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Performance analysis of machine learning centered workload prediction models for cloud
The precise estimation of resource usage is a complex and challenging issue due to the
high variability and dimensionality of heterogeneous service types and dynamic workloads …
high variability and dimensionality of heterogeneous service types and dynamic workloads …
esDNN: deep neural network based multivariate workload prediction in cloud computing environments
Cloud computing has been regarded as a successful paradigm for IT industry by providing
benefits for both service providers and customers. In spite of the advantages, cloud …
benefits for both service providers and customers. In spite of the advantages, cloud …
AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach
Currently, researchers have motivated a vision of 6G for empowering the new generation of
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
Resource allocation with workload-time windows for cloud-based software services: a deep reinforcement learning approach
As the workloads and service requests in cloud computing environments change constantly,
cloud-based software services need to adaptively allocate resources for ensuring the Quality …
cloud-based software services need to adaptively allocate resources for ensuring the Quality …
Multivariate resource usage prediction with frequency-enhanced and attention-assisted transformer in cloud computing systems
Resource usage prediction in cloud data centers is critically important. It can improve
providers' service quality and avoid resource wastage and insufficiency. However, the time …
providers' service quality and avoid resource wastage and insufficiency. However, the time …
Arima-based and multiapplication workload prediction with wavelet decomposition and savitzky–golay filter in clouds
Current cloud data centers (CDCs) provide highly scalable, flexible, and cost-effective
services to meet the performance needs of emerging applications. It is critical for CDC …
services to meet the performance needs of emerging applications. It is critical for CDC …
Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives
B Feng, Z Ding - Tsinghua Science and Technology, 2024 - ieeexplore.ieee.org
Workload prediction is critical in enabling proactive resource management of cloud
applications. Accurate workload prediction is valuable for cloud users and providers as it …
applications. Accurate workload prediction is valuable for cloud users and providers as it …
A multiple controlled toffoli driven adaptive quantum neural network model for dynamic workload prediction in cloud environments
The key challenges in cloud computing encompass dynamic resource scaling, load
balancing, and power consumption. Accurate workload prediction is identified as a crucial …
balancing, and power consumption. Accurate workload prediction is identified as a crucial …
FAST: A forecasting model with adaptive sliding window and time locality integration for dynamic cloud workloads
B Feng, Z Ding, C Jiang - IEEE Transactions on Services …, 2022 - ieeexplore.ieee.org
The workload predictor has attracted attention as a key component of the proactive service
operation management framework. However, the request and resource workloads of cloud …
operation management framework. However, the request and resource workloads of cloud …
[HTML][HTML] Workflow performance prediction based on graph structure aware deep attention neural network
With the rapid growth of cloud computing, efficient operational optimization and resource
scheduling of complex cloud business processes rely on real-time and accurate …
scheduling of complex cloud business processes rely on real-time and accurate …