601 research outputs found
Cache policies for cloud-based systems: To keep or not to keep
In this paper, we study cache policies for cloud-based caching. Cloud-based
caching uses cloud storage services such as Amazon S3 as a cache for data items
that would have been recomputed otherwise. Cloud-based caching departs from
classical caching: cloud resources are potentially infinite and only paid when
used, while classical caching relies on a fixed storage capacity and its main
monetary cost comes from the initial investment. To deal with this new context,
we design and evaluate a new caching policy that minimizes the overall cost of
a cloud-based system. The policy takes into account the frequency of
consumption of an item and the cloud cost model. We show that this policy is
easier to operate, that it scales with the demand and that it outperforms
classical policies managing a fixed capacity.Comment: Proceedings of IEEE International Conference on Cloud Computing 2014
(CLOUD 14
CLOUD LIVE VIDEO TRANSFER
As multimedia content continues to grow, considerations for more effective storage options, like cloud technologies, become apparent. While video has become a mainstream media source on the web, live video streaming is growing as a prominent player in the modern marketplace for both businesses and individuals. For instance, a business owner may want to oversee operations while he or she is away, or an individual may want to surveillance their property. In this work, we propose Cloud Live Video Streaming (CLVS) - a very efficient method to stream live video that creates a separate pricing model from modern video streaming services. The key component to CLVS is Amazon Simple Storage Service (S3), which is used to store video segments and metadata. By using S3, CLVS employs what is referred to as a ”serverless” design by removing the need to stream video through an intermediary server. CLVS also removes the need for third party accounts and license agreements. We implement a prototype of CLVS and compare it with an existing commercial video streaming software - Wowza Streaming Engine. As live video streaming becomes more common, alternative and cost effective solutions are essential
Crowdsourced Live Streaming over the Cloud
Empowered by today's rich tools for media generation and distribution, and
the convenient Internet access, crowdsourced streaming generalizes the
single-source streaming paradigm by including massive contributors for a video
channel. It calls a joint optimization along the path from crowdsourcers,
through streaming servers, to the end-users to minimize the overall latency.
The dynamics of the video sources, together with the globalized request demands
and the high computation demand from each sourcer, make crowdsourced live
streaming challenging even with powerful support from modern cloud computing.
In this paper, we present a generic framework that facilitates a cost-effective
cloud service for crowdsourced live streaming. Through adaptively leasing, the
cloud servers can be provisioned in a fine granularity to accommodate
geo-distributed video crowdsourcers. We present an optimal solution to deal
with service migration among cloud instances of diverse lease prices. It also
addresses the location impact to the streaming quality. To understand the
performance of the proposed strategies in the realworld, we have built a
prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our
extensive experiments demonstrate that the effectiveness of our solution in
terms of deployment cost and streaming quality
QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach
In the last decade, empowered by the technological advancements of mobile devices
and the revolution of wireless mobile network access, the world has witnessed an
explosion in crowdsourced live streaming. Ensuring a stable high-quality playback
experience is compulsory to maximize the viewers’ Quality of Experience and the
content providers’ profits. This can be achieved by advocating a geo-distributed cloud
infrastructure to allocate the multimedia resources as close as possible to viewers, in
order to minimize the access delay and video stalls.
Additionally, because of the instability of network condition and the heterogeneity of
the end-users capabilities, transcoding the original video into multiple bitrates is
required. Video transcoding is a computationally expensive process, where generally a
single cloud instance needs to be reserved to produce one single video bitrate
representation. On demand renting of resources or inadequate resources reservation
may cause delay of the video playback or serving the viewers with a lower quality. On
the other hand, if resources provisioning is much higher than the required, the
extra resources will be wasted.
In this thesis, we introduce a prediction-driven resource allocation framework, to
maximize the QoE of viewers and minimize the resources allocation cost. First, by
exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud
site. Second, based on the predicted results that showed to be close to the actual values,
we formulate an optimization problem to proactively allocate resources at the viewers’
proximity. Additionally, we will present a trade-off between the video access delay and
the cost of resource allocation.
Considering the complexity and infeasibility of our offline optimization to respond to
the volume of viewing requests in real-time, we further extend our work, by introducing
a resources forecasting and reservation framework for geo-distributed cloud sites. First,
we formulate an offline optimization problem to allocate transcoding resources at the
viewers’ proximity, while creating a tradeoff between the network cost and viewers
QoE. Second, based on the optimizer resource allocation decisions on historical live
videos, we create our time series datasets containing historical records of the optimal
resources needed at each geo-distributed cloud site. Finally, we adopt machine learning
to build our distributed time series forecasting models to proactively forecast the exact
needed transcoding resources ahead of time at each geo-distributed cloud site.
The results showed that the predicted number of transcoding resources needed in each
cloud site is close to the optimal number of transcoding resources
Value Creation in a QoE Environment
User behavior of multimedia services currently undergoes strong changes. This is reflected in several recent trends, e.g. the increase of rich media content consumption, preferences for more individual and personalized services and the higher sensitivity of end users for quality issues. These changes will eventually lead to strong changes in network traffic characteristics: rising congestion in peak times and less availability of bandwidth for the individual user. As a result, the quality as perceived by the end-user will decrease if network operators and service providers do not anticipate the required changes for the network. Measurable network requirements such as available video and speech quality, security and reliability are addressed by technologies that are commonly summed up in the Quality of Service (QoS) concept. However, the end-users' perception of quality is only reflected in the wider concept of Quality of Experience (QoE). This takes the measurable network requirements into account as well as customer needs, wants and preferences. For the implementation of QoE technologies several network components need to be added or changed resulting in high capital expenditures. Yet, it is not clear if these costs can be compensated with efficiency increases. Thus, new revenue streams for the network operator are necessary to incentivize investments in QoE technologies. In this paper we address four new value creation models that can serve as basis for more elaborated business models for network operators and other actors. We show how interest in QoE of the user, the content provider, the service provider and the advertiser induces new revenue streams. These models are embedded in five possible future QoE scenarios that reveal regulation, end user quality sensibility and end-to-end support as major issues for the future. --Business Models,Quality of Experience (QoE),Quality of Service (QoS),Value Creation
RL-OPRA: Reinforcement Learning for Online and Proactive Resource Allocation of crowdsourced live videos
© 2020 Elsevier B.V. With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.This work was supported by the Qatar Foundation
Cloud Cost Optimization: A Comprehensive Review of Strategies and Case Studies
Cloud computing has revolutionized the way organizations manage their IT
infrastructure, but it has also introduced new challenges, such as managing
cloud costs. This paper explores various techniques for cloud cost
optimization, including cloud pricing, analysis, and strategies for resource
allocation. Real-world case studies of these techniques are presented, along
with a discussion of their effectiveness and key takeaways. The analysis
conducted in this paper reveals that organizations can achieve significant cost
savings by adopting cloud cost optimization techniques. Additionally, future
research directions are proposed to advance the state of the art in this
important field
CloudMedia: When cloud on demand meets video on demand
Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2011 IEEE.published_or_final_versionThe 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-27
Equity research Netflix
Netflix is a media subscription services provider and production corporation headquartered in Los Gatos, California. North America remains its largest and most highly penetrated market to date, whilst the international markets accounted for roughly 90% of the company’s overall growth since the beginning of 2017. As the streaming video on demand industry is ushering in a new era of intensifying competition in 2020, the streaming provider invests significantly into its exclusive content, licensed and produced. The purpose of this paper is to provide a detailed analysis of Netflix and an estimation of the company’s enterprise value as of December 2020
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