High Performance Scheduling for Heterogeneous Distributed Systems

in Special Issue   Posted on August 28, 2015 

Information for the Special Issue

Special Issue Call for Papers:

Scope and Objective

Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. Large-scale interconnected systems aim to aggregate and efficient exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed in order to increase the performance of such systems.

The goal of this special issue is to explore new directions and approaches for reasoning about advanced scheduling methods and algorithms for HDS, 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 High Performance Scheduling for HDS. Authors are solicited to submit complete unpublished papers in the following, but not limited to:

Foundational Models for Task Scheduling and Resource Management in HDS
Distributed Scheduling Algorithms
Adaptive and Machine Learning based Scheduling Algorithms
Dynamic Resource Provisioning
Load-Balancing and Co-Allocation
Data-aware Scheduling
Self-* Techniques for High Performance Scheduling
Scheduling in Big Data Platforms
Content Distribution Systems for Large Data
Data-intensive Computing Applications
Scheduling for MapReduce and Hadoop
High-throughput Computing (HTC) Applications
Cloud Workload Profiling and Deployment Control
Workflow Scheduling and Scalability Analysis
Scheduling for Many-Task Computing
Cloud Resource Virtualization and Composition
Task Offloading and Scheduling Techniques for Mobile Cloud Computing
Resource Management for High Performance Cloud Computing
Scheduling for Green Computing
Quality Management and Service Level Agreement (SLA)
Reliability and Fault Tolerance of HDS
Performance Evaluation


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 2015 (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: HPS-HDS” 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 innovation.

Tentative schedule

Manuscript Due: December 1, 2015
First Decision Date: Feb 15, 2016
Revision Due: March 30, 2016
Final Decision Date: May 30, 2016
Final Paper Due: June 30, 2016

Guest Editors

Florin Pop
University Politehnica of Bucharest, Romania ([email protected])
Alexandru Iosup
Delft University of Technology, The Netherlands ([email protected])
Radu Prodan
University of Innsbruck, Austria ([email protected])

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