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An Online Learning Approach to Network Application Optimization with Guarantee

An Online Learning Approach to Network Application Optimizat...

引用

37th IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Cai, Kechao Liu, Xutong Chen, Yu-Zhen Janice Lui, John C. S. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781538641286

Network application optimization is essential for improving the performance of the application as well as its user experience. The network application parameters are crucial in making proper decisions for network application optimizations. However, many works are impractical by assuming a priori knowledge of the parameters which are usually unknown and need to be estimated. There have been studies that consider optimizing network application in an online learning context using multi-armed bandit models. However, existing frameworks are problematic as they only consider to find the optimal decisions to minimize the regret, but neglect the constraints (or guarantee) requirements which may be excessively violated. in this paper, we propose a novel online learning framework for network application optimizations with guarantee. To the best of our knowledge, we are the first to formulate the stochastic constrained multi-armed bandit model with time-varying "multi-level rewards" by taking both "regret" and "violation" into consideration. We are also the first to design a constrained bandit policy, Learning with Minimum Guarantee (LMG), with provable sub-linear regret and violation bounds. We illustrate how our framework can he applied to several emerging network application optimizations, namely, (1) opportunistic multichannel selection, (2) data-guaranteed crowdsensing, and (3) stability guaranteed crowdsourced transcoding To show the effectiveness of LMG in optimizing these applications with different minimum requirements, we also conduct extensive simulations by comparing LMG with existing state-of-the-art policies.

关键词： Optimization Compounds Stochastic processes Throughput transcoding Task analysis Random processes

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Adaptive Video Streaming in Software-defined Mobile Networks: A Deep Reinforcement Learning Approach

Adaptive Video Streaming in Software-defined Mobile Networks...

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IEEE Global Communications Conference

作者： Jia Luo F. Richard Yu Qianbin Chen Lun Tang Zhicai Zhang Key Lab. of Mobile Comm. Tech. Chongqing Univ. of Posts and Telecom. Chongqing P. R. China Depart. of Systems and Computer Eng. Carleton Univ. Ottawa Canada School of Physics and Electronic Eng. Shanxi University Taiyuan P. R. China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Both mobile edge cloud (MEC) and software-defined networking (SDN) are technologies for next generation mobile networks. In this paper, we simultaneously optimize energy consumption and quality of experience (QoE) in video streaming over software-defined mobile networks (SDMN) with MEC. Specifically, we propose to jointly consider buffer dynamics, video quality adaption, edge caching, video transcoding and transmission. We formulate two optimization problems which can be depicted as a constrained Markov decision process (CMDP) and a Markov decision process (MDP). Then we transform the CMDP problem into regular MDP by deploying Lyapunov technique. We utilize asynchronous advantage actor-critic (A3C) algorithm, one of the deep reinforcement learning (DRL) methods, to solve the corresponding MDP problems. Simulation results are presented to show that the proposed scheme can achieve the goal of energy saving and QoE enhancement with the corresponding constraints satisfied.

关键词： Streaming media transcoding Bit rate Quality of experience Markov processes Adaptation models Task analysis

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Fast and Energy-Efficient HEVC Transrating based on Frame Partitioning Inheritance 9

Fast and Energy-Efficient HEVC Transrating based on Frame Pa...

引用

9th IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Bubolz, Thiago Conceicao, Ruhan Grellert, Mateus Zatt, Bruno Agostini, Luciano Correa, Guilherme Fed Univ Pelotas UFPel Video Technol Res Grp ViTech Pelotas Brazil Fed Univ Rio Grande Sul UFRGS Grp Microelect GME Porto Alegre RS Brazil

ISBN: (纸本)9781538623114

Video transcoding for bit rate adaptation, usually called transrating, has become an essential task to allow the transmission of different versions of the same video in streaming services and live applications. However, as the transrating operation is comprised of a decoding and an encoding step in sequence, it requires long processing times and high energy consumption, which is prohibitive in large-scale systems or battery-operated devices. This work proposes a fast and energy-efficient HEVC transrating strategy that can be employed in any HEVC codec implementation. The solution is based on a frame partitioning analysis that led to the definition of an inheritance rule between the decoding and the encoding steps. Experimental results have shown that the strategy is able to reduce the HEVC transrating time by 48.5% and the energy consumption by 50.4%, on average, with a negligible loss of 0.818% in encoding efficiency.

