams. Rather than making configuration changes across multiple disparate point products as required by legacy caching systems, network operations staff has a single unified system to administer video delivery in their networks through an intuitive, easy to use web-based interface. In addition, the autonomous and small form factor nature of the solution allows operators to deploy the video delivery functionality at close proximity to the subscribers achieving maximum infrastructure cost savings while delivering unparalleled QoE to the end user. Qwilt’s QB-Series eliminates the need for 3rd party video content provider caches, and the associated deployment, management, maintenance, and recurring equipment leasing costs. Qwilt’s Universal Video Delivery features the fastest time-to-value in the industry, delivering savings immediately after being deployed into the network. Qwilt’s flexible “pay as you grow” pricing model, optimal subscriber edge deployment approach, and the most comprehensive media and content provider support in the industry deliver ROI to service providers faster than any competitive solution on the market.

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CACHE EFFICIENCY AND ABR As high quality streaming video continues to make its way into our living rooms and mobile devices, online video is moving away from the old days of single video clip format - known as progressive download. Although progressive download is still used for smaller, shorter video titles like some of those posted on YouTube, Adaptive Bit Rate (ABR) format is universally agreed to be the future of streaming video in fixed and mobile networks, and already represents over 80% of video traffic in many regions. The idea behind ABR is essentially breaking video files into smaller sequential chunks in varying bit rates, lengths, and file sizes. This allows content providers to optimize download times, minimize delays and interruptions, and deliver video to their consumers in the best possible quality as allowed by network conditions or end user clients. A cache is only as good as the amount of duplicate traffic it is able to offload. When considering caching efficiency, the ability to properly identify progressive download is still important, but making sure that the caching solution you are considering is able to properly classify, cache, and deliver video in ABR formats is perhaps the most critical aspect of your transparent caching solution evaluation and decision. While many vendors claim to handle ABR traffic, not all are able to do it at an acceptable level - for example, legacy caching solutions are only able to successfully cache a mere 15% of ABR traffic. In contrast, Qwilt’s next generation caching solutions with Deep Video Classification capabilities is able to cache more than 50% of ABR traffic - now that is caching efficiency.

PERFORMANCE One of the key considerations for any network equipment is performance, which is typically measured by throughput (amount of data transferred per second). A complete transparent caching solution must perform the following four key functions: Classification, Monitoring, Storage, and Video Delivery, without compromising or degrading network performance for video delivery or any other essential services In order to ascertain the performance of a complete solution, network operators need to account for the performance of all four functions working in parallel, factoring in all required network elements. In many legacy solutions this means accounting not only for the cache engines, but also for the required external storage enclosures, switches, DPI, and management appliances. As the industry moves towards greener data centers and energy efficient networks, power consumption is becoming a major consideration since typically more devices mean higher power usage and greater carbon footprint. Accurate performance measurement can be achieved by summing up all required elements and assessing the concurrent performance of the entire solution – instead of simply measuring cache-out figures which can be deceiving and inaccurate when presented out of context.

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Operators also need to measure the actual number of devices needed for processing of the traffic that will eventually lead to cache out.

The simplest way to perform an objective measurement is by normalizing performance with the

amount of rack-space or rack units (RU). Rack-space accurately reflects the size, costs and

power consumption of the solution. There are two key metrics to look for in a solution:

Classification and Analysis Throughput per RU

ATpU = ∑ {Analysis throughput of solution} /

∑ {storage rack units + networking rack units + caches rack units}

Video Delivery Throughput per RU

VTpU = ∑ {Video delivery throughput of solution} /

∑ {storage rack units + networking rack units + caches rack units}

Example: A network insertion location requiring 20 Gbps classification and analysis and 5 Gbps

of video delivery throughput. The tested solution requires several cache engines, storage

devices, switches and management servers, totaling 20 RU to achieve these requirements.

This means that the performance figures for that solution are:

ATpU = 20 Gbps / 20 RU = 1 Gbps/RU

VTpU = 5 Gbps / 20 RU = 0.25 Gbps/RU

Unlike legacy solutions, Qwilt’s QB-Series does more with less by combining all required functionality into a single, integrated platform that requires a fraction of rack space and power consumption of competing products. Qwilt’s QB-Series all-in-one software approach delivers at least 5x the performance per rack unit of any other transparent video caching vendor, resulting in a smaller footprint, higher scalability and reliability, and lower total cost of ownership (TCO) than any alternative solution.

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VIDEO NETWORK INTELLIGENCE

Video network intelligence - the ability to identify, classify and monitor video traffic in the

network – is among the most important feature of a comprehensive video delivery solution.

Online video has changed dramatically over the years, with more people viewing larger

amounts of online video content from a wide range of locations and devices. The underlying

architecture has changed considerably as well, with new technologies such as adaptive

streaming (ABR) rapidly becoming the industry standard used by many content providers.

As a result, caching solutions have to adapt to meet the increasingly heavy demands in terms of

complexity as well as performance, in order to achieve precise real time classification and

analysis. First generation caching systems typically had to rely on external third party solutions

for traffic classification and steering, which dramatically increased the complexity and footprint

of the solution. At the same time, these solutions used basic non-granular file comparison

techniques which have become inadequate and in some cases entirely obsolete in today’s

advanced online video landscape.

