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Achieving balance between economic efficiency and user fairness in traffic rate management
Author(s) -
Pan Lian,
Wang Hanwu,
Li Hexin
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4618
Subject(s) - computer science , resource allocation , resource (disambiguation) , max min fairness , economic efficiency , key (lock) , resource management (computing) , balance (ability) , resource efficiency , relation (database) , fairness measure , environmental economics , operations research , computer network , computer security , microeconomics , economics , telecommunications , database , throughput , ecology , engineering , biology , wireless , medicine , physical medicine and rehabilitation
Summary This paper studies achieving the desired balance between the resource economic efficiency and user fairness in network traffic rate allocation management. Fairness is a basic requirement of different competing traffic users, while maximizing the economic efficiency of traffic rate resource is desired by the network system resource provider. Hence, how to balance these two respects to achieve the desired trade‐off is a key challenging issue in real applications. Particularly, we propose a weight mechanism to specify the rate allocation weight ratio among different competing traffic users and also a trade‐off factor mechanism to clarify the extent of fairness compromise. To optimize the system rate resource economic efficiency, we formulate an objective optimization problem with consideration of both user weight diversity and fairness trade‐off as well. We also derive the balance curve, which shows the rate resource economic efficiency improved in relation to the extent of traffic user fairness trade‐off. The demonstration models are also constructed to implemented to implement both the trade‐off acceptable and unacceptable cases in practical traffic rate resource allocation applications.