Transactions on Emerging Telecommunications Technologies,
Год журнала:
2025,
Номер
36(3)
Опубликована: Март 1, 2025
ABSTRACT
Workload
prediction
is
the
necessary
factor
in
cloud
data
center
for
maintaining
elasticity
and
scalability
of
resources.
However,
accuracy
workload
very
low,
because
redundancy,
noise,
low
center.
In
this
manuscript,
Prediction
Cloud
Data
Centers
using
Complex‐Valued
Spatio‐Temporal
Graph
Convolutional
Neural
Network
Optimized
with
Gazelle
Optimization
Algorithm
(CVSTGCN‐WLP‐CDC)
proposed.
Initially,
input
collected
from
two
standard
datasets
such
as
NASA
Saskatchewan
HTTP
traces
dataset.
Then,
preprocessing
Multi‐Window
Savitzky–Golay
Filter
(MWSGF)
used
to
remove
noise
redundant
data.
The
preprocessed
fed
CVSTGCN
a
dynamic
environment.
work,
proposed
Approach
(GOA)
enhance
weight
bias
parameters.
CVSTGCN‐WLP‐CDC
technique
executed
efficacy
based
on
structure
evaluated
several
performances
metrics
accuracy,
recall,
precision,
energy
consumption
correlation
coefficient,
sum
index
(SEI),
root
mean
square
error
(RMSE),
squared
(MPE),
percentage
(PER).
provides
23.32%,
28.53%
24.65%
higher
accuracy;
22.34%,
25.62%,
22.84%
lower
when
comparing
existing
methods
Artificial
Intelligence
augmented
evolutionary
approach
espoused
centres
architecture
(TCNN‐CDC‐WLP),
Performance
analysis
machine
learning
centered
techniques
(PA‐BPNN‐CWPC),
Machine
effectual
utilization
centers
(ARNN‐EU‐CDC)
respectively.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 7, 2025
In
order
to
make
up
for
the
shortcomings
of
original
dung
beetle
optimization
algorithm,
such
as
low
population
diversity,
insufficient
global
exploration
ability,
being
easy
fall
into
local
and
unsatisfactory
convergence
accuracy,
etc.
An
improved
algorithm
using
hybrid
multi-
strategy
is
proposed.
Firstly,
cubic
chaotic
mapping
approach
used
initialize
improve
expand
search
range
solution
space,
enhance
ability.
Secondly,
cooperative
utilized
strength
communication
between
individual
beetles
groups
in
foraging
stage
space
Thirdly,
T-distribution
mutation
differential
evolutionary
variation
strategies
are
introduced
provide
perturbation
diversity
avoid
falling
optimization.
Fourthly,
proposed
algorithm(named
SSTDBO)
compared
with
other
algorithms,
including
GODBO,
QHDBO,
DBO,
KOA,
NOA,
WOA
HHO,
by
29
benchmark
test
functions
CEC2017.
The
results
show
that
has
stronger
robustness
algorithm's
performance
substantially
enhanced.
Finally,
applied
solve
real-world
robot
path
planning
engineering
cases,
demonstrate
its
effectiveness
dealing
real
which
further
verified
how
noteworthy
enhanced
strategy's
efficacy
superiority
addressing
cases.
Transactions on Emerging Telecommunications Technologies,
Год журнала:
2025,
Номер
36(3)
Опубликована: Март 1, 2025
ABSTRACT
Workload
prediction
is
the
necessary
factor
in
cloud
data
center
for
maintaining
elasticity
and
scalability
of
resources.
However,
accuracy
workload
very
low,
because
redundancy,
noise,
low
center.
In
this
manuscript,
Prediction
Cloud
Data
Centers
using
Complex‐Valued
Spatio‐Temporal
Graph
Convolutional
Neural
Network
Optimized
with
Gazelle
Optimization
Algorithm
(CVSTGCN‐WLP‐CDC)
proposed.
Initially,
input
collected
from
two
standard
datasets
such
as
NASA
Saskatchewan
HTTP
traces
dataset.
Then,
preprocessing
Multi‐Window
Savitzky–Golay
Filter
(MWSGF)
used
to
remove
noise
redundant
data.
The
preprocessed
fed
CVSTGCN
a
dynamic
environment.
work,
proposed
Approach
(GOA)
enhance
weight
bias
parameters.
CVSTGCN‐WLP‐CDC
technique
executed
efficacy
based
on
structure
evaluated
several
performances
metrics
accuracy,
recall,
precision,
energy
consumption
correlation
coefficient,
sum
index
(SEI),
root
mean
square
error
(RMSE),
squared
(MPE),
percentage
(PER).
provides
23.32%,
28.53%
24.65%
higher
accuracy;
22.34%,
25.62%,
22.84%
lower
when
comparing
existing
methods
Artificial
Intelligence
augmented
evolutionary
approach
espoused
centres
architecture
(TCNN‐CDC‐WLP),
Performance
analysis
machine
learning
centered
techniques
(PA‐BPNN‐CWPC),
Machine
effectual
utilization
centers
(ARNN‐EU‐CDC)
respectively.
