Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network *** setup of programmable software-defined networking(SDN)control and elastic...
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Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network *** setup of programmable software-defined networking(SDN)control and elastic virtual computing resources within network functions virtualization(NFV)are cooperative for enhancing the applicability of intelligent edge *** offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization,this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows,link delays,and allocatable bandwidth *** partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop *** optimization problem of resource placement is tackled by a deep reinforcement learning(DRL)-based policy following the Markov decision process(MDP).The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation *** reward formulation primarily considers taskrequired computing resources and action-applied allocation *** defined policies of resource determination,the orchestration procedure is configured within each virtual network function(VNF)descriptor using topology and orchestration specification for cloud applications(TOSCA)by specifying the allocated *** simulation for the control rule installation is conducted using Mininet and Ryu SDN *** delay and task delivery/drop ratios are used as the key performance metrics.
Flood forecasting methods based on deep learning rely on a large number of observational data, and are facing serious challenges in areas with scarce data. Aiming at the problems of flood inundated range prediction in...
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Originally presented in previous work to capture the set of fundamental elements of the UML state machine specification, Common Declarative Language (CDL) provides a model that can aid in the validation and verificati...
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The subject of research. Analysis of the impact of priority data transmission in high-load multi-channel systems represented as a mass service system with request prioritization under different variations in the distr...
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Satellite edge computing (SatEC) is an emerging computing paradigm for remote and disaster-affected areas with scarce computing resources. It is crucial to make the appropriate offloading strategy to maximize quality ...
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The subject of research. Analysis of the influence of priority service in multi-channel data transmission systems characterized by limited storage capacity and high load, taking into account the non-stationary charact...
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With the rapid development of intelligent systems, Multi-Agent Systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of Multi-Agent Reinforcement Learning (M...
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Hyperspectral images (HSIs) provide rich spectral information, but acquiring high-resolution data is costly and challenging, making spectral super-resolution essential. Inspired by the near-linear efficiency of state ...
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The study investigates battery degradation under high C-rates and subzero temperatures, analyzing temperature gradients (ΔT/Δt) and differential temperature rises (ΔT) on 21700 lithium nickel cobalt aluminum oxide ...
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With the evolution of social media technology, the mode of information exchange has transitioned from a singular text format to a multifaceted blend of text, images, audio, and video. This transformation has given ris...
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