367 research outputs found
On the Load Balancing of Edge Computing Resources for On-Line Video Delivery
Online video broadcasting platforms are distributed, complex, cloud oriented, scalable, micro-service-based systems that are intended to provide over-the-top and live content to audience in scattered geographic locations. Due to the nature of cloud VM hosting costs, the subscribers are usually served under limited resources in order to minimize delivery budget. However, operations including transcoding require high-computational capacity and any disturbance in supplying requested demand might result in quality of experience (QoE) deterioration. For any online delivery deployment, understanding user's QoE plays a crucial role for rebalancing cloud resources. In this paper, a methodology for estimating QoE is provided for a scalable cloud-based online video platform. The model will provide an adeptness guideline regarding limited cloud resources and relate computational capacity, memory, transcoding and throughput capability, and finally latency competence of the cloud service to QoE. Scalability and efficiency of the system are optimized through reckoning sufficient number of VMs and containers to satisfy the user requests even on peak demand durations with minimum number of VMs. Both horizontal and vertical scaling strategies (including VM migration) are modeled to cover up availability and reliability of intermediate and edge content delivery network cache nodes
Resource management for power-constrained HEVC transcoding using reinforcement learning
The advent of online video streaming applications and services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher video quality and more compression at the cost of increased complexity. On one hand, HEVC exposes a set of dynamically tunable parameters to provide trade-offs among Quality-of-Service (QoS), performance, and power consumption of multi-core servers on the video providers' data center. On the other hand, resource management of modern multi-core servers is in charge of adapting system-level parameters, such as operating frequency and multithreading, to deal with concurrent applications and their requirements. Therefore, efficient multi-user HEVC streaming necessitates joint adaptation of application- and system-level parameters. Nonetheless, dealing with such a large and dynamic design space is challenging and difficult to address through conventional resource management strategies. Thus, in this work, we develop a multi-agent Reinforcement Learning framework to jointly adjust application- and system-level parameters at runtime to satisfy the QoS of multi-user HEVC streaming in power-constrained servers. In particular, the design space, composed of all design parameters, is split into smaller independent sub-spaces. Each design sub-space is assigned to a particular agent so that it can explore it faster, yet accurately. The benefits of our approach are revealed in terms of adaptability and quality (with up to to 4x improvements in terms of QoS when compared to a static resource management scheme), and learning time (6 x faster than an equivalent mono-agent implementation). Finally, we show that the power-capping techniques formulated outperform the hardware-based power capping with respect to quality
Seamless multimedia delivery within a heterogeneous wireless networks environment: are we there yet?
The increasing popularity of live video streaming from mobile devices such as Facebook Live, Instagram Stories, Snapchat, etc. pressurises the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users’ quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: adaptation, energy efficiency and multipath content delivery. Discussions, challenges and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided
Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?
The increasing popularity of live video streaming from mobile devices, such as Facebook Live, Instagram Stories, Snapchat, etc. pressurizes the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of quality of experience (QoE) as the basis for network control, customer loyalty, and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: 1) adaptation; 2) energy efficiency; and 3) multipath content delivery. Discussions, challenges, and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided
SoC-Cluster as an Edge Server: an Application-driven Measurement Study
Huge electricity consumption is a severe issue for edge data centers. To this
end, we propose a new form of edge server, namely SoC-Cluster, that
orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip
network. For the first time, we have developed a concrete SoC-Cluster server
that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server
has been commercialized successfully and deployed in large scale on edge
clouds. The current dominant workload on those deployed SoC-Clusters is cloud
gaming, as mobile SoCs can seamlessly run native mobile games.
