226 research outputs found

    Algorithms for advance bandwidth reservation in media production networks

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    Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

    A State-Based Proactive Approach To Network Isolation Verification In Clouds

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    The multi-tenancy nature of public clouds usually leads to cloud tenants' concerns over network isolation around their virtual resources. Verifying network isolation in clouds faces unique challenges. The sheer size of virtual infrastructures paired with the self-serviced nature of clouds means the verification will likely have a high complexity and yet its results may become obsolete in seconds. Moreover, the _ne-grained and distributed network access control (e.g., per-VM security group rules) typical to virtual cloud infrastructures means the verification must examine not only the events but also the current state of the infrastructures. In this thesis, we propose VMGuard, a state-based proactive approach for efficiently verifying large-scale virtual infrastructures against network isolation policies. Informally, our key idea is to proactively trigger the verification based on predicted events and their simulated impact upon the current state, such that we can have the best of both worlds, i.e., the efficiency of a proactive approach and the effectiveness of state-based verification. We implement and evaluate VMGuard based on OpenStack, and our experiments with both real and synthetic data demonstrate the performance and efficiency

    ENOS: a HolisticFramework forConducting ScientificEvaluations of OpenStack

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    STACK_HCERES2020By massively adopting OpenStack for operating small to large private and public clouds, the industry has made it one of the largest running software project. Driven by an incredibly vibrant community, OpenStack has now overgrown the Linux kernel. However, with success comes an increased complexity; facing technical and scientific challenges, developers are in great difficulty when testing the impact of individual changes on the performance of such a large codebase, which will likely slow down the evolution of OpenStack. In the light of the difficulties the OpenStack community is facing, we claim that it is time for our scientific community to join the effort and get involved in the development and the evolution of OpenStack, as it has been once done for Linux. However, diving into complex software such as OpenStack is tedious: reliable tools are necessary to ease the efforts of our community and make science as collaborative as possible.In this spirit, we developed ENOS, an integrated framework that relies on container technologies for deploying and evaluating OpenStack on any testbed. ENOS allows researchers to easily express different configurations, enabling fine-grained investigations of OpenStack services. ENOS collects performance metrics at runtime and stores them for post-mortem analysis and sharing. The relevance of ENOS approach to reproducible research is illustrated by evaluating different OpenStack scenarios on the Grid’5000 testbed

    Network Function Virtualization: state-of-the-art and research challenges

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    Network Function Virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling Network Functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in Operating Expenses (OPEX) and Capital Expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of Software Defined Networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases and commercial products.Peer ReviewedPostprint (author's final draft

    SDQ: enabling rapid QoE experimentation using Software Defined Networking

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    The emerging network paradigm of Software Defined Networking (SDN) has been increasingly adopted to improve the Quality of Experiences (QoE) across multiple HTTP adaptive streaming (HAS) instances. However, there is currently a gap between research and reality in this field. QoE models, which offer user-level context to network management processes, are often tested in a simulation environment. Such environments do not consider the effects that network protocols, client programs, and other real world factors may have on the outcomes. Ultimately, this can lead to models not functioning as expected in real networks. On the other hand, setting up an experiment that reflects reality is a time consuming process requiring expert knowledge. This paper shares designs and guidelines of an SDN experimentation framework (SDQ), which offers rapid evaluation of QoE models using real network infrastructures
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