Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions
With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer
dynamic, reliable and elastic computing services. Efficient scheduling of resources or …
dynamic, reliable and elastic computing services. Efficient scheduling of resources or …
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 …
Computation offloading in blockchain-enabled MCS systems: A scalable deep reinforcement learning approach
Abstract In Mobile Crowdsensing (MCS) systems, cloud service providers (CSPs) pay for
and analyze the sensing data collected by mobile devices (MDs) to enhance the Quality-of …
and analyze the sensing data collected by mobile devices (MDs) to enhance the Quality-of …
Intelligent decision-making of load balancing using deep reinforcement learning and parallel PSO in cloud environment
Machine learning and parallel processing are extremely commonly used to enhance
computing power to induce knowledge from an outsized volume of data. To deal with the …
computing power to induce knowledge from an outsized volume of data. To deal with the …
Task offloading in hybrid-decision-based multi-cloud computing network: a cooperative multi-agent deep reinforcement learning
J Chen, P Chen, X Niu, Z Wu, L Xiong, C Shi - Journal of Cloud Computing, 2022 - Springer
Multi-cloud computing is becoming a promising paradigm to provide abundant computation
resources for Internet-of-Things (IoT) devices. For a multi-device multi-cloud network, the …
resources for Internet-of-Things (IoT) devices. For a multi-device multi-cloud network, the …
Profit-aware cooperative offloading in uav-enabled mec systems using lightweight deep reinforcement learning
In mobile edge computing (MEC) systems, unmanned aerial vehicles (UAVs) facilitate edge
service providers (ESPs) offering flexible resource provisioning with broader communication …
service providers (ESPs) offering flexible resource provisioning with broader communication …
Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review
H Hou, SNA Jawaddi, A Ismail - Future Generation Computer Systems, 2023 - Elsevier
The expanding scale of cloud data centers and the diversification of user services have led
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
Abstractive text summarization: State of the art, challenges, and improvements
Specifically focusing on the landscape of abstractive text summarization, as opposed to
extractive techniques, this survey presents a comprehensive overview, delving into state-of …
extractive techniques, this survey presents a comprehensive overview, delving into state-of …
Load balancing for multiedge collaboration in wireless metropolitan area networks: A two-stage decision-making approach
Mobile edge computing (MEC) relieves the latency and energy consumption of mobile
applications by offloading computation-intensive tasks to nearby edges. In wireless …
applications by offloading computation-intensive tasks to nearby edges. In wireless …