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Overbook in Advance, Trade in Future: Computing Resource Provisioning in Hybrid Device-Edge-Cloud Networks

Published: 05 September 2022 Publication History

Abstract

The big data processing in distributed Internet of Things (IoT) systems calls for innovative computing architectures and resource provisioning techniques to support real-time and cost-effective computing services. This article introduces a novel overbooking-promoted forward trading mechanism named Overbook in Advance, Trade in Future (OATF), where computing resources can be traded across three parties, i.e. end-users, an edge server and a remote cloud server, under a hybrid device-edge-cloud network with uncertainties (e.g., “no shows”). More importantly, OATF encourages a feasible overbooking rate that allows the edge server to overbook resources to multiple end-users (e.g., exceed the resource supply), while purchasing backup resources from the cloud server, by determining rights and obligations associated with forward contracts in advance via analyzing historical statistics (e.g., network, resource dynamics). Such a mechanism can greatly improve time efficiency and resource utilization thanks to overbooking and pre-signed forward contracts. Critical issues such as overbooking rate design and risk management are carefully investigated in this article, while an interesting case study is proposed with mathematical analysis. Comprehensive simulations demonstrate that OATF achieves mutually beneficial utilities for different parties (cloud, edge, and end-users), as well as substantial resource usage and commendable time efficiency, in comparison with conventional trading methods.

References

[1]
S. Guan and A. Boukerche, “Intelligent edge-based service provisioning using smart cloudlets, fog and mobile edges,” IEEE Netw., vol. 36, no. 2, pp. 139–145, Mar./Apr. 2022.
[2]
S. Heet al., “An edge-computing paradigm for Internet of Things over power line communication networks,” IEEE Netw., vol. 34, no. 2, pp. 262–269, Mar./Apr. 2020.
[3]
M. Liwang, Z. Gao, and X. Wang, “Let’s trade in the future! A futures-enabled fast resource trading mechanism in edge computing-assisted UAV networks,” IEEE J. Sel. Areas Commun., vol. 39, no. 11, pp. 3252–3270, Nov. 2021.
[4]
M. Liwanget al., “Unifying futures and spot market: Overbooking-enabled resource trading in mobile edge networks,” IEEE Trans. Wireless Commun., vol. 21, no. 7, pp. 5467–5485, Jul. 2022.
[5]
M. Daiet al., “An edge-driven security framework for intelligent Internet of Things,” IEEE Netw., vol. 34, no. 5, pp. 39–45, Sep./Oct. 2020.
[6]
R. Malik and M. Vu, “On-request wireless charging and partial computation offloading in multi-access edge computing systems,” IEEE Trans. Wireless Commun., vol. 20, no. 10, pp. 6665–6679, Oct. 2021.
[7]
Y. Zhanget al., “Efficient computing resource sharing for mobile edge-cloud computing networks,” IEEE/ACM Trans. Netw., vol. 28, no. 3, pp. 1227–1240, Jun. 2020.
[8]
T. Q. Dinhet al., “Online resource procurement and allocation in a hybrid edge-cloud computing system,” IEEE Trans. Wireless Commun., vol. 19, no. 3, pp. 2137–2149, Mar. 2020.
[9]
A. Buzachiset al., “Evaluating an application aware distributed Dijkstra shortest path algorithm in hybrid cloud/edge environments,” IEEE Trans. Sustain. Comput., vol. 7, no. 2, pp. 289–298, Apr./Jun. 2021.
[10]
S. Shenget al., “Futures-based resource trading and fair pricing in real-time IoT networks,” IEEE Wireless Commun. Lett., vol. 9, no. 1, pp. 125–128, Jan. 2020.
[11]
K. Chard and K. Bubendorfer, “High performance resource allocation strategies for computational economies,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 1, pp. 72–84, Jan. 2013.
[12]
C. Sexton, N. Marchetti, and L. A. DaSilva, “On provisioning slices and overbooking resources in service tailored networks of the future,” IEEE/ACM Trans. Netw., vol. 28, no. 5, pp. 2106–2119, Oct. 2020.
[13]
X. Xie, Z. Fan, and X. Zhong, “Appointment capacity planning with overbooking for outpatient clinics with patient no-shows,” IEEE Trans. Autom. Sci. Eng., vol. 19, no. 2, pp. 864–883, Apr. 2022.
[14]
W. Z. Zhanget al., “Secure and optimized load balancing for multitier IoT and edge-cloud computing systems,” IEEE Internet Things J., vol. 8, no. 10, pp. 8119–8132, May 2021.
[15]
Y. Liet al., “Hybrid NOMA-FDMA assisted dual computation offloading: A latency minimization approach,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 5, pp. 3345–3360, Sep./Oct. 2022.

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        Published: 05 September 2022

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