Where academic tradition
meets the exciting future

You are here: TUCS > PUBLICATIONS > Publication Search > Self-Adaptive Resource Managem...

Self-Adaptive Resource Management System in IaaS Clouds

Fahimeh Farahnakian, Rami Bahsoon, Pasi Liljeberg, Tapio Pahikkala, Self-Adaptive Resource Management System in IaaS Clouds. In: Carl Kesselman (Ed.), IEEE CLOUD, 381–388, IEEE Computer Society Conference Publishing Services, 2016.

Abstract:

Resource management in cloud infrastructures is
one of the most challenging problems due to the heterogeneity of
resources, variability of the workload and scale of data centers.
Efficient management of physical and virtual resources can be
achieved considering performance requirements of hosted applications
and infrastructure costs. In this paper, we present a selfadaptive
resource management system based on a hierarchical
multi-agent based architecture. The system uses novel adaptive
utilization threshold mechanism and benefits from reinforcement
learning technique to dynamically adjust CPU and memory
thresholds for each Physical Machine (PM). It periodically runs a
Virtual Machine (VM) placement optimization algorithm to keep
the total resource utilization of each PM within given thresholds
for improving Service Level Agreement (SLA) compliance.
Moreover, the algorithm consolidates VMs into the minimum
number of active PMs in order to reduce the energy consumption.
Experimental results on real workload traces show that our
recourse management system provides substantial improvement
over other approaches in terms of performance requirements,
energy consumption and the number of VM migrations.

Files:

Abstract in PDF-format

Full publication in PDF-format

BibTeX entry:

@INPROCEEDINGS{inpFaBaLiPa16b,
  title = {Self-Adaptive Resource Management System in IaaS Clouds},
  booktitle = {IEEE CLOUD},
  author = {Farahnakian, Fahimeh and Bahsoon, Rami and Liljeberg, Pasi and Pahikkala, Tapio},
  editor = {Kesselman, Carl},
  publisher = {IEEE Computer Society Conference Publishing Services},
  pages = {381–388},
  year = {2016},
  keywords = {Resource management, VM consolidation, reinforcement learning, energy-efficiency, SLA, green computing},
}

Belongs to TUCS Research Unit(s): Embedded Computer and Electronic Systems (ECES)

Edit publication