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:
2022,
Номер
17(1), С. 1419 - 1430
Опубликована: Июль 20, 2022
In
the
cloud
data
centers,
due
to
variable
resource
requirements
of
tenants,
designers
applied
infrastructure
as
a
service
(IaaS)
model
provide
services
for
tenants
with
allocating
in
charging.
The
application
virtualization
technology
enables
multiple
virtual
machines
(VMs)
share
resources
physical
machine
(PM).
Meanwhile,
efficiency
centers
greatly
depends
on
working
VMs.
Virtual
placement
(VMP)
plays
vital
role
minimizing
total
energy
consumption
and
wastage
(CDCs).
this
article,
we
propose
greedy
algorithm
power
(GMPR)
VMP
scheme
address
abovementioned
issues.
GMPR
prioritizes
power-efficiency
PM
reduce
number
active
PMs
minimize
consumption.
addition,
reducing
involves
first
balance
novel
hosts
VM
second
placed
VM.
Extensive
simulation
results
are
conducted
synthetic
instances
Amazon
EC2
performance
metrics
confirm
that
has
superiority
by
an
average
1.91%
16.18%
compared
cutting-edge
method.
Future Internet,
Год журнала:
2018,
Номер
10(6), С. 52 - 52
Опубликована: Июнь 13, 2018
With
the
rapid
development
of
cloud
computing,
demand
for
infrastructure
resources
in
data
centers
has
further
increased,
which
already
led
to
enormous
amounts
energy
costs.
Virtual
machine
(VM)
consolidation
as
one
important
techniques
Infrastructure
a
Service
clouds
(IaaS)
can
help
resolve
consumption
by
reducing
number
active
physical
machines
(PMs).
However,
necessity
considering
energy-efficiency
and
obligation
providing
high
quality
service
(QoS)
customers
is
trade-off,
aggressive
may
lead
performance
degradation.
Moreover,
most
existing
works
threshold-based
VM
strategy
are
mainly
focused
on
single
CPU
utilization,
although
resource
request
different
VMs
very
diverse.
This
paper
proposes
novel
self-adaptive
based
dynamic
multi-thresholds
(DMT)
PM
selection,
be
dynamically
adjusted
future
utilization
multi-dimensional
CPU,
RAM
Bandwidth.
Besides,
selection
placement
algorithm
also
improved
utilizing
each
parameter
DMT.
The
experiments
show
that
our
proposed
better
than
other
strategies,
not
only
QoS
but
less
consumption.
In
addition,
advantage
its
reduction
hosts
much
more
obvious,
especially
when
it
under
extreme
workloads.
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.
