International Journal of Computer Networks And Applications,
Год журнала:
2024,
Номер
11(1), С. 1 - 1
Опубликована: Фев. 26, 2024
In
a
cloud
computing
environment,
good
resource
management
remains
major
challenge
for
its
operation.Implementing
virtual
machine
placement
(VMP)
on
physical
machines
helps
to
achieve
various
objectives,
such
as
allocation,
load
balancing,
energy
consumption,
and
quality
of
service.VMP
(virtual
placement)
in
the
is
critical,
so
it's
important
audit
implementation.It
must
take
into
account
resources
server,
including
CPU,
RAM,
storage.In
this
paper,
metaheuristic
algorithm
based
Grey
Wolf
Optimization
(GWO)
method
used
optimize
effectively
minimizing
number
active
host
servers.Experimental
results
demonstrate
effectiveness
proposed
method,
called
Virtual
Machine
Placement
(GWOVMP).The
reduces
power
consumption
by
20.99
wastage
1.80
compared
with
existing
algorithms.
IEEE Systems Journal,
Год журнала:
2020,
Номер
15(2), С. 2571 - 2582
Опубликована: Июнь 30, 2020
Cloud
computing
efficiency
greatly
depends
on
the
of
virtual
machines
(VMs)
placement
strategy
used.
However,
VM
has
remained
one
major
challenging
issues
in
cloud
mainly
because
heterogeneity
both
and
physical
(PMs),
multidimensionality
resources,
increasing
scale
data
centers
(CDCs).
An
inefficiency
a
significant
influence
quality
service
provided,
amount
energy
consumed,
running
costs
CDCs.
To
address
these
issues,
this
article,
we
propose
greedy
randomized
(GRVMP)
algorithm
large-scale
CDC
with
heterogeneous
multidimensional
resources.
GRVMP
inspires
"power
two
choices"
model
places
VMs
more
power-efficient
PMs
to
jointly
optimize
usage
resource
utilization.
The
performance
is
evaluated
using
synthetic
real-world
production
scenarios
(Amazon
EC2)
several
matrices.
results
experiment
confirm
that
optimizes
power
overall
wastage
also
show
significantly
outperforms
baseline
schemes
terms
metrics