The primary goal of this work is to demystify whether SoC-Cluster can
efficiently serve more general-purpose, edge-typical workloads. Therefore, we
built a benchmark suite that leverages state-of-the-art libraries for two
killer edge workloads, i.e., video transcoding and deep learning inference. The
benchmark comprehensively reports the performance, power consumption, and other
application-specific metrics. We then performed a thorough measurement study
and directly compared SoC-Cluster with traditional edge servers (with Intel CPU
and NVIDIA GPU) with respect to physical size, electricity, and billing. The
results reveal the advantages of SoC-Cluster, especially its high energy
efficiency and the ability to proportionally scale energy consumption with
various incoming loads, as well as its limitations. The results also provide
insightful implications and valuable guidance to further improve SoC-Cluster
and land it in broader edge scenarios
A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone
Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption
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Design Space Exploration of Accelerators for Warehouse Scale Computing
With Moore’s law grinding to a halt, accelerators are one of the ways that new silicon can improve performance, and they are already a key component in modern datacenters. Accelerators are integrated circuits that implement parts of an application with the objective of higher energy efficiency compared to execution on a standard general purpose CPU. Many accelerators can target any particular workload, generally with a wide range of performance, and costs such as area or power. Exploring these design choices, called Design Space Exploration (DSE), is a crucial step in trying to find the most efficient accelerator design, the one that produces the largest reduction of the total cost of ownership.
This work aims to improve this design space exploration phase for accelerators and to avoid pitfalls in the process. This dissertation supports the thesis that early design choices – including the level of specialization – are critical for accelerator development and therefore require benchmarks reflective of production workloads. We present three studies that support this thesis. First, we show how to benchmark datacenter applications by creating a benchmark for large video sharing infrastructures. Then, we present two studies focused on accelerators for analytical query processing. The first is an analysis on the impact of Network on Chip specialization while the second analyses the impact of the level of specialization.
The first part of this dissertation introduces vbench: a video transcoding benchmark tailored to the growing video-as-a-service market. Video transcoding is not accurately represented in current computer architecture benchmarks such as SPEC or PARSEC. Despite posing a big computational burden for cloud video providers, such as YouTube and Facebook, it is not included in cloud benchmarks such as CloudSuite. Using vbench, we found that the microarchitectural profile of video transcoding is highly dependent on the input video, that SIMD extensions provide limited benefits, and that commercial hardware transcoders impose tradeoffs that are not ideal for cloud video providers. Our benchmark should spur architectural innovations for this critical workload. This work shows how to benchmark a real world warehouse scale application and the possible pitfalls in case of a mischaracterization.
When considering accelerators for the different, but no less important, application of analytical query processing, design space exploration plays a critical role. We analyzed the Q100, a class of accelerators for this application domain, using TPC-H as the reference benchmark. We found that the hardware computational blocks have to be tailored to the requirements of the application, but also the Network on Chip (NoC) can be specialized. We developed an algorithm capable of producing more effective Q100 designs by tailoring the NoC to the communication requirements of the system. Our algorithm is capable of producing designs that are Pareto optimal compared to standard NoC topologies. This shows how NoC specialization is highly effective for accelerators and it should be an integral part of design space exploration for large accelerators’ designs.
The third part of this dissertation analyzes the impact of the level of specialization, e.g. using an ASIC or Coarse Grain Reconfigurable Architecture (CGRA) implementation, on an accelerator performance. We developed a CGRA architecture capable of executing SQL query plans. We compare this architecture against Q100, an ASIC that targets the same class of workloads. Despite being less specialized, this programmable architecture shows comparable performance to the Q100 given an area and power budget. Resource usage explains this counterintuitive result, since a well programmed, homogeneous array of resources is able to more effectively harness silicon for the workload at hand. This suggests that a balanced accelerator research portfolio must include alternative programmable architectures – and their software stacks
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Multimedia delivery in the future internet
The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio
and video are produced, distributed, shared, managed and consumed on-line through various networks,
like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white
paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked
challenges of the Networked Media in the transition to the Future of the Internet.
Internet has evolved and changed the way we work and live. End users of the Internet have been confronted
with a bewildering range of media, services and applications and of technological innovations concerning
media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace
of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more
than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so
regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected
to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising
to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged
that in a near- to mid-term future, the Internet will provide the means to share and distribute (new)
multimedia content and services with superior quality and striking flexibility, in a trusted and personalized
way, improving citizens’ quality of life, working conditions, edutainment and safety.
In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe
network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as
community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of
interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and
innovative applications “on the move”, like virtual collaboration environments, personalised services/
media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content
combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P
networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to
contribute towards such a vision.
Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6)
and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily
contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way
ahead in the area of Content Aware media delivery platforms
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