Applying Optimized Machine Learning Models for Predicting Unconfined Compressive Strength in Fine-Grained Soil
Transportation Infrastructure Geotechnology,
Год журнала:
2024,
Номер
11(4), С. 2235 - 2269
Опубликована: Фев. 8, 2024
Язык: Английский
Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil
Journal of Earth System Science,
Год журнала:
2024,
Номер
133(3)
Опубликована: Авг. 17, 2024
Язык: Английский
Heuristic Models for Optimal Host Selection
Опубликована: Янв. 1, 2025
Язык: Английский
Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach
Sustainable Operations and Computers,
Год журнала:
2024,
Номер
5, С. 141 - 155
Опубликована: Янв. 1, 2024
The
cloud
computing
paradigm
provides
services
to
users
in
an
on-demand
fashion
using
high-speed
Internet.
This
Internet-based
resources
on
a
rent
basis
without
any
fault.
Virtual
machine
resource
allocation
is
one
of
the
challenging
concerns
environment.
existing
static,
dynamic,
and
Meta-Heuristic
approaches
provide
solution
virtual
problem.
These
techniques
stuck
with
local
optimal
solution.
slow
convergence
rate
leads
locally
fails
Globally.
manuscript
proposes
hybrid
Spotted
Hyena
optimizer
artificial
neural
network,
named
SHO-ANN
technique,
assignment
presented
technique
evaluated
analyzed
performance
metrics
"Energy
Consumption
(Kwh)
(8.54%),
Host
Utilization
(24.8%),
Average
Execution
Time(ms)
(26.33%),
SLA
Violations
(1.33%),
Number
Migrations
(Counts)
(19.73%)".
spotted
hyena
used
vast
data
set
ANN
model
for
better
accuracy.
approach
globally
high
convergence.
experimental
results
exhibit
that
outperforms
IqMc,
SHO,
Genetic
real
workload
scenarios
fabricated
scenarios.
Язык: Английский
Harmonic Migration Algorithm for Virtual Machine Migration and Switching Strategy in Cloud Computing
Concurrency and Computation Practice and Experience,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 29, 2024
ABSTRACT
Nowadays,
cloud
computing
(CC)
has
been
utilized
broadly
owing
to
the
services
it
provides
which
can
be
received
from
any
location
at
time
on
basis
of
customer's
requirements.
A
huge
amount
data
transmission
is
made
both
user
host
as
well
customer
in
environment,
but
here
placing
virtual
machine
(VM)
a
suitable
and
transferring
challenging
task.
In
this
research,
harmonic
migration
algorithm
(HMA)
developed
by
combining
(MA)
analysis
(HA)
for
migrating
VM
an
overloaded
under‐loaded
physical
(PM)
enabling
or
disabling
through
switching
strategies
CC.
The
tasks
are
allocated
corresponding
round‐robin
(RR)
manner
subsequently,
load
predicted
gated
recurrent
unit
(GRU).
HMA
technique
migrates
when
higher
than
value
threshold
also,
enables
disables
necessary.
Thus,
performance
improved
over
other
previous
schemes
100,
200,
300,
400
varying
iterations.
Therefore,
load,
makespan,
resource
utilization
0.148,
0.327
s,
0.482%
task
100
iteration
100.
Язык: Английский