In
the
present
study,
we
propose
an
algorithm
for
mapping
virtual
machines
(VMs)
to
physical
(PMs)
in
cloud
data
centers.
The
proposed
method
models
a
dynamic
system
where
VMs
enter
and
terminate.
goal
of
is
minimize
power
consumption
PMs
network
while
preventing
service
level
agreement
violation
(SLA).
Moreover,
oversubscription
leveraged
enhance
PM
utilization.
problem
formulated
as
optimization
solved
using
heuristic
meta-heuristic
algorithm.
For
latter,
used
chemical
reaction
optimization.
addition,
convert
various
important
metrics
into
one
goal,
first,
raw
revenue
executing
calculated.
Then,
all
other
measured
parameters,
including
consumption,
migration
cost,
SLA
penalty,
are
converted
monetary
measures
obtain
net
revenue,
which
considered
goal.
simulation
results
show
that
implemented
CRO
outperforms
methods
by
significant
margin
terms
consumption.
Mathematics,
Journal Year:
2024,
Volume and Issue:
12(3), P. 468 - 468
Published: Feb. 1, 2024
The
advancement
of
cloud
computing
technologies
has
positioned
virtual
machine
(VM)
migration
as
a
critical
area
research,
essential
for
optimizing
resource
management,
bolstering
fault
tolerance,
and
ensuring
uninterrupted
service
delivery.
This
paper
offers
an
exhaustive
analysis
VM
processes
within
infrastructures,
examining
various
types,
server
load
assessment
methods,
selection
strategies,
ideal
timing,
target
determination
criteria.
We
introduce
queuing
theory-based
model
to
scrutinize
dynamics
between
servers
in
environment.
By
reinterpreting
resource-centric
mechanisms
into
task-processing
paradigm,
we
accommodate
the
stochastic
nature
demands,
characterized
by
random
task
arrivals
variable
processing
times.
is
specifically
tailored
scenarios
with
two
three
VMs.
Through
numerical
examples,
elucidate
several
performance
metrics:
blocking
probability,
average
tasks
processed
VMs,
managed
servers.
Additionally,
examine
influence
arrival
rates
duration
on
these
measures.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 106190 - 106209
Published: Jan. 1, 2023
In
the
last
decade,
users
can
access
their
applications,
data,
and
services
via
cloud
from
any
location
with
an
internet
connection.
The
scale
of
heterogeneous
environments
is
continuously
growing
due
to
development
computing-intensive
smart
devices.
A
data
center
central
processing
unit
environment,
it
made
up
hardware-oriented
machines
known
as
Physical
Machines
(PMs)
or
server
software-oriented
Virtual
(VMs).
deployment
a
huge
number
physical
servers
result
exponential
in
demand
for
has
resulted
high
energy
consumption
ineffective
resource
usage.
Efficient
utilization
minimizing
power
by
have
become
crucial
challenges.
machine
consolidation(VMC)
method
optimizing
computing
resources
consolidating
multiple
VMs
onto
reduced
PMs.
By
running
fewer
servers,
VM
consolidation
lead
reducing
efficient
utilization.
This
review
paper
presents
comprehensive
analysis
virtual
consolidation,
exploring
various
strategies,
benefits,
challenges,
future
trends
this
domain.
examining
wide
range
literature
year
2015
2023,
attempts
provide
insight
into
current
state
its
possible
effects
on
performance
sustainability
computing.
main
flaw
articles
that
authors
focused
different
assessment
metrics
while
emphasis
should
been
improving
efficiency
quality
service
systems.
Future
research
be
aimed
at
developing
multi-objective
system
emphasizes
usage
without
sacrificing
quality,
preventing
level
agreements
being
compromised.
PeerJ Computer Science,
Journal Year:
2023,
Volume and Issue:
9, P. e1675 - e1675
Published: Nov. 14, 2023
Virtual
machine
scheduling
and
resource
allocation
mechanism
in
the
process
of
dynamic
virtual
consolidation
is
a
promising
access
to
alleviate
cloud
data
centers
prominent
energy
consumption
service
level
agreement
violations
with
improvement
quality
(QoS).
In
this
article,
we
propose
an
efficient
algorithm
(AESVMP)
based
on
Analytic
Hierarchy
Process
(AHP)
for
accordance
measure.
