IEICE TRANSACTIONS on Fundamentals
Auction-Based Resource Allocation for Mobile Edge Computing Networks
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.4 pp.718-722
- Publication Date
- 2020/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019EAL2136
- Type of Manuscript
- LETTER
- Category
- Communication Theory and Signals
Authors
Ben LIU
Nanjing University of Posts and Telecommunications
Ding XU
Nanjing University of Posts and Telecommunications
Keyword
Latest Issue
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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},}
Copy
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 -
English
