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  • Book
  • Open Access
  • © 2016

Resource Management for Big Data Platforms

Algorithms, Modelling, and High-Performance Computing Techniques

  • Provides a comprehensive overview of the development of RMS for big data platforms and applications, covering theory, methodologies, experimentation, and real-world applications

  • Presents state-of-the-art solutions for issues of big data processing, resource and data management, fault tolerance, monitoring and controlling, and security

  • Discusses the development of related programming models and technologies in information and communication, and how these help in formulating practical solutions for the topics covered

  • Includes supplementary material:

Part of the book series: Computer Communications and Networks (CCN)

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-44881-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 139.99
Price excludes VAT (USA)
Hardcover Book USD 199.99
Price excludes VAT (USA)

Table of contents (23 chapters)

  1. Front Matter

    Pages i-xiii
  2. Architecture of Big Data Platforms and Applications

    1. Front Matter

      Pages 1-1
    2. Performance Modeling of Big Data-Oriented Architectures

      • Marco Gribaudo, Mauro Iacono, Francesco Palmieri
      Pages 3-34
    3. Workflow Scheduling Techniques for Big Data Platforms

      • Mihaela-Catalina Nita, Mihaela Vasile, Florin Pop, Valentin Cristea
      Pages 35-53
    4. Cloud Technologies: A New Level for Big Data Mining

      • Viktor Medvedev, Olga Kurasova
      Pages 55-67
    5. Maximize Profit for Big Data Processing in Distributed Datacenters

      • Weidong Bao, Ji Wang, Xiaomin Zhu
      Pages 83-95
    6. Energy and Power Efficiency in Cloud

      • Michał Karpowicz, Ewa Niewiadomska-Szynkiewicz, Piotr Arabas, Andrzej Sikora
      Pages 97-127
    7. Context-Aware and Reinforcement Learning-Based Load Balancing System for Green Clouds

      • Ionut Anghel, Tudor Cioara, Ioan Salomie
      Pages 129-144
  3. Big Data Analysis

    1. Front Matter

      Pages 145-145
    2. High-Performance Storage Support for Scientific Big Data Applications on the Cloud

      • Dongfang Zhao, Akash Mahakode, Sandip Lakshminarasaiah, Ioan Raicu
      Pages 147-170
    3. Information Fusion for Improving Decision-Making in Big Data Applications

      • Nayat Sanchez-Pi, Luis Martí, José Manuel Molina, Ana C. Bicharra García
      Pages 171-188
    4. Load Balancing and Fault Tolerance Mechanisms for Scalable and Reliable Big Data Analytics

      • Nitin Sukhija, Alessandro Morari, Ioana Banicescu
      Pages 189-203
    5. Fault Tolerance in MapReduce: A Survey

      • Bunjamin Memishi, Shadi Ibrahim, María S. Pérez, Gabriel Antoniu
      Pages 205-240
    6. Big Data Security

      • Agnieszka Jakóbik
      Pages 241-261
  4. Biological and Medical Big Data Applications

    1. Front Matter

      Pages 263-263
    2. Big Biological Data Management

      • Edvard Pedersen, Lars Ailo Bongo
      Pages 265-277
    3. Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms

      • Suejb Memeti, Sabri Pllana, Joanna Kołodziej
      Pages 279-309
    4. Feature Dimensionality Reduction for Mammographic Report Classification

      • Luca Agnello, Albert Comelli, Salvatore Vitabile
      Pages 311-337
    5. Parallel Algorithms for Multirelational Data Mining: Application to Life Science Problems

      • Rui Camacho, Jorge G. Barbosa, Altino Sampaio, João Ladeiras, Nuno A. Fonseca, Vítor S. Costa
      Pages 339-363

About this book

Serving as a flagship driver towards advance research in the area of Big Data platforms and applications, this book provides a platform for the dissemination of advanced topics of theory, research efforts and analysis, and implementation oriented on methods, techniques and performance evaluation. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These chapters discuss the research of members from the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). This volume is ideal as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and their potential solutions.


  • Big-Data platforms
  • High-performance computing
  • Modelling and simulation
  • Massive data processing
  • Performance analysis

Editors and Affiliations

  • University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  • Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  • Second University of Naples, Naples, Italy

    Beniamino Di Martino

About the editors

Dr. Florin Pop is an Associate Professor in the Distributed Systems Laboratory of the Computer Science Department at the University Politehnica of Bucharest, Romania.

Dr. Joanna Kołodziej is a Professor in the Department of Computer Science at Cracow University of Technology, Poland. Amongst her recent publications are the Springer titles Intelligent Agents in Data-intensive Computing and Evolutionary Based Solutions for Green Computing.

Dr. Beniamino Di Martino is a full Professor of Information Systems at the Second University of Naples, Italy. His publications include the Springer titles Cloud Portability and Interoperability and Smart Organizations and Smart Artifacts.

Bibliographic Information

  • Book Title: Resource Management for Big Data Platforms

  • Book Subtitle: Algorithms, Modelling, and High-Performance Computing Techniques

  • Editors: Florin Pop, Joanna Kołodziej, Beniamino Di Martino

  • Series Title: Computer Communications and Networks

  • DOI:

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer International Publishing AG, part of Springer Nature 2016

  • Hardcover ISBN: 978-3-319-44880-0Published: 04 November 2016

  • Softcover ISBN: 978-3-319-83155-8Published: 16 June 2018

  • eBook ISBN: 978-3-319-44881-7Published: 27 October 2016

  • Series ISSN: 1617-7975

  • Series E-ISSN: 2197-8433

  • Edition Number: 1

  • Number of Pages: XIII, 516

  • Number of Illustrations: 81 b/w illustrations, 57 illustrations in colour

  • Topics: Computer Communication Networks, Computer Modelling, Hardware Performance and Reliability, Database Management

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-44881-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 139.99
Price excludes VAT (USA)
Hardcover Book USD 199.99
Price excludes VAT (USA)