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Skyfire - Experience Assurance - Cloud-based QoE Management experience assurance - cloud-based qo e management 2014

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Measurement and mitigation of bandwidth congestion in REAL TIME is now possible for every subscriber session on the operator network, thanks to Rocket Optimizer'€™s cloud-based Experience Assurance ...

Measurement and mitigation of bandwidth congestion in REAL TIME is now possible for every subscriber session on the operator network, thanks to Rocket Optimizer'€™s cloud-based Experience Assurance solution. Learn more in this short, descriptive paper.

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Skyfire - Experience Assurance - Cloud-based QoE Management  experience assurance - cloud-based qo e management 2014 Skyfire - Experience Assurance - Cloud-based QoE Management experience assurance - cloud-based qo e management 2014 Document Transcript

  • Experience Assurance Cloud-Based QoE Management 2014
  • Experience Assurance - Cloud-Based QoE Management | 2014 Page 2 Keep in touch Experience Assurance Cloud-Based QoE Management Introduction – Why QoE Matters In an era in which wireless operators are under immense competitive and investor pressure to differentiate themselves from each other, while also increasing data’s percentage of monthly ARPU – all while coping with unprecedented over-the-top video services clogging their networks - an increasingly crucial component of both happy customers and a healthy balance sheet is an ever-improving Quality of Experience (QoE) for customers. Quality of experience is fairly simply defined. QoE measures total system/network performance using subjective and objective measures of customer satisfaction. The combination of these measures helps determine a customer’s overall satisfaction with their wireless operator, which in turn determines their willingness to stay with that operator, recommend that operator, and purchase value-added (and ARPU- enhancing) services from that operator. US Smartphone Owners Who Have Switched Within Past Year (n=317) UK Smartphone Owners Who Have Switched Within Past Year (n=338) Many operators’ quality of experience has been severely tested in recent years by the explosive growth of mobile video traveling on their networks, particularly from popular OTT streaming applications like YouTube, Netflix and countless others – to say nothing of the growth of browser-based HTML5 video. These applications not only put pressure on mobile networks by consuming a tremendous amount of bandwidth, but they also must be delivered at a very fast rate in order to avoid 50%25% 75% 100% 40% 49% 26% 25% 12% 13% Price Better data quality/service (i.e. faster connection speeds, better coverage) Device Selection (i.e. to get a phone only available on another carrier) Better voice quality/service (i.e. avoid dropped calls, better coverage) Other None 50%25% 75% 100% 58% 43% 35% 31% 6% 9% Price Better data quality/service (i.e. faster connection speeds, better coverage) Device Selection (i.e. to get a phone only available on another carrier) Better voice quality/service (i.e. avoid dropped calls, better coverage) Other None
  • Experience Assurance - Cloud-Based QoE Management | 2014 Page 3 Keep in touch Experience Assurance Cloud-Based QoE Management re-buffering and slow start times. In a 2012 survey by Harris Interactive of US smartphone owners, 40% of customers who had switched wireless operators during the previous year said they did so because of “data quality/service”, each having left their current operator in search of better data connections with a new one. This was second only to “price” as a key reason for churning. In the UK, 43% of consumers who had switched operators cited “data quality/service” as a leading reason for having done so; again, this reason for leaving was cited as second only to price. When surveyed, wireless consumers are also quite clear on what “quality of experience” means to them when it comes to video. According to the same 2012 Harris Interactive survey of UK and US smartphone owners, 87% of British and 86% of US consumers indicated that when their mobile connection is poor, they care more about seeing a standard definition video which plays smoothly than seeing a high definition video with slow starts, stuttering and re-buffering. In other words, how users perceive their quality of experience is more heavily influenced by transmission quality than visual quality. Consumers are very willing to watch a video that has been optimized for their experience, with a delivery bitrate matched for their individual place on the network at that time (congested, within a building, or behind a wall), “as long as it plays”. With the latest Cisco Virtual Networking Index projecting that mobile video - already 50% of the bandwidth on global wireless networks - will grow to 66% of all bandwidth by 2017, it is clear that a leading indicator of experience quality for operators is video, and how successfully it is delivered to subscribers: free of buffering, stuttering, slow starts, audio/video sync problems and excessive pixilation. Legacy Approaches to QoE To date, the typical approach to managing and optimizing end-user video on mobile networks has been either (A) operator- defined global optimization settings that are based on time of day or other static policies, or (B) using “probes” in the