Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks

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Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks. / Jedari, Behrouz; Di Francesco, Mario.

INFOCOM 2019 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers, 2019. p. 1864-1872 8737433 (Proceedings - IEEE INFOCOM; Vol. 2019-April).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Jedari, B & Di Francesco, M 2019, Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks. in INFOCOM 2019 - IEEE Conference on Computer Communications., 8737433, Proceedings - IEEE INFOCOM, vol. 2019-April, Institute of Electrical and Electronics Engineers, pp. 1864-1872, IEEE Conference on Computer Communications, Paris, France, 29/04/2019. https://doi.org/10.1109/INFOCOM.2019.8737433

APA

Jedari, B., & Di Francesco, M. (2019). Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks. In INFOCOM 2019 - IEEE Conference on Computer Communications (pp. 1864-1872). [8737433] (Proceedings - IEEE INFOCOM; Vol. 2019-April). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/INFOCOM.2019.8737433

Vancouver

Jedari B, Di Francesco M. Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks. In INFOCOM 2019 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers. 2019. p. 1864-1872. 8737433. (Proceedings - IEEE INFOCOM). https://doi.org/10.1109/INFOCOM.2019.8737433

Author

Jedari, Behrouz ; Di Francesco, Mario. / Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks. INFOCOM 2019 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers, 2019. pp. 1864-1872 (Proceedings - IEEE INFOCOM).

Bibtex - Download

@inproceedings{fc21aeb9e0d64d3dbcc0feef10821b5a,
title = "Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks",
abstract = "Content providers (CPs) are keen to cache their popular contents in small-cell base stations (SBSs) provided by mobile network operators (MNOs). In fact, they can serve the requests of their subscribers with low latency, thereby increasing user satisfaction. Employing advanced video encoding techniques, such as scalable video coding (SVC), improves the utilization of wireless resources and the network infrastructure. However, the cache trading policies for SVC videos in multi-provider networks have not been studied yet. In this article, we design a commercial trading system in which multiple CPs, each owning SVC videos, compete over renting the cache in multiple SBSs provided by an MNO. We model cache trading between the MNO and CPs as a social welfare maximization problem, whose objective is to maximize the trading profit while achieving the economic properties of rationality, balanced budget, and truthfulness. Since optimal allocation of random-size caches to multiple CPs is NP-hard, we devise an iterative trading mechanism based on double auction called DOCAT, wherein the cache of SBSs is segmented and traded in multiple rounds. In each round of the auction, the MNO and CPs price the cache segments based on their profit, then submit their asking and buying bids, respectively. Next, a many-to-one matching algorithm is run to efficiently find perfect matches between the cache segments and winning CPs. Numerical results based on a real video dataset show that DOCAT increases the social welfare of the system while satisfying the desired economic properties.",
keywords = "auction, edge caching, game theory, Heterogeneous networks, network economics, scalable video coding",
author = "Behrouz Jedari and {Di Francesco}, Mario",
year = "2019",
month = "4",
day = "1",
doi = "10.1109/INFOCOM.2019.8737433",
language = "English",
series = "Proceedings - IEEE INFOCOM",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1864--1872",
booktitle = "INFOCOM 2019 - IEEE Conference on Computer Communications",
address = "United States",

}

RIS - Download

TY - GEN

T1 - Auction-based Cache Trading for Scalable Videos in Multi-Provider Heterogeneous Networks

AU - Jedari, Behrouz

AU - Di Francesco, Mario

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Content providers (CPs) are keen to cache their popular contents in small-cell base stations (SBSs) provided by mobile network operators (MNOs). In fact, they can serve the requests of their subscribers with low latency, thereby increasing user satisfaction. Employing advanced video encoding techniques, such as scalable video coding (SVC), improves the utilization of wireless resources and the network infrastructure. However, the cache trading policies for SVC videos in multi-provider networks have not been studied yet. In this article, we design a commercial trading system in which multiple CPs, each owning SVC videos, compete over renting the cache in multiple SBSs provided by an MNO. We model cache trading between the MNO and CPs as a social welfare maximization problem, whose objective is to maximize the trading profit while achieving the economic properties of rationality, balanced budget, and truthfulness. Since optimal allocation of random-size caches to multiple CPs is NP-hard, we devise an iterative trading mechanism based on double auction called DOCAT, wherein the cache of SBSs is segmented and traded in multiple rounds. In each round of the auction, the MNO and CPs price the cache segments based on their profit, then submit their asking and buying bids, respectively. Next, a many-to-one matching algorithm is run to efficiently find perfect matches between the cache segments and winning CPs. Numerical results based on a real video dataset show that DOCAT increases the social welfare of the system while satisfying the desired economic properties.

AB - Content providers (CPs) are keen to cache their popular contents in small-cell base stations (SBSs) provided by mobile network operators (MNOs). In fact, they can serve the requests of their subscribers with low latency, thereby increasing user satisfaction. Employing advanced video encoding techniques, such as scalable video coding (SVC), improves the utilization of wireless resources and the network infrastructure. However, the cache trading policies for SVC videos in multi-provider networks have not been studied yet. In this article, we design a commercial trading system in which multiple CPs, each owning SVC videos, compete over renting the cache in multiple SBSs provided by an MNO. We model cache trading between the MNO and CPs as a social welfare maximization problem, whose objective is to maximize the trading profit while achieving the economic properties of rationality, balanced budget, and truthfulness. Since optimal allocation of random-size caches to multiple CPs is NP-hard, we devise an iterative trading mechanism based on double auction called DOCAT, wherein the cache of SBSs is segmented and traded in multiple rounds. In each round of the auction, the MNO and CPs price the cache segments based on their profit, then submit their asking and buying bids, respectively. Next, a many-to-one matching algorithm is run to efficiently find perfect matches between the cache segments and winning CPs. Numerical results based on a real video dataset show that DOCAT increases the social welfare of the system while satisfying the desired economic properties.

KW - auction

KW - edge caching

KW - game theory

KW - Heterogeneous networks

KW - network economics

KW - scalable video coding

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U2 - 10.1109/INFOCOM.2019.8737433

DO - 10.1109/INFOCOM.2019.8737433

M3 - Conference contribution

T3 - Proceedings - IEEE INFOCOM

SP - 1864

EP - 1872

BT - INFOCOM 2019 - IEEE Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers

ER -

ID: 35242236