1. Ningning S. et al, “Fog computing dynamic load balancing mechanism based on graph repartitioning,” China Communications, vol. 13, 2016, pp. 156–164.
2. Varghese B. and Buyya R., “Next generation cloud computing: New trends and research directions,” Future Generation Computer Systems, vol. 79, 2018, pp. 849–861.
3. A. Noronha et al, “Attaining IoT Value: How to move from Connecting Things to Capturing Insight,” White paper, Cisco, 2014.
4. U. Ozeer et al, “Resilience of Stateful IoT Applications in a Dynamic Fog Environment,” in Proc. of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 5–7 Nov., New York City, USA, 2018, pp.1-10.
5. Ren S. and van der Schaar M., “Dynamic scheduling and pricing in wireless cloud computing,” IEEE Transactions on Mobile Computing, vol. 13, no. 10, pp. 2283–2292, 2014.
6. The LLOYD’s Emerging Risk Report 2018 Technology [Online]. https://www.lloyds.com/~/media/files/news-and-insight/risk-insight/2018/cloud-down/aircyberlloydspublic2018final.pdf.
7. Zhang J., “Overview on Fault Tolerance Strategies of Composite Service in Service Computing,” Wireless Communications and Mobile Computing, vol. 2018, 2018, pp. 1–8.
8. Hasan M. and Goraya M. S., “Fault tolerance in cloud computing environment: a systematic survey,” Computers in Industry, vol. 99, pp. 156–172, 2018.
9. https://aws.amazon.com/premiumsupport/knowledge-center/autoscaling-fault-tolerance-load-balancer/. Accessed Jan. 12, 2019.
10. Szpuszta M., Vaitinadin S., “Microsoft Azure—Fault Tolerance Pitfalls and Resolutions in the Cloud,” MSDN Magazine Blog, vol. 30, no. 9, 2015.
11. Amoon M., “A job checkpointing system for computational grids,” Open Computer Science, vol. 3, 2013, pp. 17–26.
12. Abdulhamid S. et al, “Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm,” Neural Computing and Applications, vol. 29, 2018, pp. 279–293.
13. Liu Y., Fieldsend J. and Min G., “A Framework of Fog Computing: Architecture, Challenges and Optimization,” IEEE Access, vol. 5, 2017, pp. 25445–25454.
14. I. Goiri, F. Julià, J. Guitart, and J. Torres, “Checkpoint-based fault-tolerant infrastructure for virtualized service providers,” in Proc. of the 12th IEEE/IFIP Network Operations and Management Symposium (NOMS’10), Osaka, Japan, 2010, pp. 455–462.
15. J. Cao et al, “Checkpointing as a Service in Heterogeneous Cloud Environments,” in Proc. of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Shenzhen, Guangdong, China, 2015, pp. 61–70.
16. Abdulhamid S. and Abd Latiff M., “A Checkpointed League Championship Algorithm-Based Cloud Scheduling Scheme with Secure Fault Tolerance Responsiveness,” Applied Soft Computing, vol. 61, 2017, pp. 670–680.
17. Louatia T., Abbesa H., Cérinb C. and Jemnia M., “LXCloud-CR: Towards LinuX Containers Distributed Hash Table based Checkpoint-Restart,” Journal of Parallel Distributed Computing, vol. 111, 2018, pp. 187–205.
18. P. Das and P. M. Khilar, “VFT: A Virtualization and Fault Tolerance Approach for Cloud Computing,” in Proc. of the 2013 IEEE Conference on Information and Communication Technologies, Thuckalay, Tamil Nadu, India, 2013, pp. 473–478.
19. A. Alhosban et al, “Self-healing Framework for Cloud-based Services,” in Proc. of the 2013 Int’l Conf. on Computer Systems and Applications, Ifrane, Morocco, 2013.
20. Saranya S. et al, “Enhanced Fault Tolerance and Cost Reduction using Task Replication using Spot Instances in Cloud,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, 2015, pp. 12–16.
21. Zhu X. et al, “Fault-Tolerant Scheduling for Real-Time Scientific Workflows with Elastic Resource Provisioning in Virtualized Clouds,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, Issue 12, Dec. 2016, pp. 3501–3517.
22. V. Souza et al, “Proactive vs. Reactive Failure Recovery Assessment in Combined Fog-to-Cloud (F2C) Systems,” in Proc. of IEEE 22nd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Lund, Sweden, 2017, pp. 1–5.
23. Wang K. et al, “Adaptive and Fault-tolerant Data Processing in Healthcare IoT Based on Fog Computing,” IEEE Transactions on Network Science and Engineering, 2018, https://doi.org/10.1109/tnse.2018.2859307.
24. Dantu K., Ko S. and Ziarek L., “RAINA: Reliability and Adaptability in Android for Fog Computing,” IEEE Communications Magazine, vol. 55, 2017, pp. 41–45.
25. R. Oma et al, “Fault-Tolerant Fog Computing Models in the IoT,” in Proc. of the 13th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2018), October 27–29, Tunghai University, Taichung, Taiwan, pp. 14–25.
26. Amoon Mohammed, “Adaptive Framework for Reliable Cloud Computing Environment,” IEEE Access, vol. 4, 2016, pp. 9469–9478.
27. Wei Y., Qiu J., Lam H., and Wu L., ‘‘Approaches to T-S fuzzy affine-model-based reliable output feedback control for nonlinear Ito stochastic systems,” IEEE Trans. Fuzzy Syst., vol. 25, issue 3, 2017, pp. 569–583.
28. Gupta el al H., “iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in the Internet of Things, Edge and Fog Computing Environments,” Software: Practice and Experience, vol. 47, 2017, pp. 1275–1296.
29. J. Byrne et al, “Recap Simulator: Simulation of Cloud/Edge/Fog Computing Scenarios,” in Proc. of the 2017 Winter Simulation Conference, Las Vegas, NV, USA, 2017, pp. 4568–4569.
30. M. Lopes et al, “MyiFogSim: A Simulator for Virtual Machine Migration in Fog Computing,” in Proc. of the10th International Conference on Utility and Cloud Computing, Austin, Texas, USA, 2017, pp. 47–52.