A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
In-Network Decision Making Intelligence for Task Allocation in Edge Computing
2018
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)
Preserved Fulltext
We contribute with a distributed methodology that pushes the task allocation decision at the network edge by intelligently scheduling and distributing analytics tasks among nodes. ...
We comprehensively evaluate our methodology demonstrating its applicability in edge computing environments. ...
A task stream T i at node n i yields an appropriate decision making for task allocation. ...
doi:10.1109/ictai.2018.00104
dblp:conf/ictai/KolomvatsosA18a
fatcat:jbmm5ti5rzat7bjdsr5j4u4xny
AI-driven resource management strategies for cloud computing systems, services, and applications
2024
World Journal of Advanced Engineering Technology and Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
These strategies include automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management ...
We also present case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container ...
When a user submits one or more artificial intelligence tasks, computing resources for the tasks are allocated through the cloud portal system. ...
doi:10.30574/wjaets.2024.11.2.0137
fatcat:vtq4f4uvsrdqvepcopee4ltxsi
Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory ...
Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. ...
AI is a promising approach for making vehicle networks intelligent. RL is a powerful tool in ML. ...
doi:10.1109/access.2020.2983609
fatcat:b45abdrxbracnbfpvtvtu5uxui
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Thus, we first propose an intelligent ultra-dense edge computing (I-UDEC) framework, which integrates blockchain and Artificial Intelligence (AI) into 5G ultra-dense edge computing networks. ...
(e.g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes. ...
At last, the controller makes i) joint computation offloading and resource allocation decisions for the edge servers and EDs in a fast timescale and ii) service caching placement strategies for the edge ...
arXiv:2009.10601v1
fatcat:wxika4igsjg65pwu64ted23aqa
Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. ...
Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G. ...
Hence, there is a need for optimal decision making on both networks and UEs' computational tasks/data. ...
arXiv:2212.13141v1
fatcat:5ftcjml6k5cjvhbsv3a46zxvni
Collaborative Edge Computing and Caching with Deep Reinforcement Learning Decision Agents
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Specifically, due to the complex resource allocation problem at the edge, we used DDQN as a decision agent, which makes the edge have certain adaptation and cooperation. ...
[17] proposed a computing resource allocation strategy based on deep reinforcement learning for URLLC edge computing networks with multiple users. Wang et al. ...
It needs to train for a while to make better decisions. ...
doi:10.1109/access.2020.3007002
fatcat:eetpyov3rjhkxgovhgw2qxfkv4
Improved Multimedia Object Processing for the Internet of Vehicles
2022
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The combination of edge computing and deep learning helps make intelligent edge devices that can make several conditional decisions using comparatively secured and fast machine learning algorithms. ...
The corresponding comprehensive network combines cooperative multimedia data processing, Internet of Things (IoT) fact handling, validation, computation, precise detection, and decision making. ...
Similarly, other cars have their intelligent edge cluster for making decisions in real time. ...
doi:10.3390/s22114133
pmid:35684754
pmcid:PMC9185502
fatcat:v2656me6tnesrjyxcfmszvlhxa
The Framework of 6G Self-Evolving Networks and the Decision-Making Scheme for Massive IoT
2021
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Then, we introduce the autonomous decision-making methods and analyze the characteristics of the different methods and mechanisms for 6G networks. ...
In this paper, we first propose the framework of the 6G self-evolving networks, in which the autonomous decision-making is one of the vital parts. ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/app11199353
fatcat:fijsb2tfgjajbadqndyl5e2duy
Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, a deep reinforcement learning algorithm is proposed to solve the complex computation offloading problem for the heterogeneous Edge Computing Server(ECS) collaborative computing. ...
Considering multi-task, the heterogeneity of edge subnet and mobility of edge devices, the proposed algorithm can learn the network environment and generate the computation offloading decision to minimize ...
Reference [35] used the deep reinforcement learning method to make data migration decisions for multi-access edge computing in a dynamic network environment. ...
doi:10.1109/access.2020.2991773
fatcat:6nb6dfjjjbaudpxvdq4v7jpdry
Deterministic Computing Power Networking: Architecture, Technologies and Prospects
[article]
2024
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
In this article, we firstly introduce the research advance of computing power networking. And then the motivations and scenarios of Det-CPN are analyzed. ...
With the development of new Internet services such as computation-intensive and delay-sensitive tasks, the traditional "Best Effort" network transmission mode has been greatly challenged. ...
Providing end-to-end transmission and computing determinacy for computation-intensive and time-sensitive applications is of great significance. ...
arXiv:2401.17812v1
fatcat:elgtecnp3jentbudkfuaalifra
DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
As a distributed computing paradigm, edge offloading that migrates complex tasks from IoT devices to edge-cloud servers can break through the resource limitation of IoT devices, reduce the computing burden ...
and Deep Neural Network (DNN) computing. ...
On the one side, optimizing DNNs through task offloading has become a new direction in edge intelligence research since edge computing can offload complex computing tasks to edge/cloud servers. ...
arXiv:2008.09930v1
fatcat:ahggutqwsrbyvgd2hnwcuy26xi
Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things
2022
Electronics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
and whether smart connected objects, situational awareness algorithms, and edge computing technologies configure IoRT systems and cloud robotics in relation to distributed task coordination through visual ...
Artificial intelligence and intelligent workflows by use of AMSTAR (Assessing the Methodological Quality of Systematic Reviews), Dedoose, DistillerSR, and SRDR (Systematic Review Data Repository) have ...
Autonomous robotic systems harness edge and cloud computing tools, swarm technologies, and machine intelligence in cognitive decision-making. ...
doi:10.3390/electronics12010022
fatcat:74fn3hdotncxxh4agsxcycbp7m
AI Inspired Intelligent Resource Management in Future Wireless Network
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
However, the volume of edge resources is limited, while the number and complexity of tasks in the network are increasing sharply. ...
With the development of Artificial Intelligence (AI) technology, these AI algorithms have been applied to joint resource allocation problems to solve complex decision-making problems. ...
While, reinforcement learning algorithms are suitable for making resource allocation decisions in low-scale wireless networks with low-complexity. ...
doi:10.1109/access.2020.2968554
fatcat:2j7ulgfdcvht5c7c7kxkqw6lha
Review on Offloading of Vehicle Edge Computing
2022
Journal of Artificial Intelligence and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
For limited computing power and delay sensitive mobile applications on the Internet of Vehicles (IOV). It is important to offload computing tasks to the end of the VEC network. ...
This survey could give a reference for researchers to find and understand the important characteristics of VEC, which helps choose the optimal solutions for the offloading of computing tasks in VEC. ...
Zhan et al. proposed a computing offloading game formula to solve the dynamic computing offloading decision-making problems among edge computing users in the dynamic environment [34] . ...
doi:10.37965/jait.2022.0120
fatcat:cdmk3tyrlrhl3g4nqhk5wlwnlm
An Edge-Based Resource Allocation Optimization for the Internet of Medical Things (IoMT)
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Many healthcare organizations are progressively embracing or adopting an edge computing paradigm such that computationally intensive tasks can be processed at the edge of the network in order to avoid ...
In this paper, we extend our Edgify resource provisioning framework to consider the task offloading of healthcare applications' involving patients' data as a multiple criteria decision making (MCDM) process ...
This problem can often be solved using edge-computing, a paradigm for shifting computational resources closer to end-user devices making it an ideal solution for completing advanced tasks within hospital ...
arXiv:2108.13177v1
fatcat:j7mrmt3mvrdg3linpjtkowbpzm
« Previous
Showing results 1 — 15 out of 41,538 results