Scientific Programming

Resource Management in Virtualized Clouds


Status
Published

Lead Editor

1University of Warwick, Coventry, UK

2St. Francis Xavier University, Antigonish, Canada

3Tsinghua University, Beijing, China

4University Politehnica of Bucharest, Bucharest, Romania


Resource Management in Virtualized Clouds

Description

Resource management is critical in achieving high performance in Cloud environments. Different from traditional parallel and distributed systems, resource management in Clouds involves managing Virtual Machines (VMs) since virtualization is a key technology in Clouds. Researchers have been developing various strategies and techniques to address the resource management issues encountered in virtualized Clouds. New strategies are designed to consolidate VMs to improve resource utilization in Clouds. Many schemes are implemented to improve running performance of VMs, such as I/O response and inter-VM communications, in virtualized environments. Monitoring and modelling techniques are developed to facilitate VM management in Cloud settings. Energy-aware VM management frameworks are proposed to save energy consumptions in Clouds. Resource management frameworks are custom-designed to optimize the application- or system-oriented performance for scientific, service, or big data applications.

This special issue aims to report the latest scientific advances and stimulate continuing research endeavors in resource management techniques for virtualized Clouds. We cordially invite researchers and practitioners to submit original research and development work.

Potential topics include, but are not limited to:

  • VM consolidation strategies
  • VM scheduling algorithms and frameworks
  • I/O optimization in VMs
  • Communication-aware VM management in Clouds
  • Energy-aware VM management in Clouds
  • Performance monitoring and modelling of VM executions in Clouds
  • Security in VM management
  • Resource management frameworks optimized for hosting scientific or service-oriented applications in Clouds
  • Optimizing resource management for processing big data applications in Clouds
  • Elastic resource allocations for large-scale applications in Clouds
Scientific Programming
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Acceptance rate7%
Submission to final decision126 days
Acceptance to publication29 days
CiteScore1.700
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