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Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing

Fahimeh Farahnakian, Tapio Pahikkala, Pasi Liljeberg, Juha Plosila, Hannu Tenhunen, Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing. In: da Silva Dilma (Ed.), IEEE, 1–1, IEEE, 2015.

Abstract:

Dynamic Virtual Machine (VM) consolidation is one
of the most promising solutions to reduce energy consumption
and improve resource utilization in data centers. Since VM
consolidation problem is strictly NP-hard, many heuristic algorithms
have been proposed to tackle the problem. However,
most of the existing works deal only with minimizing the
number of hosts based on their current resource utilization and
these works do not explore the future resource requirements.
Therefore, unnecessary VM migrations are generated and the
rate of Service Level Agreement (SLA) violations are increased
in data centers. To address this problem, our VM consolidation
method which is formulated as a bin-packing problem considers
both the current and future utilization of resources. The future
utilization of resources is accurately predicted using a k-nearest
neighbor regression based model. In this paper, we investigate
the effectiveness of VM and host resource utilization predictions
in the VM consolidation task using real workload traces. The
experimental results show that our approach provides substantial
improvement over other heuristic algorithms in reducing energy
consumption, number of VM migrations and number of SLA
violations.

BibTeX entry:

@INPROCEEDINGS{inpFaPaLiPlTe15a,
  title = {Utilization Prediction Aware VM Consolidation Approach for Green Cloud Computing},
  booktitle = {IEEE},
  author = {Farahnakian, Fahimeh and Pahikkala, Tapio and Liljeberg, Pasi and Plosila, Juha and Tenhunen, Hannu},
  editor = {Dilma, da Silva},
  publisher = {IEEE},
  pages = {1–1},
  year = {2015},
}

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

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