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

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 Authors are encouraged to submit complete unpublished papers in the following, but not limited to: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