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Emerging opportunities and challenges for the future of reservoir computing
Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …
Modeling the Green Cloud Continuum: integrating energy considerations into Cloud–Edge models
The energy consumption of Cloud–Edge systems is becoming a critical concern
economically, environmentally, and societally; some studies suggest data centers and …
economically, environmentally, and societally; some studies suggest data centers and …
MAG-D: A multivariate attention network based approach for cloud workload forecasting
The Coronavirus pandemic and the work-from-home have drastically changed the working
style and forced us to rapidly shift towards cloud-based platforms & services for seamless …
style and forced us to rapidly shift towards cloud-based platforms & services for seamless …
Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism
The resources required to service cloud computing applications are dynamic and fluctuate
over time in response to variations in the volume of incoming requests. Proactive …
over time in response to variations in the volume of incoming requests. Proactive …
Multi-task learning for electricity price forecasting and resource management in cloud based industrial IoT systems
Cloud computing has gained immense popularity in the logistics industry. This innovative
technology optimizes computing operations by eliminating the requirement for physical …
technology optimizes computing operations by eliminating the requirement for physical …
Multivariate time series ensemble model for load prediction on hosts using anomaly detection techniques
Host load prediction is essential in computing to improve resource utilization and for
achieving service level agreements. However, due to variations in load and the inefficiency …
achieving service level agreements. However, due to variations in load and the inefficiency …
A succinct state-of-the-art survey on green cloud computing: Challenges, strategies, and future directions
Cloud computing is a method of providing various computing services, including software,
hardware, databases, data storage, and infrastructure, to the public through the Internet. The …
hardware, databases, data storage, and infrastructure, to the public through the Internet. The …
Ensemble cnn attention-based bilstm deep learning architecture for multivariate cloud workload prediction
Cloud computing has drastically changed the nature of computing in recent years. However,
despite its countless benefits, it also suffers from some major challenges including …
despite its countless benefits, it also suffers from some major challenges including …
A common feature-driven prediction model for multivariate time series data
X Yu, H Wang, J Wang, X Wang - Information Sciences, 2024 - Elsevier
Multivariate time series data contain a variety of common features that are difficult to extract,
among which the sudden irregular fluctuation trend, the trend feature of large fluctuation …
among which the sudden irregular fluctuation trend, the trend feature of large fluctuation …
A feature extraction and time war** based neural expansion architecture for cloud resource usage forecasting
Accurate resource utilization estimation is crucial for efficient resource allocation, capacity
planning, and cost optimization in cloud systems. In the past, several artificial intelligence …
planning, and cost optimization in cloud systems. In the past, several artificial intelligence …