A Novel Physical Machine Overload Detection Algorithm
Combined with Quiescing for Dynamic Virtual Machine Consolidation in Cloud Data
Centers
Loiy Alsbatin1, Gürcü Öz1, and Ali
Ulusoy2
1Department
of Computer Engineering, Eastern Mediterranean University, North Cyprus via
Mersin 10 Turkey
2Department
of Information Technology, Eastern Mediterranean University, North Cyprus via
Mersin 10 Turkey
Abstract: Further growth of
computing performance has been started to be limited due to increasing energy
consumption of cloud data centers. Therefore, it is important to pay attention
to the resource management. Dynamic virtual machines consolidation is a
successful approach to improve the utilization of resources and energy
efficiency in cloud environments. Consequently, optimizing the online
energy-performance trade off directly influences Quality of Service (QoS). In
this paper, a novel approach known as Percentage of Overload Time Fraction
Threshold (POTFT) is proposed that decides to migrate a Virtual Machine (VM) if
the current Overload Time Fraction (OTF) value of Physical Machine (PM) exceeds
the defined percentage of maximum allowed OTF value to avoid exceeding the
maximum allowed resulting OTF value after a decision of VM migration or during
VM migration. The proposed POTFT algorithm is also combined with VM quiescing
to maximize the time until migration, while meeting QoS goal. A number of
benchmark PM overload detection algorithms is implemented using different
parameters to compare with POTFT with and without VM quiescing. We evaluate the
algorithms through simulations with real world workload traces and results show
that the proposed approaches outperform the benchmark PM overload detection
algorithms. The results also show that proposed approaches lead to better time
until migration by keeping average resulting OTF values less than allowed
values. Moreover, POTFT algorithm with VM quiescing is able to minimize number
of migrations according to QoS requirements and meet OTF constraint with a few
quiescings.
Keywords: Distributed systems, cloud computing,
dynamic consolidation, overload detection and energy efficiency.