radio access network (RAN) to identify congested cells – and then focusing optimization only on those congested cells. The static approach is useful in targeting optimization when it is needed – with the hope being that by performing optimization based at times when operators know congestion occurs, it will release the load and increase the average QoE. Regrettably, it is not that simple. In a typical network, only 10% to 20% of cells (at most) will be congested at any one given time, and a blanket approach such as this doesn’t focus optimization on the cells where it is needed most, nor does it take into account the realities of modern mobile video traffic, where a previously uncongested cell may be become overwhelmed due to an event or a unplanned spike in usage. The RAN probe approach came about in an effort to overcome the limitations of the aforementioned static approach by precisely identifying congested cells in the network in order to target optimization only on congested cells. The RAN probe approach is indeed more surgical, but it still has two downsides. First, most mobile networks do not currently have RAN probes deployed, and doing so requires a significant capital outlay. Second, the RAN probe approach is useful for reducing or eliminating RAN congestion – but many quality of experience issues have nothing to do with macro-level congestion, and are instead caused by impairments at the individual user level. For example, the user may be indoors and suffering from signal blockage; at the edge of a cell, with poor signal; using an old phone with a CPU that just can’t handle the bitrate of the given video they’ve requested, and so on. Clearly, a better approach is needed. View slide
  • Experience Assurance - Cloud-Based QoE Management | 2014 Page 4 Keep in touch Experience Assurance Cloud-Based QoE Management The Skyfire Breakthrough – Measure, Quantify, Mitigate Skyfire has introduced a breakthrough approach to guaranteeing end-user quality of experience in its Rocket Optimizer network traffic management and mobile video optimization solution. Measurement and mitigation of bandwidth congestion in real time is now possible for virtually every image- and video-based subscriber session on the operator network, thanks to Rocket Optimizer’s Experience Assurance feature. For the first time, wireless operators can measure, quantify and instantly manage session-level quality of experience, without the need to deploy RAN probes. Skyfire software measures bandwidth conditions in real time for all subscriber sessions on a network, and detects when a user on a poor connection is trying to stream high-quality video that will likely result in a frustrating experience. This measurement can now automatically invoke Skyfire’s cloud-based Rocket Optimizer cluster to adapt that video to fit existing capacity - taking into account RF, backhaul and spectrum bottlenecks. The end result is an insurance policy that ensures better QoE for users on crowded towers, inside buildings or at the edge of cells, as well as a solution that can “rescue” users from frustrating stalls, long video start times, and other annoyances. Rocket Optimizer provides operators with several key measurements that allow them to set effective, QoE-assuring network policy for their customers. It accurately measures and provides a baseline rate for both video start time and for video buffering without optimization, so that operators can truly see the health of their network and the quality of their user’s experience on it when nothing is being done. Then, by applying optimization on a per-session basis, operators can then see the effect that optimization has actually had on their users’ video start times and buffering rates. Legacy inline appliances, more on which is said in the following section, can provide raw numbers of “total bytes saved” via optimization, but they cannot provide detailed, dashboard-level metrics on start time and buffering, the two key metrics for QoE when it comes to video streaming. Rocket Optimizer also provides operators with average video duration and the average bit rates for each subscriber session on the network. By providing both baseline rates and how much an operator will save via optimization, and then, with optimization turned on, how much is actually being saved under different configurations, Skyfire puts full control of the network back in the hands of the operator, and finally allows QoE to be managed down to the individual session level in real time. Skyfire optimizes an affected subscriber’s video traffic by allowing as many bytes to be delivered to the device as quickly as possible without allowing a slow start time or buffering. Metrics such as these can be dynamically configured by the operator, and can be deployed on a per-cell, per-region or network-wide basis via the Rocket Optimizer dashboard. Operator using Skyfire’s cloud-based optimization solution who later find that they need more capacity can easily add it in within seconds. Additional optimization can be turned up as needed in the cloud – whether the carrier’s cloud or public clouds like Amazon EC2 - doubling or tripling capacity with the touch of a button. Only Skyfire’s flexible CloudBurst™ architecture allows for dynamic bandwidth elasticity that can stretch to sustain quality service delivery. Rocket Optimizer is able to promise Experience Assurance by virtue of its virtual buffer model, which is effectively an emulation of the media player that any given end user is using. Skyfire, by emulating the state sequence each player goes through (start/stop/play/buffer/play/etc.), accurately predicts what’s truly about to happen to that user when taking in various network data, and how best to mitigate it in real time so that every user with that given device and media player can smoothly view his or her video. View slide
