rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Modeling and Simulation in HPC and Cloud Systems
  name:
    name part: Kołodziej, Joanna.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Pop, Florin.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Dobre, Ciprian.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XX, 155 p. 35 illus., 23 illus. in color. online resource.
  abstract: This book consists of eight chapters, five of which provide a summary of the tutorials and workshops organised as part of the cHiPSet Summer School: High-Performance Modelling and Simulation for Big Data Applications Cost Action on "New Trends in Modelling and Simulation in HPC Systems," which was held in Bucharest (Romania) on September 21-23, 2016. As such it offers a solid foundation for the development of new-generation data-intensive intelligent systems. Modelling and simulation (MS) in the big data era is widely considered the essential tool in science and engineering to substantiate the prediction and analysis of complex systems and natural phenomena. MS offers suitable abstractions to manage the complexity of analysing big data in various scientific and engineering domains. Unfortunately, big data problems are not always easily amenable to efficient MS over HPC (high performance computing). Further, MS communities may lack the detailed expertise required to exploit the full potential of HPC solutions, and HPC architects may not be fully aware of specific MS requirements. The main goal of the Summer School was to improve the participants' practical skills and knowledge of the novel HPC-driven models and technologies for big data applications. The trainers, who are also the authors of this book, explained how to design, construct, and utilise the complex MS tools that capture many of the HPC modelling needs, from scalability to fault tolerance and beyond. In the final three chapters, the book presents the first outcomes of the school: new ideas and novel results of the research on security aspects in clouds, first prototypes of the complex virtual models of data in big data streams and a data-intensive computing framework for opportunistic networks. It is a valuable reference resource for those wanting to start working in HPC and big data systems, as well as for advanced researchers and practitioners. .
  table of contents: Evaluating Distributed Systems and Applications through Accurate Models and Simulations -- Scheduling Data-Intensive Workloads in Large-Scale Distributed Systems: Trends and Challenges -- Design Patterns and Algorithmic Skeletons: A Brief Concordance -- Evaluation of Cloud Systems -- Science Gateways in HPC: Usability meets Efficiency and Effectiveness -- MobEmu: A Framework to Support Decentralized Ad-Hoc Networking -- Virtualisation Model For Processing of the Sensitive Mobile Data -- Analysis of selected cryptographic services for processing batch tasks in Cloud Computing systems.
  note: edited by Joanna Kołodziej, Florin Pop, Ciprian Dobre.
  subject:
    topic: Computational intelligence
  subject:
    topic: Big data
  subject:
    topic: Computational Intelligence
  subject:
    topic: Big Data
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Big Data, 36
  identifier: 9783319737676
  identifier: https://doi.org/10.1007/978-3-319-73767-6
  record info:
    record creation date: 180131
    record change date: 20191021172502.0
    record identifier: 978-3-319-73767-6