4,932 research outputs found

    End-to-end QoE optimization through overlay network deployment

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    In this paper an overlay network for end-to-end QoE management is presented. The goal of this infrastructure is QoE optimization by routing around failures in the IP network and optimizing the bandwidth usage on the last mile to the client. The overlay network consists of components that are located both in the core and at the edge of the network. A number of overlay servers perform end-to-end QoS monitoring and maintain an overlay topology, allowing them to route around link failures and congestion. Overlay access components situated at the edge of the network are responsible for determining whether packets are sent to the overlay network, while proxy components manage the bandwidth on the last mile. This paper gives a detailed overview of the end-to-end architecture together with representative experimental results which comprehensively demonstrate the overlay network's ability to optimize the QoE

    Datacenter Traffic Control: Understanding Techniques and Trade-offs

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    Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

    Overlay networks for smart grids

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    Analysis domain model for shared virtual environments

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    The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

    WCAM: secured video surveillance with digital rights management

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    The WCAM project aims to provide an integrated system for secure delivery of video surveillance data over a wireless network, while remaining scalable and robust to transmission errors. To achieve these goals., the content is encoded in Motion-JPEG2000 and streamed with a specific RTP protocol encapsulation to prevent the loss of packets containing the most essential data. Protection of the video data is performed at content level using the standardized JPSEC syntax along with flexible encryption of quality layers or resolution levels. This selective encryption respects the JPEG2000 structure of the stream, not only ensuring end-to-end ciphered delivery, but also enabling dynamic content adaptation within the wireless network (quality of service, adaptation to the user's terminal). A DRM (Digital Rights Management) solution, called OpenSDRM is added to manage all authenticated peers on the WLAN (from end-users to cameras), as well as to manage the rights to access and display conditionally the video data. This whole integrated architecture addresses several security problems such as data encryption, integrity, access control and rights management. Using several protection lavers, the level of confidentiality can depend both on content characteristics and user rights, thus also addressing the critical issue of privacy.info:eu-repo/semantics/acceptedVersio

    Internet-of-Video Things Based Real-Time Traffic Flow Characterization

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    Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management
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