A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Multi-Agent based Architecture for Dynamic VM Consolidation in Cloud Data Centers
Tekijät: Fahimeh Farahnakian, Tapio Pahikkala, Pasi Liljeberg, Juha Plosila, Hannu Tenhunen
Konferenssin vakiintunut nimi: Euromicro conference on software engineering and advanced applications
Julkaisuvuosi: 2014
Kokoomateoksen nimi: Software Engineering and Advanced Applications (SEAA), 2014 40th EUROMICRO Conference on
Aloitussivu: 111
Lopetussivu: 118
Sivujen määrä: 8
ISBN: 978-1-4799-5794-1
ISSN: 1089-6503
DOI: https://doi.org/10.1109/SEAA.2014.56
Tiivistelmä
As the scale of cloud data centers becomes larger
and larger, the energy consumption of data centers also grows
rapidly. Dynamic consolidation of Virtual Machines (VMs)
presents a significant opportunity to save energy by turning off
idle or under-utilized Physical Machines (PMs) in data centers.
In this paper, we present a multi-agent based architecture for
performing dynamic VM consolidation task. The architecture
uses a local agent in each PM to decide when a PM becomes
overloaded using reinforcement learning approach. Moreover, a
global agent is proposed as a supervisor to dynamically optimize
the VM placement based on the local agents’ decisions. Therefore,
agents cooperate together to minimize the number of active PMs
according to the current resource requirements. Experimental
results on the real workload traces from more than a thousand
PlanetLab virtual machines show that the proposed architecture
can reduce the energy consumption and maintains the required
performance level in a large-scale data center.
As the scale of cloud data centers becomes larger
and larger, the energy consumption of data centers also grows
rapidly. Dynamic consolidation of Virtual Machines (VMs)
presents a significant opportunity to save energy by turning off
idle or under-utilized Physical Machines (PMs) in data centers.
In this paper, we present a multi-agent based architecture for
performing dynamic VM consolidation task. The architecture
uses a local agent in each PM to decide when a PM becomes
overloaded using reinforcement learning approach. Moreover, a
global agent is proposed as a supervisor to dynamically optimize
the VM placement based on the local agents’ decisions. Therefore,
agents cooperate together to minimize the number of active PMs
according to the current resource requirements. Experimental
results on the real workload traces from more than a thousand
PlanetLab virtual machines show that the proposed architecture
can reduce the energy consumption and maintains the required
performance level in a large-scale data center.
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