Distributed Multiuser Computation Offloading for Cloudlet-Based Mobile Cloud Computing: A Game-Theoretic Machine Learning Approach

In this paper, we investigate the problem of multiuser computation offloading for cloudlet-based mobile cloud computing in a multichannel wireless contention environment. The studied system is fully distributed so that each mobile device user can make the offloading decisions based only on its indiv...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on vehicular technology Ročník 67; číslo 1; s. 752 - 764
Hlavní autoři: Cao, Huijin, Cai, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial intelligence
Cloud computing
Cloudlet based mobile cloud computing
Computation offloading
Computer simulation
Computing costs
Decision analysis
Decision making
Electronic devices
exact potential game
Game theory
Games
Machine learning
Machine learning algorithms
Mobile applications
Mobile communication
Mobile communication systems
Mobile computing
Mobile handsets
Multichannel communication
nash equilibrium points
Technology assessment
Wireless communication
ISSN:0018-9545, 1939-9359
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, we investigate the problem of multiuser computation offloading for cloudlet-based mobile cloud computing in a multichannel wireless contention environment. The studied system is fully distributed so that each mobile device user can make the offloading decisions based only on its individual information, and without information exchange. We first formulate this multiuser computation offloading decision making problem as a noncooperative game. After analyzing the structural property of the formulated game, we show that it is an exact potential game, and has at least one pure-strategy Nash equilibrium point (NEP). To achieve the NEPs in a fully distributed environment, we propose a fully distributed computation offloading (FDCO) algorithm based on machine learning technology. We then theoretically analyze the performance of the proposed FDCO algorithm in terms of the number of beneficial cloudlet computing mobile devices and the system-wide execution cost. Finally, simulation results validate the effectiveness of our proposed algorithm compared with counterparts.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2017.2740724