The Experts below are selected from a list of 71460 Experts worldwide ranked by ideXlab platform

Keith Winstein - One of the best experts on this subject based on the ideXlab platform.

  • learning in situ a randomized experiment in video streaming
    Networked Systems Design and Implementation, 2020
    Co-Authors: Francis Y Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, Keith Winstein
    Abstract:

    We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real Deployment Environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.

Liu Huanhua - One of the best experts on this subject based on the ideXlab platform.

  • meta model of paas based cloud application s Deployment Environment
    Computer Science, 2015
    Co-Authors: Liu Huanhua
    Abstract:

    PaaS is one of the service paradigms of cloud computing,which is used to provide the application container service.Calling API and editing configuration files are the main way of cloud application Deployment in PaaS,which needs a lot of learning costs and are error-prone.API and configuration files of different PaaS have different syntax,as a result,application migration on PaaS is very difficult and cross-platform or multi-platform Deployment is scarcely possible.This article proposed the meta-model of PaaS-based cloud application's Deployment Environment,which can lower the learning costs,make the Deployment process more automated,simplify application migration,and make cross-platform or multi-platform Deployment possible.

Ingemar Kaj - One of the best experts on this subject based on the ideXlab platform.

  • probabilistic analysis of hierarchical cluster protocols for wireless sensor networks
    Lecture Notes in Computer Science, 2009
    Co-Authors: Ingemar Kaj
    Abstract:

    Wireless sensor networks are designed to extract data from the Deployment Environment and combine sensing, data processing and wireless communication to provide useful information for the network users. Hundreds or thousands of small embedded units, which operate under low-energy supply and with limited access to central network control, rely on interconnecting protocols to coordinate data aggregation and transmission. Energy efficiency is crucial and it has been proposed that cluster based and distributed architectures such as LEACH are particularly suitable. We analyse the random cluster hierarchy in this protocol and provide a solution for low-energy and limited-loss optimization. Moreover, we extend these results to a multi-level version of LEACH, where clusters of nodes again self-organize to form clusters of clusters, and so on.

Gruia-catalin Roman - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Application Deployment in Shared Sensor Networks Based on Quality of Monitoring
    2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium, 2010
    Co-Authors: Subhashish Bhattacharya, C. Lu, Abusayeed Saifullah, Gruia-catalin Roman
    Abstract:

    Wireless sensor networks are evolving from dedicated application-specific platforms to integrated infrastructure shared by multiple applications. Shared sensor networks offer inherent advantages in terms of flexibility and cost since they allow dynamic resource sharing and allocation among multiple applications. Such shared systems face the critical need for allocation of nodes to contending applications to enhance the overall Quality of Monitoring (QoM) under resource constraints. To address this need, this paper presents Utility-based Multi-application Allocation and Deployment Environment (UMADE), an integrated application Deployment system for shared sensor networks. In sharp contrast to traditional approaches that allocate applications based on cyber metrics (e.g., computing resource utilization), UMADE adopts a cyber-physical system approach that dynamically allocates nodes to applications based on their QoM of the physical phenomena. The key novelty of UMADE is that it is designed to deal with the inter-node QoM dependencies typical in cyber-physical applications. Furthermore, UMADE provides an integrated system solution that supports the end-to-end process of (1) QoM specification for applications, (2) QoM-aware application allocation, (3) application Deployment over multi-hop wireless networks, and (4) adaptive reallocation of applications in response to network dynamics. UMADE has been implemented on TinyOS and Agilla virtual machine for Telos motes. The feasibility and efficacy of UMADE have been demonstrated on a 28-node wireless sensor network testbed in the context of building automation applications.

Rajiv Ranjan - One of the best experts on this subject based on the ideXlab platform.

  • a survey on modeling energy consumption of cloud applications deconstruction state of the art and trade off debates
    IEEE Transactions on Sustainable Computing, 2017
    Co-Authors: Selome Kostentinos Tesfatsion, Saeed Bastani, Ahmed Alieldin, Erik Elmroth, Maria Kihl, Rajiv Ranjan
    Abstract:

    Given the complexity and heterogeneity in Cloud computing scenarios, the modeling approach has widely been employed to investigate and analyze the energy consumption of Cloud applications, by abstracting real-world objects and processes that are difficult to observe or understand directly. It is clear that the abstraction sacrifices, and usually does not need, the complete reflection of the reality to be modeled. Consequently, current energy consumption models vary in terms of purposes, assumptions, application characteristics and Environmental conditions, with possible overlaps between different research works. Therefore, it would be necessary and valuable to reveal the state-of-the-art of the existing modeling efforts, so as to weave different models together to facilitate comprehending and further investigating application energy consumption in the Cloud domain. By systematically selecting, assessing, and synthesizing 76 relevant studies, we rationalized and organized over 30 energy consumption models with unified notations. To help investigate the existing models and facilitate future modeling work, we deconstructed the runtime execution and Deployment Environment of Cloud applications, and identified 18 Environmental factors and 12 workload factors that would be influential on the energy consumption. In particular, there are complicated trade-offs and even debates when dealing with the combinational impacts of multiple factors.