[HTML][HTML] A state-of-the-art review of task scheduling for edge computing: A delay-sensitive application perspective
The edge computing paradigm enables mobile devices with limited memory and processing
power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications …
power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications …
Drl-driven joint task offloading and resource allocation for energy-efficient content delivery in cloud-edge cooperation networks
With the proliferation of mobile devices (eg, vehicles and smartphones), rich media content
services from massive users lead to high network resource consumption and energy usage …
services from massive users lead to high network resource consumption and energy usage …
Intelligent resource allocation for edge-cloud collaborative networks: A hybrid DDPG-D3QN approach
To handle the ever-increasing IoT devices with computation-intensive and delay-critical
applications, it is imperative to leverage the collaborative potential of edge and cloud …
applications, it is imperative to leverage the collaborative potential of edge and cloud …
DRL-based secure video offloading in MEC-enabled IoT networks
Wireless offloading in mobile-edge-computing (MEC)-enabled Internet of Things (IoT)
networks inevitably suffers the risk of eavesdropping. Physical-layer security (PLS) …
networks inevitably suffers the risk of eavesdropping. Physical-layer security (PLS) …
Energy-efficient collaborative multi-access edge computing via deep reinforcement learning
The joint problem of task offloading, collaborative computing, and resource allocation for
multi-access edge computing (MEC) is a challenging issue. In this article, splitting computing …
multi-access edge computing (MEC) is a challenging issue. In this article, splitting computing …
Security computing resource allocation based on deep reinforcement learning in serverless multi-cloud edge computing
H Zhang, J Wang, H Zhang, C Bu - Future Generation Computer Systems, 2024 - Elsevier
Handling computationally intensive tasks is challenging for user devices (UDs) with limited
computing resources. Serverless cloud edge computing solves this problem and reduces …
computing resources. Serverless cloud edge computing solves this problem and reduces …
The dichotomy of cloud and iot: Cloud-assisted iot from a security perspective
In recent years, the existence of a significant cross-impact between Cloud computing and
Internet of Things (IoT) has lead to a dichotomy that gives raise to Cloud-Assisted IoT …
Internet of Things (IoT) has lead to a dichotomy that gives raise to Cloud-Assisted IoT …
Deep reinforcement learning-based joint optimization of delay and privacy in multiple-user MEC systems
Multi-access Edge Computing (MEC) enables mobile users to run various delay-sensitive
applications via offloading computation tasks to MEC servers. However, the location privacy …
applications via offloading computation tasks to MEC servers. However, the location privacy …
Joint task offloading and resource allocation for multi-user and multi-server MEC networks: A deep reinforcement learning approach with multi-branch architecture
Y Sun, Q He - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Abstract Mobile Edge Computing (MEC) is a promising computing paradigm in the context of
5G networks, as it enables the migration of workloads from User Equipments (UEs) to …
5G networks, as it enables the migration of workloads from User Equipments (UEs) to …
Privacy-preserving cloud computing: ecosystem, life cycle, layered architecture and future roadmap
S Ahmadi, M Salehfar - arXiv preprint arXiv:2204.11120, 2022 - arxiv.org
Privacy-Preserving Cloud Computing is an emerging technology with many applications in
various fields. Cloud computing is important because it allows for scalability, adaptability …
various fields. Cloud computing is important because it allows for scalability, adaptability …