In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Efficient task and workflow scheduling are very important for improving resource
management and reducing power consumption in cloud computing data centers (DCs) …

Federated learning (FL) is a promising paradigm to enable collaborative model training with
decentralized data. However, the training process of Large Language Models (LLMs) …

Nowadays, we live an unprecedented evolution in cloud computing technology that
coincides with the development of the vast amount of complex interdependent data which …

Deep neural networks (DNNs) exploit many layers and a large number of parameters to
achieve excellent performance. The training process of DNN models generally handles …

In recent years, more and more large-scale data processing and computing workflow
applications run on heterogeneous clouds. Such cloud applications with precedence …

Cloud computing is a distributed computing paradigm, that provides infrastructure and
services to the users using the pay-as-you-use billing model. With the increasing demands …

The search algorithm based on symbiotic organisms' interactions is a relatively recent bio-
inspired algorithm of the swarm intelligence field for solving numerical optimization …

Electricity prices differ during different time periods and change from place to place. Cloud
workflow applications often require geo-distributed data which is transmitted among …

Cloud is a common distributed environment to share strong and available resources to
increase the efficiency of complex and heavy calculations. In return for the cost paid by cloud …