Mobile edge computing services for dynamic quality of service control

2017 ◽  
Author(s):  
Evelina Pencheva ◽  
Ivaylo Atanasov
Keyword(s):  
Quality Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Services ◽  
Service Control ◽  
Dynamic Quality

Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing

2021 ◽  
pp. 1-1
Author(s):  
Huiying Jin ◽  
Pengcheng Zhang ◽  
Hai Dong ◽  
Yuelong Zhu ◽  
Athman Bouguettaya
Keyword(s):  
Quality Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing

IPv6-Based Dynamic Quality of Service Control Strategy for Wireless Network

2010 ◽  
Vol 5 (9) ◽  
Author(s):  
Zhifeng Chen ◽  
Qiaoming Zhu ◽  
Peide Qian ◽  
Fengyan Chen
Keyword(s):  
Quality Of Service ◽  
Wireless Network ◽  
Control Strategy ◽  
Service Control ◽  
Dynamic Quality

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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