关键词： HEVC video coding transrating transcoding complexity reduction energy efficiency

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Performance Analysis of Video Streaming at the Edge and Core Cloud 5

Performance Analysis of Video Streaming at the Edge and Core...

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5th International Conference on Networking, Systems and Security (NSysS)

作者： Sarker, Samiul Ali Rahman, Masudur Muslim, Nasif Islam, Salekul United Int Univ Dhaka Bangladesh

ISBN: (纸本)9781728113241

Cloud computing provides application software, platform or infrastructure as a reliable service through the next-generation data centers. Recently the notion of edge Cloud has received a lot of attention with its diverse presence as fog computing, cloudlet or mobile edge. It brings Cloud computing closer to the client allowing for scalability, lower latency and smaller client-side computing footprint. In this paper, a video streaming framework has been developed that is consist of a streaming server, web server and storage server. The framework has been deployed in the edge Cloud and core Cloud to study the advantages of edge Cloud computing over core Cloud computing. For this, we have studied all the important network performance parameters including packet loss, throughput and round-trip time that influence the QoE of end user. Finally, our study has established that edge Cloud computing has significant advantages over core Cloud computing.

关键词： Cloud computing edge Cloud computing video streaming transcoding round-trip time throughput

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Unsupervised Multiple-Change Detection in VHR Multisensor Images Via Deep-Learning Based Adaptation

Unsupervised Multiple-Change Detection in VHR Multisensor Im...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sudipan Saha Francesca Bovolo Lorenzo Bruzzone Fondazione Bruno Kessler Trento Italy University of Trento Trento Italy

Change Detection (CD) using multitemporal satellite images is an important application of remote sensing. In this work, we propose a Convolutional-Neural-Network (CNN) based unsupervised multiple-change detection approach that simultaneously accounts for the high spatial correlation among pixels in Very High spatial Resolution (VHR) images and the differences in multisensor images. We accomplish this by learning in an unsupervised way a transcoding between multisensor multitemporal data by exploiting a cycle-consistent Generative Adversarial Network (CycleGAN) that consists of two generator CNN networks. After unsupervised training, one generator of the CycleGAN is used to mitigate multisensor differences, while the other is used as a feature extractor that enables the computation of multitemporal deep features. These features are then compared pixelwise to generate a change detection map. Changed pixels are then further analyzed based on multitemporal deep features for identifying different kind of changes (multiple-change detection). Results obtained on multisensor multitemporal dataset consisting of Quickbird and Pleiades images confirm the effectiveness of the proposed approach.

关键词： Feature extraction transcoding Spatial resolution Generators Training data Semantics

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Joint Content Distribution and Traffic Engineering of Adaptive Videos in Telco-CDNs

Joint Content Distribution and Traffic Engineering of Adapti...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Khaled Diab Mohamed Hefeeda School of Computing Science Simon Fraser University Burnaby BC Canada

Telco-CDNs refer to content distribution networks deployed and managed by Internet Service Providers (ISPs). They are getting popular among major ISPs because they offer new revenue streams and have the potential of providing better performance compared to traditional CDNs. Managing telco-CDNs is, however, a complex problem, because it requires jointly managing the network resources (links and switches) and the caching resources (processing and storage capacities), while supporting the adaptive nature and skewed popularity of multimedia content. To address this problem, we present a new algorithm called CAD (Cooperative Active Distribution), which strives to serve as much as possible of the requested multimedia objects within the ISP while carefully engineering the traffic paths through the network. This is achieved by enabling the cooperation among caches within the ISP not only to serve various representations of multimedia objects, but also to create them on demand using the available processing capacity of caches. We have implemented CAD and evaluated it on top of a network emulator that runs deployment code and processes real traffic. Using an actual ISP topology, our experimental results show that CAD achieves substantial performance improvements compared to the closest work in the literature, e.g., up to 64% reduction in the total inter-domain traffic.

关键词： Streaming media Servers Videos Solid modeling Multimedia systems transcoding Adaptive systems

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Live Signal Recording and Segmenting Solution Based on Cloud Architecture

Live Signal Recording and Segmenting Solution Based on Cloud...

