Toward intelligent cooperation at the edge: improving the QoS of workflow scheduling with the competitive cooperation of edge servers-Reference-Cited by-同舟云学术

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Toward intelligent cooperation at the edge: improving the QoS of workflow scheduling with the competitive cooperation of edge servers

Published:2023-05-16 Issue: Volume: Page:
ISSN:1022-0038
Container-title:Wireless Networks
language:en
Short-container-title:Wireless Netw

Author:

Zhu Kaige,Zhang Zhenjiang^ORCID,Sun Feng

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s11276-023-03361-1.pdf

Reference35 articles.

1. Kim, T. H., Ramos, C., & Mohammed, S. (2017). Smart city and IoT. Future Generation Computer Systems, 76, 159–162. https://doi.org/10.1016/j.future.2017.03.034

2. Patel, P., Narmawala, Z., & Thakkar, A. (2019). A survey on intelligent transportation system using internet of things. Emerging Research in Computing, Information, Communication and Applications: ERCICA, 2018(1), 231–240. https://doi.org/10.1007/978-981-13-5953-8_20

3. Rodriguez, M. A., & Buyya, R. (2017). A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Concurrency and Computation: Practice and Experience, 29(8), 1–23. https://doi.org/10.1002/cpe.4041

4. Chitra, S. (2020). Compensatory Aggregation based Failure Aware Cloud Workflow Scheduling. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), IEEE. https://doi.org/10.1109/ICIMIA48430.2020.9074964

5. Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Network, 32(1), 96–101. https://doi.org/10.1109/MNET.2018.1700202

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