  • Experience Assurance - Cloud-Based QoE Management | 2014 Page 5 Keep in touch Experience Assurance Cloud-Based QoE Management Finally, Rocket Optimizer can be additive to an operator’s existing RAN probes, and does not necessarily have to be a full replacement for an existing solution that has yet to be monetized. For instance, a RAN probe might detect cell-wide congestion issues that would benefit from additional optimization, and then, working with Skyfire software, allow for session- level optimization to be done in the cloud. Operators, using the Rocket Optimizer dashboard, can easily configure in what circumstances the two systems work with each other, and how and when to apply optimization in a given region. Can a Legacy Inline Appliance deliver Experience Assurance? Legacy optimization solutions often make use of inline appliances, rather than Skyfire’s cloud-based approach, to optimize video and images. Legacy solution vendors will argue that their solutions can assure QoE better than a cloud approach, because every single network flow must traverse their hardware-based solution. This, alas, could not be further from the truth. By detecting every flow and every session that travels through the network – regardless of whether that session contains a request for video, large images or other bandwidth-clogging traffic that is likely to cause poor QoE. Precious CPU is expended looking for and optimizing “ants” (text and small images), rather than focusing on the “elephants” that truly matter (video and large images). More relevant for an operator’s CFO and CTO offices, inline appliances are quite costly, and they do not quickly nor flexibly scale to match business needs. When capacity runs out, more hardware must be ordered and deployed throughout the network, a cycle that takes many months. This legacy model was, alas, the only network traffic optimization option for operators during the previous decade, yet in the software-defined networking (SDN) and network function virtualization (NFV) models quickly gaining precedence in operator network planning in 2013, it’s increasingly being seen as too costly, too outdated and not scalable for the somewhat daunting realities of mobile data traffic in the years to come.
  • Experience Assurance - Cloud-Based QoE Management | 2014 Page 6 Keep in touch Experience Assurance Cloud-Based QoE Management Conclusion – Real-Time Experience Assurance Is Here Rocket Optimizer establishes an entirely new model for how operators guarantee a first-rate video viewing experience for their customers. No longer will it be acceptable for operators to simply measure the aggregate number of bytes being delivered and optimized when optimization is turned on. Now operators can surgically measure, quantify and then mitigate actual customer- affecting issues in real time – before they become issues that endanger an individual customer’s quality of experience, and as a result, endanger that customer’s opinion of their operator’s network. Skyfire can do this with an incredibly CapEx-efficient solution that does not require additional equipment, and can in fact reduce the amount of optimization hardware needed – since content is only optimized when and where it truly benefits users. Ensuring better QoE for users on crowded towers, inside buildings or at the edge of cells is essential for operators looking to address the challenges raised by the unprecedented over-the-top video traffic running across their networks. Skyfire’s Rocket Optimizer answers this call in a flexible, lightweight and cost-effective manner that allows operators to chart and pilot their own destiny, and win back a large measure of control to ensure a first-rate quality of experience for each and every user who uses their network. About Skyfire Skyfire, an Opera Software company, is dedicated to leveraging the power of cloud computing to radically improve the mobile Internet experience for both operators and their consumers. Skyfire’s innovative, next-generation carrier cloud approach to mobile video and data optimization provides wireless operators with huge cost savings, elastic capacity, and the ability to surgically enhance quality of experience on a per- stream level. The company has also introduced the first mobile browser extension platform to enable robust contextual & social browsing, as well as enhanced monetization opportunities for operators. The company currently counts 4 of the largest mobile operators in the world as customers for its Rocket Optimizer™ and Skyfire Horizon™ solutions. Skyfire was founded in 2007, and is located in Mountain View, CA, in the heart of Silicon Valley. We welcome customer inquiries at partners@skyfire.com or visit us at www.skyfire.com for more information. © 2014 Skyfire, Inc. All rights reserved. Skyfire, the Skyfire logo and other trademarks, service marks, and designs are registered or unregistered trademarks of Skyfire and its subsidiaries in the USA and in foreign countries. All other trademarks are the properties of their respective owners.