RELIABILITY-AWARE MONITORING FOR CLOUD–FOG ARCHITECTURES USING LIGHTWEIGHT MACHINE LEARNING
DOI:
https://doi.org/10.65009/9tsbnd10Keywords:
Cloud–fog computing, reliability-aware monitoring, lightweight machine learning, fault detection, adaptive sampling, edge intelligence.,,Abstract
Cloud-fog architectures allow high-scale and latency-aware services through the distribution of
the computation to nearer data sources, still, the provision of reliable and efficient monitoring is
a challenging issue because of resource limitation, heterogeneity, and workload dynamism.
Redundancy A paper highlighting a dependable platform of monitoring in cloud-fog ecosystems
merging optimistic machine learning with agile sampling and selective reporting is introduced
in this paper. The estimation of local reliability at the fog nodes is useful in dynamic setting the
intensity of monitoring depending on the predicted operational stability to minimize the overhead
unnecessarily and maintain observability. The framework lays more emphasis on critical
conditions by employing the reliability-based adaptation and only transmits small-sized
summaries to the cloud in cases of degradation. Large-scale testing based on heterogeneous
monitoring data shows that it is characterized by substantial benefits in average monitoring
latency, stability, scalability, coverage, and reliability detection accuracy in comparison with
well-established fog computing, edge computing, adaptive sampling, and lightweight anomaly
detection strategies. The findings uphold that reliability awareness and lightweight learning are
effective measures of scalability, efficiency, and resilience of monitoring in cloud-fog
architectures.
References
S. Yi, C. Li, and Q. Li, “A survey of Fog computing: Concepts, applications and issues,” in Proc.
Workshop on Mobile Big Data (Mobidata), Hangzhou, China, Jun. 2015, pp. 37–42, doi:
1109/MBD.2015.11.
of
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE
Internet
Things Journal, vol. 3, no. 5, pp. 637–646, Sept. 2016, doi:
1109/JIOT.2016.2579198.
R. Mahmud, R. Kotagiri, and R. Buyya, “Application deployment and resource scheduling in
fog computing environments: A taxonomy and survey,” ACM Computing Surveys, vol. 51, no.
, Art. 104, Oct. 2018, doi: 10.1145/3231786.
D. Trihinas, G. Pallis, and M. D. Dikaiakos, “Low-Cost Adaptive Monitoring Techniques for the
Internet of Things,” IEEE Transactions on Services Computing, 2018.
of
N. Abbas, Y. Zhang, A. Taherkordi, and T. Skeie, “Mobile edge computing: A survey,” IEEE
Internet
Things Journal, vol. 5, no. 1, pp. 450–465, Feb. 2018, doi:
1109/JIOT.2017.2769498.
I. Azimi, A. Anzanpour, A. M. Rahmani, T. Pahikkala, M. Levorato, P. Liljeberg, and N. Dutt,
“HiCH: Hierarchical fog-assisted computing architecture for healthcare IoT,” ACM Transactions
on Embedded Computing Systems, vol. 16, no. 5s, Art. 174, Sep. 2017.
Y. Lai, F. Yang, J. Su, Q. Zhou, T. Wang, L. Zhang, and Y. Xu, “Fog-based two-phase event
monitoring and data gathering in vehicular sensor networks,” Sensors, vol. 18, no. 1, Art. 82,
Jan. 2018, doi: 10.3390/s18010082.
H. Sedjelmaci, S. M. Senouci, and M. Al-Bahri, “A lightweight anomaly detection technique for
low-resource IoT devices: A game-theoretic methodology,” in Proc. IEEE International
Conference on Communications (ICC), 2016, pp. 1–6, doi: 10.1109/ICC.2016.7510811.
J. Yao and N. Ansari, “Caching in energy harvesting aided Internet of Things: A game-theoretic
approach,” IEEE Internet Things J., vol. 6, no. 2, pp. 3194–3201, Apr. 2019. doi:
1109/JIOT.2018.2880483.
M. Li, P. Si, and Y. Zhang, “Delay-tolerant data traffic to software-defined vehicular
networks with mobile edge computing in smart city,” IEEE Trans. Veh. Technol., vol. 67, no. 10,
pp. 9073–9086, Oct. 2018.
A. Bartoli, G. Hernández-Serrano, M. Soriano, and G. Pérez, “Energy-efficient data
collection in Internet of Things through adaptive sampling,” IEEE Internet of Things Journal,
vol. 6, no. 3, pp. 5227–5237, Jun. 2019, doi: 10.1109/JIOT.2019.2896093.
C. Prazeres and M. Serrano, “SOFT-IoT: Self-Organizing FOG of Things,” 30th
International Conference on Advanced Information Networking and Applications Workshops
(WAINA), pp. 803–808, 2016.
A. M. Rahmani, T. N. Gia, B. Negash, A. Anzanpour, I. Azimi, M. Jiang, and P. Liljeberg,
“Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog
computing approach,” Future Gener. Comput. Syst., vol. 78, pp. 641–658, Jan. 2018.
N. C. Luong, P. Wang, D. Niyato, Y. Xiao, and P. Zhang, “Data-collection and routing
protocols for Internet of Things: A survey,” IEEE Communications Surveys & Tutorials, vol. 18,
no. 3, pp. 2091–2127, 2016.
H. Atlam, R. Walters, and G. Wills, “Fog computing and the Internet of Things: A
review,” Big Data Cognit. Comput., vol. 2, no. 2, p. 10, Apr. 2018.
F. Meneghello, M. Calore, D. Zucchetto, M. Polese, and A. Zanella, “IoT: Internet of
threats? A survey of practical security vulnerabilities in real IoT devices,” IEEE Internet of
Things Journal, vol. 6, no. 5, pp. 8182–8201, Oct. 2019, doi: 10.1109/JIOT.2019.2935189.
S. H. Y. Wong, W. T. Ooi, and C. M. Lee, “Adaptive and energy-aware sensing strategies
for IoT systems,” IEEE Sensors Journal, vol. 18, no. 12, pp. 4866–4878, Jun. 2018, doi:
1109/JSEN.2018.2826025.
R. L. C. de Oliveira and E. R. de Lima, “Adaptive sampling and data reduction in IoT
systems: A survey,” IEEE Communications Surveys & Tutorials, 2019.
C.-Y. Chu, K. Xi, M. Luo, and H. J. Chao, “Congestion-aware single link failure recovery
in hybrid SDN networks,” in Proc. IEEE Conf. Computer Commun., 2015, pp. 1086–1094.
Q. Zhang, Q. Zhu, and M. R. Lyu, “Fault tolerance in distributed systems: A survey,” IEEE
Transactions on Dependable and Secure Computing, vol. 14, no. 4, pp. 783–796, Jul.–Aug. 2017.
Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge
computing: The communication perspective,” IEEE Communications Surveys & Tutorials, vol.
, no. 4, pp. 2322–2358, 2017, doi: 10.1109/COMST.2017.2745201.
D. N. C. Luong, P. Wang, D. Niyato, Y. Xiao, and P. Zhang, “Data-collection and routing
protocols for Internet of Things: A survey,” IEEE Communications Surveys & Tutorials, vol. 18,
no. 3, pp. 2091–2127, 2016, doi: 10.1109/COMST.2016.255730
