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.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 107480 - 107495
Published: Jan. 1, 2023
Due
to
the
rapid
utilization
of
cloud
services,
energy
consumption
data
centres
is
increasing
dramatically.
These
services
are
provided
by
Virtual
Machines
(VMs)
through
center.
Therefore,
energy-aware
VMs
allocation
and
migration
essential
tasks
in
environment.
This
paper
proposes
a
Branch-and-Price
based
energy-efficient
algorithm
Multi-Dimensional
Machine
Migration
(MDVMM)
at
The
reduces
wastage
resources
selecting
optimal
number
PMs
proposed
MDVMM
saves
avoids
Service
Level
Agreement
(SLA)
violation
performing
an
migrations.
experimental
results
demonstrate
that
our
with
algorithms
more
than
31%
improves
21.7%
average
resource
over
existing
state-of-the-art
techniques
95%
confidence
interval.
performance
approaches
outperforms
terms
SLA
violation,
migration,
Energy
Violation
(ESV)
combined
metrics
algorithms.
An
increasingly
important
component
in
the
development
of
Cloud
Computing,
an
Internet-based
technology,
is
optimization
its
resources.
To
make
most
available
resources,
cloud
data
centre
models
need
a
resource
management
strategy.
The
Bin-Packing
issue
combinatorial
that
may
be
used
to
efficiently
assign
virtual
machines
physical
machines.
In
this
study,
we
present
two-stage
approach
for
managing
and
allocating
resources
effectively.
first
step,
propose
Load
Balanced
Multi-Dimensional
(LBMBP)
heuristics
(VMs)
(PMs
or
hosts)
by
taking
into
account
all
at
their
disposal.
As
indicated
second
stage,
technique
identify
overload
load
balance
hosts
based
on
anomalies
necessary
VM
migration.
CloudSim
Plus
Simulator
simulation
results
were
demonstrate
planned
work,
it
was
found
number
operational
PMs
reduced.
Reduced
energy
use
emigration
rates
due
more
efficient
Intelligent Decision Technologies,
Journal Year:
2023,
Volume and Issue:
17(4), P. 983 - 1006
Published: Nov. 14, 2023
The
virtualization
of
hardware
resources
like
network,
memory
and
storage
are
included
in
the
core
cloud
computing
provided
with
help
Virtual
Machines
(VM).
issues
based
on
reliability
security
reside
its
acceptance
environment
during
migration
VMs.
VM
highly
enhanced
manageability,
performance,
fault
tolerance
systems.
Here,
a
set
tasks
submitted
by
various
users
arranged
virtual
platform
using
Energy
efficiency
is
effectively
attained
loadbalancing
strategy
it
critical
issue
environment.
During
VMs,
providing
high
very
important
task
To
resolve
such
challenges,
an
effective
method
proposed
optimal
key-based
encryption
process.
main
objective
this
research
work
to
perform
derive
multi-objective
constraints
hybrid
heuristic
improvement.
achieved
algorithm
as
Improved
Binary
Battle
Royale
Moth-flame
Optimization
(IBinBRMO).
It
can
also
be
used
functions
some
resource
utilization,
active
servers,
makespan,
energy
consumption,
etc.
After
migration,
data
transmission
should
take
place
securely
between
source
destination.
secure
data,
HybridHomophorphic
Advanced
Encryption
Standard(HH-AES)
Algorithm,
where
IBinBRMO
optimizes
key.
optimizing
keys,
transformed
along
parameters
includingthe
degree
modification,
hiding
failure
rate
information
preservation
rate.
Thus,
effectiveness
guaranteed
analyzed
other
classical
models.
Hence,
results
illustrate
that
attains
better
performance.
2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT),
Journal Year:
2023,
Volume and Issue:
unknown
Published: July 6, 2023
Service
Level
Agreements
(SLAs)
are
essential
for
seamless
cloud
operations
but
can
get
violated
due
to
misconfigurations
and
network
faults.
Existing
SLA
violation
identification
mechanisms
rely
on
static
reconfiguration,
which
limits
their
scalability
under
high-speed
scenarios.
Additionally,
most
of
these
models
either
highly
complex
or
exhibit
low
efficiency
real-time
To
address
challenges,
a
novel
framework
is
proposed
in
this
research
paper.
The
uses
iterative
learning
via
customized
1D
Convolutional
Neural
Network
(CNN)
Q-Learning
identify
violations
recommend
mitigation
strategies
incremental
operations.
model
able
learn
from
previous
responses
improve
its
performance
incrementally,
making
it
useful
wide
variety
use
cases.
reduces
by
8.3%,
improves
deadline
hit
ratio
1.2%,
scheduling
delay
4.5%
traffic
scenarios
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.
