Heliyon,
Год журнала:
2024,
Номер
10(19), С. e37912 - e37912
Опубликована: Сен. 13, 2024
The
convenience
and
cost-effectiveness
offered
by
cloud
computing
have
attracted
a
large
customer
base.
In
environment,
the
inclusion
of
concept
virtualization
requires
careful
management
resource
utilization
energy
consumption.
With
rapidly
increasing
consumer
base
data
centers,
it
faces
an
overwhelming
influx
Virtual
Machine
(VM)
requests.
technology,
mapping
these
requests
onto
actual
hardware
is
known
as
VM
placement
which
significant
area
research.
article
presents
Dragonfly
Algorithm
integrated
with
Modified
Best
Fit
Decreasing
(DA-MBFD)
proposed
to
minimize
overall
power
consumption
migration
count.
DA-MBFD
uses
MBFD
for
ranking
VMs
based
on
their
requirement,
then
Minimization
Migration
(MM)
algorithm
hotspot
detection
followed
DA
optimize
replacement
from
overutilized
hosts.
compared
few
other
existing
techniques
show
its
efficiency.
comparative
analysis
against
E-ABC,
E-MBFD,
MBFD-MM
shows
%improvement
reflecting
reduction
in
8.21
%,
8.6
6.77
violations
service
level
agreement
9.25
6.98
%-7.86
%
number
migrations
6.65
8.92
7.02
respectively.
Computers & Electrical Engineering,
Год журнала:
2024,
Номер
119, С. 109506 - 109506
Опубликована: Июль 26, 2024
Cloud
computing
has
revolutionized
the
way
businesses
and
organizations
manage
their
computational
workloads.
However,
massive
data
centers
that
support
cloud
services
consume
a
lot
of
energy,
making
energy
sustainability
critical
concern.
To
address
this
challenge,
article
introduces
an
innovative
approach
to
optimize
consumption
in
environments
through
knowledge
acquisition.
The
proposed
method
uses
Knowledge
Acquisition
version
Gray
Wolf
Optimizer
(KAGWO)
algorithm
collect
on
availability
use
renewable
within
centers,
contributing
improved
computing.
KAGWO
is
introduced
provide
systematic
for
addressing
complex
problems
by
integrating
global
optimization
principles,
enhancing
decision-making
processes
with
fewer
configuration
parameters.
This
conducts
comparative
analysis
between
Swarm
Intelligence
Approach
(KASIA)
Genetic
Algorithm
(Pittsburgh)
highlight
benefits
advantages
former.
By
comparing
performance
KAGWO,
Pittsburgh
KASIA
terms
sustainability,
study
offers
valuable
insights
into
effectiveness
knowledge-acquisition-based
algorithms
optimizing
usage
environments.
results
demonstrate
outperforms
offering
more
accurate
acquisition
capabilities,
resulting
enhanced
sustainability.
Overall,
demonstrates
substantial
improvements
ranging
from
0.53%
5.23%
over
previous
paper
baselines,
particular
significance
found
slightly
outperforming
new
small,
medium
large
scenarios.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 48022 - 48032
Опубликована: Янв. 1, 2023
In
cloud
computing
environments,
virtualization
is
used
to
share
physical
machine
(PM)
resources
among
multiple
users
by
creating
virtual
machines
(VMs).
Running
the
PM
consumes
a
large
amount
of
energy.
Additionally,
will
be
overloaded
when
demand
for
exceeds
capacity.
This
overload
on
leads
violations
Service
Level
Agreements
(SLAs).
Dynamic
VM
consolidation
techniques
use
live
migration
VMs
optimize
resource
utilization
and
minimize
energy
consumption.
However,
excessive
impacts
negatively
application
performance
due
incurred
overhead
at
runtime.
paper
presents
modified
genetic-based
(MGVMC)
strategy
that
aims
replace
in
an
online
manner
taking
into
account
consumption,
SLA
violations,
number
migrations.
The
MGVMC
utilizes
genetic
algorithm
migrate
appropriate
way
minimizes
over-utilized
under-utilized
(PMs)
as
low
possible.
was
evaluated
using
CloudSim
Plus
framework
with
workload
traces
from
PlanetLab
platform.
experimental
results
revealed
achieved
significant
improvement
migrations
compared
other
recent
approaches.
These
demonstrate
effectiveness
optimizing
environment.
