Cost-Efficient Resource Provisioning in Cloud Assisted Mobile Edge Computing

2017 ◽  
Author(s):  
Xiao Ma ◽  
Shan Zhang ◽  
Peng Yang ◽  
Ning Zhang ◽  
Chuang Lin ◽  
...  
Keyword(s):  
Resource Provisioning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Efficient Resource ◽  
Cost Efficient

scholarly journals A cost efficient model for minimizing energy consumption and processing time for IoT tasks in a Mobile Edge Computing environment

2020 ◽  
Author(s):  
João Luiz Grave Gross ◽  
Cláudio Fernando Fernando Resin Geyer
Keyword(s):  
Energy Consumption ◽  
Cost Minimization ◽  
Cost Model ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Data Intensive ◽  
Battery Capacity ◽  
Cost Efficient

In a scenario with increasingly mobile devices connected to the Internet, data-intensive applications and energy consumption limited by battery capacity, we propose a cost minimization model for IoT devices in a Mobile Edge Computing (MEC) architecture with the main objective of reducing total energy consumption and total elapsed times from task creation to conclusion. The cost model is implemented using the TEMS (Time and Energy Minimization Scheduler) scheduling algorithm and validated with simulation. The results show that it is possible to reduce the energy consumed in the system by up to 51.61% and the total elapsed time by up to 86.65% in the simulated cases with the parameters and characteristics defined in each experiment.

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.

Towards cost-efficient resource provisioning with multiple mobile users in fog computing

2020 ◽  
Vol 146 ◽  
pp. 96-106
Author(s):  
Shuaibing Lu ◽  
Jie Wu ◽  
Yubin Duan ◽  
Ning Wang ◽  
Juan Fang
Keyword(s):  
Fog Computing ◽  
Resource Provisioning ◽  
Mobile Users ◽  
Efficient Resource ◽  
Cost Efficient

scholarly journals Energy-Efficient Resource Allocation for Latency-Sensitive Mobile Edge Computing

2020 ◽  
Vol 69 (2) ◽  
pp. 2246-2262 ◽  
Author(s):  
Xihan Chen ◽  
Yunlong Cai ◽  
Liyan Li ◽  
Minjian Zhao ◽  
Benoit Champagne ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Efficient Resource ◽  
Energy Efficient Resource Allocation
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