Firstly,
take
into
consideration
three
key
criteria
including
host
power
consumption,
available
balance
ratio,
which
ratio
can
be
calculated
by
value
between
overall
three-dimensional
(CPU,
RAM,
BW)
flat
surface
(when
new
migrated
(VM)
consumed
targeted
host's
resource).
Then,
placement
decision
determined
application
multi-criteria
making
techniques
AHP
embedded
above-mentioned
criteria.
Extensive
experimental
results
CloudSim
emulator
using
10
PlanetLab
workloads
demonstrate
that
proposed
approach
reduce
center
number
migration,
violation
(SLAV),
aggregate
indicators
comsumption
(ESV)
average
51.76%,
67.4%,
67.6%
compared
cutting-edge
method
LBVMP,
validates
effectiveness.
Informatica,
Journal Year:
2024,
Volume and Issue:
48(6)
Published: Feb. 27, 2024
Cloud
computing
has
emerged
as
an
efficient
scalable
solution
for
storing
and
processing
a
large
amount
of
data.
data
centers
provide
the
resources
on
demand
to
consumers
pay-per-use
model.
However,
datacenters
is
required
support
growing
cloud
consumers.
This
should
be
handled
in
optimized
way
avoid
resource
wastage
so
that
more
can
get
benefits
centers.
Virtualization
technology
creating
virtual
version
computers
called
Virtual
Machines
(VM).
A
Machine
Placement
problem
fundamental
challenge
where
goal
determine
optimal
allocation
Physical
(PM)
within
center.
An
technique
helps
properly
place
VMs
PMs
which
significantly
optimize
number
servers,
maintenance
cost,
CPU
utilization
power
consumption.
We
present
novel
hybrid
approach
combines
Ant
Colony
Optimization
(ACO)
algorithm
Sine
Cosine
Algorithm
(SCA)
VM
placement.
Since
SCA
emerging
search
using
functions
Engineering
field,
it
been
used
explore
solutions
obtained
by
ACO
applied
exploit
space
placement
management
also
minimize
wastage.
The
result
verified
with
performance
against
other
algorithms
prove
our
proposed
outperforms
2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS),
Journal Year:
2023,
Volume and Issue:
unknown, P. 1126 - 1131
Published: May 17, 2023
Over
the
past
ten
years,
cloud
computing
has
significantly
altered
many
aspects
of
human
life
by
providing
access
to
hardware
and
software
resources
through
internet.
Businesses
or
individuals
can
use
without
setting
up
maintaining
their
IT
infrastructure.
A
data
center
is
core
computation
unit
any
environment,
it
consists
hardware-oriented
machines
termed
physical
(PM)
software-oriented
that
are
virtual
(VM).
The
fundamental
method
generating
different
from
given
infrastructure
virtualization.
expanding
as
people
more
smart
gadgets
highly
computational
require
run
efficiently.
An
enormous
amount
energy
required
a
cloud's
services
when
they
established
on
large
scale.
Resource
usage
management
must
be
carefully
managed
in
environment
accomplish
To
do
this,
should
necessary
manage
workload
dividing
equally
among
machines.
But
due
rapid
development
growing
user
requests,
cannot
divided
between
There
need
apply
machine
selection
process,
which
will
identify
under-utilized
over-utilized
PMs
based
resource
utilization.
minimize
consumption,
there
migrate
VMs
other
bring
all
neutral
state
compromising
quality
service.
This
paper
presents
challenges
future
directions
VM
migration
process
computing.
Algorithms,
Journal Year:
2024,
Volume and Issue:
17(7), P. 295 - 295
Published: July 5, 2024
Cloud
service
providers
deliver
computing
services
on
demand
using
the
Infrastructure
as
a
Service
(IaaS)
model.
In
cloud
data
center,
several
virtual
machines
(VMs)
can
be
hosted
single
physical
machine
(PM)
with
help
of
virtualization.
The
placement
(VMP)
involves
assigning
VMs
across
various
machines,
which
is
crucial
process
impacting
energy
draw
and
resource
usage
in
center.
Nonetheless,
finding
an
effective
settlement
challenging
owing
to
factors
like
hardware
heterogeneity
scalability
centers.
This
paper
proposes
efficient
algorithm
named
VMP-ER
aimed
at
optimizing
power
consumption
reducing
wastage.
Our
achieves
this
by
decreasing
number
running
it
gives
priority
energy-efficient
servers.
Additionally,
improves
utilization
thus
minimizing
wastage
ensuring
balanced
allocation.
