The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A computação móvel de borda (MEC) é um novo paradigma de computação, que fornece suporte computacional para equipamentos de usuário (UEs) com recursos limitados. Nesta carta, concebemos um quadro de incentivos eficaz para encorajar os operadores do MEC a fornecer serviços de computação aos UEs. É estudado o problema da alocação conjunta de recursos de comunicação e computação para maximizar a receita das operadoras do MEC. Com base na teoria do leilão, projetamos um algoritmo de leilão iterativo multi-rodada (MRIA) para resolver o problema. Extensas simulações foram conduzidas para avaliar o desempenho do algoritmo proposto e mostra-se que o algoritmo proposto pode melhorar significativamente a receita geral das operadoras MEC.
Ben LIU
Nanjing University of Posts and Telecommunications
Ding XU
Nanjing University of Posts and Telecommunications
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Ben LIU, Ding XU, "Auction-Based Resource Allocation for Mobile Edge Computing Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 4, pp. 718-722, April 2020, doi: 10.1587/transfun.2019EAL2136.
Abstract: Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAL2136/_p
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@ARTICLE{e103-a_4_718,
author={Ben LIU, Ding XU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Auction-Based Resource Allocation for Mobile Edge Computing Networks},
year={2020},
volume={E103-A},
number={4},
pages={718-722},
abstract={Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.},
keywords={},
doi={10.1587/transfun.2019EAL2136},
ISSN={1745-1337},
month={April},}
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TY - JOUR
TI - Auction-Based Resource Allocation for Mobile Edge Computing Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 718
EP - 722
AU - Ben LIU
AU - Ding XU
PY - 2020
DO - 10.1587/transfun.2019EAL2136
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2020
AB - Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.
ER -