QoE and power efficiency tradeoff for fog computing networks with fog node cooperation

By Yong Xiao, Marwan Krunz. 2017

228 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, 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 ofﬂoad-ing [10], [11], workload scheduling [12], [13], and service migration triggered by user mobility [14], [15].”
Resource Scheduling in Edge Computing: A Survey (2021, background): “vice migration, by which services are dynamically migrated across multiple heterogeneous ENs [98], [99]; (iii) offload forwarding, in which an EN is regarded as a relay to forward workloads to neighboring ENs [100].”
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning (2018, background): “RELATED WORK Existing work on MEC focuses on generic applications, where solutions have been proposed for application offloading [7], [8], workload scheduling [9], [10], and service migration triggered by user mobility [11], [12].”
Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network (2018, methodology): “As for the resource allocation of edge computing, Xiao and Krunz [37] proposed”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “study the workload offloading problem in fog networks [295].”
Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach (2019, background): “According to [33], [43], each edge cloud is equipped with multiple CPU cores, and the sum frequency is 25 Ghz.”
Distributed Machine Learning in Coalition Environments: Overview of Techniques (2018, background): “The partition strategy and optimization algorithm can take into account specific aspects of machine learning models, such as in [16]–[19], or alternatively, use abstract computation models with annotated resource demands and apply solutions for generic service placement problems [15], [20]–[25].”
Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “The tradeoff problem is studied in [11] for computing networks with fog node cooperation aiming at minimizing the response time of fog nodes under a given power efﬁciency constraint.”