Published: July 7, 2024
Language: Английский
Journal of Cloud Computing Advances Systems and Applications, Journal Year: 2023, Volume and Issue: 12(1)
Published: June 26, 2023
Abstract
Efficient
resource
management
approaches
have
become
a
fundamental
challenge
for
distributed
systems,
especially
dynamic
environment
systems
such
as
cloud
computing
data
centers.
These
aim
at
load-balancing
or
minimizing
power
consumption.
Due
to
the
highly
nature
of
workloads,
traditional
time
series
and
machine
learning
models
fail
achieve
accurate
predictions.
In
this
paper,
we
propose
novel
hybrid
VTGAN
models.
Our
proposed
not
only
predicting
future
workloads
but
also
workload
trend
(i.e.,
upward
downward
direction
workload).
Trend
classification
could
be
less
complex
during
decision-making
process
in
approaches.
Also,
study
effect
changing
sliding
window
size
number
prediction
steps.
addition,
investigate
impact
enhancing
features
used
training
using
technical
indicators,
Fourier
transforms,
wavelet
transforms.
We
validate
our
real
dataset.
results
show
that
outperform
deep
models,
LSTM/GRU
CNN-LSTM/GRU,
concerning
classification.
model
records
an
accuracy
ranging
from
$$95.4\%$$
Language: Английский
Citations
8
Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(2), P. 101924 - 101924
Published: Jan. 21, 2024
Edge computing has gained widespread adoption for time-sensitive applications by offloading a portion of IoT system workloads from the cloud to edge nodes. However, limited resources devices hinder service deployment, making auto-scaling crucial improving resource utilization in response dynamic workloads. Recent solutions aim make proactive predicting future and overcoming limitations reactive approaches. These often rely on time-series data analysis machine learning techniques, especially Long Short-Term Memory (LSTM), thanks its accuracy prediction speed. existing suffer oscillation issues, even when using cooling-down strategy. Consequently, efficiency depends model degree scaling actions. This paper proposes novel approach improve deal with issues. Our involves an automatic featurization phase that extracts features workload data, prediction's accuracy. extracted also serve as grid controlling generated experimental results demonstrate effectiveness our accuracy, mitigating phenomena, enhancing overall performance.
Language: Английский
Citations
2
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 55248 - 55263
Published: Jan. 1, 2024
Cloud computing has become the cornerstone of modern technology, propelling industries to unprecedented heights with its remarkable and recent advances. However, fundamental challenge for cloud service providers is real-time workload prediction management optimal resource allocation. workloads are characterized by their heterogeneous, unpredictable, fluctuating nature, making this task even more challenging. As a result achievements deep learning (DL) algorithms across diverse fields, scholars have begun embrace approach addressing such challenges. It defacto standard prediction. Unfortunately, DL been widely recognized vulnerability adversarial examples, which poses significant DL-based forecasting models. In study, we utilize established white-box attack generation methods from field computer vision construct examples four cutting-edge regression models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Unit (GRU), 1D Convolutional (1D-CNN) attention-based We evaluate our study three benchmark datasets: Google trace, Alibaba Bitbrain. The findings analysis unequivocally indicate that models highly vulnerable attacks. To best knowledge, first conduct systematic research exploring in data center, highlighting inherent hazards both security cost-effectiveness centers. By raising awareness these vulnerabilities, advocate urgent development robust defensive mechanisms enhance constantly evolving technical landscape.
Language: Английский
Citations
2
IEEE Transactions on Cloud Computing, Journal Year: 2024, Volume and Issue: 12(2), P. 789 - 799
Published: April 1, 2024
Forecasting workloads and responding promptly with resource scaling migration is critical to optimizing operations enhancing management in cloud environments. However, the diverse dynamic nature of devices within environments complicates workload forecasting. These challenges often lead service level agreement violations or inefficient usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload (E-LCWF) framework designed specifically for efficient these heterogeneous The E-LCWF processes individual as multivariate time series enhances model performance through anomaly detection handling. Additionally, employs error-based ensemble approach, using transformer-based models Time Series (LTSF) linear models, each which has demonstrated exceptional LTSF. Experimental results obtained virtual machine data from real-world information systems manufacturing execution show that outperforms state-of-the-art forecasting accuracy.
Language: Английский
Citations
2
Tsinghua Science & Technology, Journal Year: 2024, Volume and Issue: 30(1), P. 34 - 54
Published: Sept. 11, 2024
Language: Английский
Citations
2
Published: May 26, 2023
Language: Английский
Citations
4
Cluster Computing, Journal Year: 2024, Volume and Issue: 27(4), P. 4963 - 4982
Published: Jan. 10, 2024
Language: Английский
Citations
1
Computing, Journal Year: 2024, Volume and Issue: 106(12), P. 3905 - 3944
Published: Aug. 25, 2024
Language: Английский
Citations
1
Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 56 - 68
Published: Nov. 15, 2024
Language: Английский
Citations
1
Lecture notes in networks and systems, Journal Year: 2023, Volume and Issue: unknown, P. 667 - 682
Published: Nov. 27, 2023
Language: Английский
Citations
2