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International Conference on Computer and Information Science (ACIS)

作者： Zhang Xin Wei Shiyao Laboratory and Equipment Management Department Communication University of China Beijing China International Communication University of China Communication University of China Beijing China

Combined with the construction of integrated media practice teaching platform and the operation of Communication University of China Network Television Station, a live signal recording and segmenting solution based on cloud architecture is proposed. The program converts the video and audio live signal into RTMP stream and pushes it to the cloud, which can perform real-time video and audio live recording and segmenting. The realization of the solution changes the working mode of the traditional manual collection of live signals, improves the production and distribution efficiency of the live program content, and implements the accurate collection and rapid segmenting processing of the live program signal in the media convergence environment.

关键词： Streaming media transcoding Media Cloud computing Production Speech recognition Real-time systems

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Intelligent Edge-Assisted Crowdcast with Deep Reinforcement Learning for Personalized QoE

Intelligent Edge-Assisted Crowdcast with Deep Reinforcement ...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Fangxin Wang Cong Zhang Feng wang Jiangchuan Liu Yifei Zhu Haitian Pang Lifeng Sun School of Computing Science Simon Fraser University Canada Department of Computer and Information Science The University of Mississippi USA

Recent years have seen booming development and great success in interactive crowdsourced livecast (i.e., crowdcast). Different from traditional livecast services, crowdcast is featured with tremendous video contents at the broadcaster side, highly diverse viewer side content watching environments/preferences as well as viewers' personalized quality of experience (QoE) demands (e.g., individual preferences for streaming delays, channel switching latencies and bitrates). This imposes unprecedented key challenges on how to flexibly and cost-effectively accommodate the heterogeneous and personalized QoE demands for the mass of viewers. In this paper, we propose DeepCast, an edge-assisted crowdcast framework, which makes intelligent decisions at edges based on the massive amount of real-time information from the network and viewers to accommodate personalized QoE with minimized system cost. Given the excessive computation complexity in this context, we propose a data-driven deep reinforcement learning (DRL) based solution that can automatically learn the best suitable strategies for viewer scheduling and transcoding selection. To our best knowledge, DeepCast is the first edge-assisted framework that applies the advance of DRL to explicitly accommodate personalized QoE optimization for crowdcast services. We collect multiple real-world datasets and evaluate the performance of DeepCast using trace-driven experiments. The results demonstrate the superiority of our DeepCast framework and its DRL-based solution.

关键词： Quality of experience Servers Bit rate Switches Bandwidth Delays transcoding

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A QoS and Container-Based Approach for Energy Saving and Performance Profiling in Multi-Core Servers

A QoS and Container-Based Approach for Energy Saving and Per...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Wellington Silva de Souza Arman Iranfar Anderson Silva Marina Zapater Samuel Xavier de Souza Katzalin Olcoz David Atienza Universidade Federal do R. G. do Norte Brazil Swiss Federal Institute of Technology Lausanne Switzerland Instituto Federal de Educação Ciência e Tecnologia da Paraíba Brazil Universidad Complutense de Madrid Brazil

In this work we present ContainEnergy, a new performance evaluation and profiling tool that uses software containers to perform application runtime assessment, providing energy and performance profiling data. It is fo... 详细信息

关键词： Quality of service Tools Software IP networks Containers Hardware transcoding

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SMART Education Framework for Adaptation Content Presentation 1

SMART Education Framework for Adaptation Content Presentatio...

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1st International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： El Janati, Salma Maach, Abdelilah El Ghanami, Driss Mohammed V Univ Mohammadia Sch Enginneers EMI LRIE Lab Dept Comp Sci Rabat Morocco

The rapid growth of the Educational System (ES) has changed traditional learning behavior and presented a new situation to learners. To address the ever increasing needs and challenges associated with ES, a strategic methods and techniques of Adaptive Learning System (ALS) is adapted, which the primary objective is the adaptation of content, presentation, and navigation. In this paper, we suggest a new framework to adapt the Content Presentation to the preferences of learners and physical disability with learners who suffer from the visual and hearing limitations to stimulate the content. This framework is intended to be integrated in Dynamic Adaptive Hypermedia System (DAHS) in a manner that increases the learner's perceived quality, high level of adaptivity and reliability. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： E-learning ES ALS Physical disability Media Adaptation DAHS Learner Model transcoding

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