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Special Issue on Resource Management for Big Data Platforms (FGCS)


Special Issue on Resource Management for Big Data Platforms 

Scope and objective

Nowadays, when we face with numerous data, when data cannot be classified into regular relational databases and new solutions are required, and when data are generated and processed rapidly, we need powerful platforms and infrastructure as support. Extracting valuable information from raw data is especially difficult considering the velocity of growing data from year to year and the fact that 80% of data is unstructured. In addition, data sources are heterogeneous (various sensors, users with different profiles, etc.) and are located in different situations or contexts. Cloud computing, which concerns large-scale interconnected systems with the main purpose of aggregation and efficient exploiting the power of widely distributed resources, represent one viable solution. Resource management and task scheduling play an essential role, in cases where one is concerned with optimized use of resources. Moreover, a recently emerging research trend focuses on the possible convergence of Big Data Analytics and High-Performance Computing. In this context, a huge research space is open for exploring resource management for Big Data processing that efficiently leverage HPC clouds or hybrid systems combining cloud platforms and HPC systems.

The goal of this special issue is to explore new directions and approaches for reasoning about advanced resource management and task scheduling methods and algorithms for Big Data platforms, and to encourage the submission of ongoing work with already important theoretical and practical results, as well as position papers and case studies of existing verification projects to highlight the art in this domain.

Topics of Interest

This special issue calls for original papers on latest research and innovations, solutions and developments on resource management for Big Data platforms. Authors are encouraged to submit complete unpublished papers in the following, but not limited to:

  • Foundational Models for Big Data
  • Cloud Computing Techniques for Big Data
  • Adaptive and Machine Learning based Scheduling Algorithms
  • Dynamic Resource Provisioning
  • Load-Balancing and Co-Allocation
  • Data-aware Scheduling
  • Big Data Persistence and Preservation
  • Self-* Techniques for Resource Management
  • Task Scheduling for Big Data Processing
  • Content Distribution Systems for Large Data
  • Big Data Storage and Retrieval
  • Convergent Big Data and HPC architectures for Big Data processing
  • Big Data Quality and Provenance Control
  • Data-intensive Computing Applications
  • Scheduling for MapReduce, Hadoop, Spark and Flink
  • Cloud Workload Profiling and Deployment Control
  • Workflow Scheduling and Scalability Analysis
  • Scheduling for Many-Task Computing
  • Quality Management and Service Level Agreement
  • Performance Evaluation

Submission

The submitted papers must be original and must not be under consideration in any other venue. This special issue is open for any submissions. The main target audience will be the papers accepted on the Workshop on Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC, http://arms-cc.hpc.pub.ro) organized in conjunction with PODC 2016 (http://www.podc.org) and also the papers accepted at the host conference.

Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Future Generation Computer Systems at http://ees.elsevier.com/fgcs/. Authors should select “SI: RM-BDP” when they reach the “Article Type” step in the submission process. All submissions will be reviewed by at least three independent reviewers. The editors will approve final decisions on accepted papers according with their quality, relevance to the special issue and originality of research.

Tentative schedule

  • Manuscript Due: December 1, 2016
  • First Decision Date: February 28, 2017
  • Revision Due:  March 30, 2017
  • Final Decision Date: May 30, 2017
  • Final Paper Due: June 30, 2017

Guest Editors

Florin Pop
University Politehnica of Bucharest, Romania (florin.pop@cs.pub.ro)

Radu Prodan
University of Innsbruck, Austria (radu@dps.uibk.ac.at)

Gabriel Antoniu
INRIA Rennes – Bretagne Atlantique Research Center and IRISA, France (gabriel.antoniu@inria.fr)