A hierarchical edge cloud architecture for mobile computing

By Liang Tong, Yong Li, Wei Gao. 2016

493 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

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Citations in the corpus

Adaptive Federated Learning in Resource Constrained Edge Computing Systems (2018, background): “Existing work on MEC focuses on generic applications, where solutions have been proposed for application offloading [11], [12], workload scheduling [13], [14], and service migration triggered by user mobility [15], [16].”
Online job dispatching and scheduling in edge-clouds (2017, background): “[10] proposed a hierarchical architecture for the edge-clouds.”
Cloud-based Federated Boosting for Mobile Crowdsensing (2020, methodology): “users. Compared with the architecture shown in Fig.1, edge servers are included to reflect the emerging edge computing architecture and provide a layer of privacy protection at the architectural level[21]. FedXGB proceeds in two steps. First, it invokes a suite of secret sharing based protocols for ”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, methodology): “The paper [92] designs a hierarchical edge cloud architecture, to efficiently utilize the cloud resources for serving the peak loads from mobile users.”
Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network (2018, methodology): “[17] designed a hierarchical edge computing architecture according to the distance between the edge server and users, and proposed an optimal offloading scheme for minimizing the task duration by using heuristic algorithm.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, background): “handle the peak computation workloads by distributing them across different tiers [144].”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, methodology): “[143] CR Develops workload placement algorithms to efficiently execute mobile programs in the Edge-Cloud network.”
Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things (2020, background): “Tong et al. [34] deployed cloud servers at the edge of the network, and the edge cloud is designed as a tree hierarchy of geo-distributed servers, which efﬁciently serves the peak loads from mobile users by utilizing the cloud resources.”
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning (2018, background): “R ELATED W ORK Existing work on MEC focuses on generic applications, where solutions have been proposed for application ofﬂoad-ing [7], [8], workload scheduling [9], [10], and service migration triggered by user mobility [11], [12].”
Collaborative Cloud and Edge Computing for Latency Minimization (2019, background): “Various fog-to-cloud hierarchical architectures were presented in [24]–[26] and joint task offloading and resource allocation algorithms for such collaborative computing systems were developed to achieve satisfactory latency and energy consumption performances [27]–[29].”
A Survey on the Edge Computing for the Internet of Things (2018, background): “in [38] proposed a hierarchical edge cloud model, which can be used to serve peak loads demanded from mobile users.”