Optimized Fault-Tolerant Placement of Microservices in Distributed Fog Networks

Authors

  • Shally Gupta Department of Information Technology, Delhi Technological University, India https://orcid.org/0000-0001-5757-8063
  • Nanhay Singh Department of Computer Science & Engineering, NSUT East Campus, India

DOI:

https://doi.org/10.59461/ijdiic.v4i4.241

Keywords:

Microservices, Fog Computing, Fault Tolerance, Load Balancing , System Reliability

Abstract

We propose a fault-tolerance aware placement algorithm for microservices in distributed fog environments that combines a reactive Dynamic Connections Load Balancer (DCLB) with a proactive, fault-threshold driven migration and backup-predetermination mechanism. The DCLB places services only on healthy nodes by using active-connection-based load metrics, while the proactive module preassigns backup nodes for critical services and triggers rapid migration when a node crosses a fault threshold. We evaluate the approach using extensive iFogSim simulations on a smart-home management microservice composition under controlled failure injections. This paper presents a fault–tolerance–aware microservice placement strategy that integrates health-aware primary placement with proactive backup node predetermination. Experimental results show that the strategy effectively limits service disruption, achieving an average service downtime of less than 30% of the total simulation time across failure scenarios. System reliability remains consistently high, with values exceeding 0.85 in single-failure cases and remaining above 0.75 under multiple failures. Resource utilization across fog nodes remains balanced, with moderate memory usage and stable energy consumption. These results demonstrate that proactive, health-aware placement improves service continuity and resilience in fog-based microservice deployments.

Downloads

Download data is not yet available.

References

A. Yousefpour et al., “All one needs to know about fog computing and related edge computing paradigms: A complete survey,” J Syst Archit, vol. 98, pp. 289–330, Sep. 2019, doi: 10.1016/j.sysarc.2019.02.009.

A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for Smart Cities,” IEEE Internet Things J, vol. 1, no. 1, pp. 22–32, Feb. 2014, doi: 10.1109/JIOT.2014.2306328.

M. Pitkänen et al., “SCAMPI,” in Proceedings of the first edition of the MCC workshop on Mobile cloud computing, New York, NY, USA: ACM, Aug. 2012, pp. 7–12. doi: 10.1145/2342509.2342512.

F. Foukalas, “Cognitive IoT platform for fog computing industrial applications,” Comput Electr Eng, vol. 87, p. 106770, Oct. 2020, doi: 10.1016/j.compeleceng.2020.106770.

https://www.iiconsortium.org/pdf/OpenFog_Reference_Architecture_2_09_17.pdf

S. Tuli, G. Casale, and N. R. Jennings, “DRAGON: Decentralized Fault Tolerance in Edge Federations,” IEEE Trans Netw Serv Manag, vol. 20, no. 1, pp. 276–291, Mar. 2023, doi: 10.1109/TNSM.2022.3199886.

X. Masip, E. Marín, J. Garcia, and S. Sànchez, “Collaborative Mechanism for Hybrid Fog‐Cloud Scenarios,” in Fog and Fogonomics, Wiley, 2020, pp. 7–60. doi: 10.1002/9781119501121.ch2.

B. K. Ray, A. Saha, S. Khatua, and S. Roy, “Proactive Fault-Tolerance Technique to Enhance Reliability of Cloud Service in Cloud Federation Environment,” IEEE Trans Cloud Comput, vol. 10, no. 2, pp. 957–971, Apr. 2022, doi: 10.1109/TCC.2020.2968522.

J. Zhang, A. Zhou, Q. Sun, S. Wang, and F. Yang, “Overview on Fault Tolerance Strategies of Composite Service in Service Computing,” Wirel Commun Mob Comput, vol. 2018, no. 1, Jan. 2018, doi: 10.1155/2018/9787503.

J. Wang and D. Li, “Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing,” Sensors, vol. 19, no. 5, p. 1023, Feb. 2019, doi: 10.3390/s19051023.

S. Pallewatta, V. Kostakos, and R. Buyya, “Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions,” ACM Comput Surv, vol. 55, no. 14s, pp. 1–43, Dec. 2023, doi: 10.1145/3592598.

L. Pons et al., “Effect of Hyper-Threading in Latency-Critical Multithreaded Cloud Applications and Utilization Analysis of the Major System Resources,” Futur Gener Comput Syst, vol. 131, pp. 194–208, Jun. 2022, doi: 10.1016/j.future.2022.01.025.

M. Adeppady, C. F. Chiasserini, H. Karl, and P. Giaccone, “iPlace: An Interference-aware Clustering Algorithm for Microservice Placement,” in ICC 2022 - IEEE International Conference on Communications, IEEE, May 2022, pp. 5457–5462. doi: 10.1109/ICC45855.2022.9839222.