Qwilt’s QB-Series is the only video delivery solution with on-board network video traffic

classification capabilities. Qwilt’s Online Video Classification Engine is a core component of its

unified Universal Video Delivery platform, eliminating the need for costly and complex third

party integrations and ensuring that classification and delivery are performed seamlessly,

without impacting the network or disrupting content provider business logic - all while

delivering the highest possible quality of service to the subscriber. The QB-Series Video

Analytics application leverages the classification data to provide operators with real time and

historic video consumption and trending reports. Video Analytics is deployed in a non-intrusive

manner, providing out-of-the-box caching simulation results before the system delivers video

traffic. Qwilt’s Online Video Classification Engine utilizes a wide array of advanced content

analysis techniques for accurate video origin and format detection, and optimized network

performance. Qwilt’s dedicated Video Signature Research team ensures that the Online Video

Classification Engine solution proactively identifies and adapts to the rapidly changing media

sources and formats, enabling carriers to maintain a robust network while delivering a high

quality, immersive online video experience to their subscribers every time.

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NETWORK INSERTION: INLINE VS. OUT-OF-BAND ARCHITECTURES One of the key factors in evaluating transparent caching solutions is the insertion method into the network infrastructure. Inline appliances must sit in the flow of live network traffic, while out-of-band appliances, as their name suggests, reside outside of the network traffic path. While some vendors describe their products as out-of-band, some elements of their solution reside directly on the data path, typically used as redirection. Such solutions should be considered as inline solutions as well in this comparison. Key factors that have limited the proliferation of inline deployments in major operator networks to date should be considered when evaluating inline solutions:

Inline solutions present another physical or routed hop, which inherently adds latency to the existing network and could potentially degrade performance. Overloaded/underperforming inline solutions invariably become congestion points, introducing a point of failure into the network segments.

Inline solutions are difficult to scale because of the amount of reconfiguration of the network topology that is required to deploy them and to maintain them as networks grow and change, especially in large deployments.

Inline solution architecture, which typically hides the device’s identity, can interfere with normal network operations such as troubleshooting and debugging.

Performance limitations increase overall inline solution costs in terms of footprint and per-unit delivery capabilities. In addition, since the networks need to be reconfigured to deploy inline solutions, administrative and management overhead increase the costs even further

When compared with same criteria, out-of-band solutions feature several key strategic and operational advantages:

Out-of-band solutions are easier to deploy within an existing infrastructure, are less

invasive, and do not interfere with network operation - even if an appliance fails, it does not result in network downtime.

Out-of-band solutions do not present another physical or routed hop in the network, maintaining agile and robust network performance.

Out-of-band solutions scale readily and cost-effectively with zero adverse impact to the network.

Out-of-band solutions can be transparently inserted via optical splitters or span/mirror ports. No other infrastructure components need to be reconfigured and no additional topology changes are required, which also makes it easier to remove, replace, or

upgrade an existing out-of-band device.

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CONFIGURATION PREREQUISITES System architecture is another key factor to consider when evaluating transparent caching solutions. Network operators must be aware of all solution components required for a successful implementation before the solution can deliver actual bandwidth saving results. Specifically, carriers must consider whether they can achieve desired results with a solution that relies on multiple external network systems in order to be fully operational. First generation caching solutions typically leveraged repurposed P2P caching architecture to deliver video. The result was bulky, overly complex systems comprised of multiple generic point products that required extensive network reconfigurations in order to be implemented.

First generation inline caching approaches relied on DPI, PBR or WCCP traffic steering. In any one of the cases, network topology will have to be reconfigured to varying degrees - if not physically, then certainly logically. This often requires a significant effort in network redesign and planning, and invariably involves more effort than a unified, self-contained deployment.

Not all infrastructure routers can be configured for traffic steering schemes (for instance, WCCP is only supported by certain Cisco routers) and non-conforming devices would need to be upgraded or replaced, adding further cost and complexity to an already costly and complex solution.

So-called “out-of-band” legacy solutions that rely on BGP configurations through BGP table manipulations in the network routers face inevitable network disruptions and require access to core network control resources. Each time a new video server is introduced by any content provider site - a fairly common occurrence - its IP address needs to be manually added to all network BGP tables, requiring additional configuration demands on the network operational teams and adding further instability and outage risk to the network.

Once deployed, legacy systems introduce numerous points of failure into the infrastructure due to the sheer number of devices dispersed throughout the network. Management of disparate components requires additional time and resources from the network operations staff.

Multiple, independent, syntactically and semantically differentiated vendor-specific management consoles result in more configuration steps, e.g., black lists and white lists have to be manually synchronized across different platforms, adding yet another layer of administrative burden.

Unlike other solutions comprised of multiple generic point products, Qwilt’s unified, integrated platform approach makes the deployment and management simple and efficient, lowering the total cost of ownership (TCO) further by reducing the maintenance burden on network administration teams. Rather than making configuration changes across multiple disparate point products, network operations staff has a single unified system to manage video delivery in their networks through an intuitive, easy to use web-based and CLI interface. Qwilt’s QB-Series ships ready to use directly out-of-the-box, requiring minimal effort and configuration to deploy.

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SUMMARY Selecting a transparent caching solution for carrier networks can be a daunting task, but proper planning and preparation will ensure a successful implementation and deployment. Identifying and determining critical factors such as the number of subscribers and aggregate amount of video bandwidth it needs to support not only in near term but also accounting for future growth, network infrastructure integration strategy, anticipated and required performance levels, as well as total cost of ownership per video delivery unit will ensure an effective and successful transparent caching and video delivery deployment in carrier networks.