scholarly journals An Overview of Service Placement Problem in Fog and Edge Computing

2020 ◽  
Vol 53 (3) ◽  
pp. 1-35 ◽  
Author(s):  
Farah Aït Salaht ◽  
Frédéric Desprez ◽  
Adrien Lebre
Keyword(s):  
Edge Computing ◽  
Placement Problem ◽  
Service Placement

scholarly journals Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ying Zhu
Keyword(s):  
Time Series ◽  
Public Opinion ◽  
Chaotic Time Series ◽  
Edge Computing ◽  
Factor Graph ◽  
New Paradigm ◽  
Placement Problem ◽  
Index Method ◽  
Service Placement ◽  
Network Public Opinion

In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

Dynamic Service Placement and Load Distribution in Edge Computing

2020 ◽  
Author(s):  
Adyson Magalhaes Maia ◽  
Yacine Ghamri-Doudane ◽  
Dario Vieira ◽  
Miguel Franklin de Castro
Keyword(s):  
Load Distribution ◽  
Edge Computing ◽  
Service Placement

scholarly journals Intelligent scaling for 6G IoE services for resource provisioning

2021 ◽  
Vol 7 ◽  
pp. e755
Author(s):  
Abdullah Alharbi ◽  
Hashem Alyami ◽  
Poongodi M ◽  
Hafiz Tayyab Rauf ◽  
Seifedine Kadry
Keyword(s):  
Reinforcement Learning ◽  
Resource Provisioning ◽  
Edge Computing ◽  
Effective Model ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Placement Decisions ◽  
Service Placement

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.

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