A. Samanta, F. Esposito, and T. G. Nguyen, “Fault-Tolerant Mechanism for Edge-Based IoT Networks With Demand Uncertainty,” IEEE Internet Things J, vol. 8, no. 23, pp. 16963–16971, Dec. 2021, doi: 10.1109/JIOT.2021.3075681.

A. Alarifi, F. Abdelsamie, and M. Amoon, “A fault-tolerant aware scheduling method for fog-cloud environments,” PLoS One, vol. 14, no. 10, p. e0223902, Oct. 2019, doi: 10.1371/journal.pone.0223902.

S. Tuli, G. Casale, L. Cherkasova, and N. R. Jennings, “DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model,” in IEEE INFOCOM 2023 - IEEE Conference on Computer Communications, IEEE, May 2023, pp. 1–10. doi: 10.1109/INFOCOM53939.2023.10229049.

S. Khan, I. A. Shah, K. Aurangzeb, S. Ahmad, J. A. Khan, and M. S. Anwar, “Energy-Efficient Task Scheduling Using Fault Tolerance Technique for IoT Applications in Fog Computing Environment,” IEEE Internet Things J, vol. 11, no. 24, pp. 39009–39019, Dec. 2024, doi: 10.1109/JIOT.2024.3403003.

S. Maurya, V. K. Jain, and D. R. Chowdhury, “Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network,” J Netw Comput Appl, vol. 144, pp. 118–137, Oct. 2019, doi: 10.1016/j.jnca.2019.06.012.

A. Marahatta, Y. Wang, F. Zhang, A. K. Sangaiah, S. K. S. Tyagi, and Z. Liu, “Energy-Aware Fault-Tolerant Dynamic Task Scheduling Scheme for Virtualized Cloud Data Centers,” Mob Networks Appl, vol. 24, no. 3, pp. 1063–1077, Jun. 2019, doi: 10.1007/s11036-018-1062-7.

K. Wang, Y. Shao, L. Xie, J. Wu, and S. Guo, “Adaptive and Fault-Tolerant Data Processing in Healthcare IoT Based on Fog Computing,” IEEE Trans Netw Sci Eng, vol. 7, no. 1, pp. 263–273, Jan. 2020, doi: 10.1109/TNSE.2018.2859307.

I. Goiri, F. Julia, J. Guitart, and J. Torres, “Checkpoint-based fault-tolerant infrastructure for virtualized service providers,” in 2010 IEEE Network Operations and Management Symposium - NOMS 2010, IEEE, Apr. 2010, pp. 455–462. doi: 10.1109/NOMS.2010.5488493.

J. Cao, M. Simonin, G. Cooperman, and C. Morin, “Checkpointing as a Service in Heterogeneous Cloud Environments,” in 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE, May 2015, pp. 61–70. doi: 10.1109/CCGrid.2015.160.

Y. Ramzanpoor, M. Hosseini Shirvani, and M. Golsorkhtabaramiri, “Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure,” Complex Intell Syst, vol. 8, no. 1, pp. 361–392, Feb. 2022, doi: 10.1007/s40747-021-00368-z.

C. Canali, C. Gazzotti, R. Lancellotti, and F. Schena, “Placement of IoT Microservices in Fog Computing Systems: A Comparison of Heuristics,” Algorithms, vol. 16, no. 9, p. 441, Sep. 2023, doi: 10.3390/a16090441.

H. Ben Rjeb, L. Sliman, H. Zorgati, R. Ben Djemaa, and A. Dhraief, “Optimizing Internet of Things Services Placement in Fog Computing Using Hybrid Recommendation System,” Futur Internet, vol. 17, no. 5, p. 201, Apr. 2025, doi: 10.3390/fi17050201.

B. Premalatha and P. Prakasam, “Optimal Energy-efficient Resource Allocation and Fault Tolerance scheme for task offloading in IoT-FoG Computing Networks,” Comput Networks, vol. 238, p. 110080, Jan. 2024, doi: 10.1016/j.comnet.2023.110080.

D. Sahu et al., “Adaptive fault tolerance mechanisms for ensuring high availability of digital twins in distributed edge computing systems,” Sci Rep, vol. 15, no. 1, p. 41676, Nov. 2025, doi: 10.1038/s41598-025-25590-4.

Downloads

Published

26-12-2025

How to Cite

Gupta, S., & Singh, N. (2025). Optimized Fault-Tolerant Placement of Microservices in Distributed Fog Networks. International Journal of Data Informatics and Intelligent Computing, 4(4), 52–69. https://doi.org/10.59461/ijdiic.v4i4.241

Issue

Section

Regular Issue