Fault-Tolerant Distributed Computing for Real-Time Applications in Critical Systems
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Distributed computing particularly, fault tolerant systems has indispensable functionality in maintaining the dependability and availability of the actual time applications across various sectors including but not limited to healthcare, aerospace, transportation, and industrial control systems. Such systems should run continuously, though there may be equipment problems or network interruptions and software glitches. The major concepts, ways and issues concerning fault-tolerant distributed computing for real time applications in safety critical systems have been discussed in this paper. They include redundancy, replication, consensus algorithms, error detection, and recovery strategies, about which the course notes stress how they ensure that system integrity is sustained during failure modes in addition to satisfying real-time constraints. Exploiting case analysis, we consider fault-tolerant application of these approaches in different sectors as critical environments with an acute necessity for fault-tolerance mechanisms. The paper also presents present day problems such as scalability, performance in fault conditions, and the effectiveness/cost ratio. Last, a consideration of future work in self-organizing and self-healing frameworks that incorporate machine learning, quantum computing, and such other related technologies aimed at achieving better fault tolerance for real-time, distributed systems is made. The role of building and designing infallible, high availability system redundancy models for the assurance of safety, speed, and uninterruptible functionality of such systems is further highlighted by this work.
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1. Rubel, P., Gillen, M., Loyall, J., Schantz, R., Gokhale, A., Balasubramanian, J., ... & Narasimhan, P. (2007, October). Fault tolerant approaches for distributed real-time and embedded systems. In MILCOM 2007-IEEE Military Communications Conference (pp. 1-8). IEEE.
2. Mehrotra, R., Dubey, A., Abdelwahed, S., & Rowland, K. W. (2011). Rfdmon: A real-time and fault-tolerant distributed system monitoring approach. Isis, 11, 107.
3. Krishna, C. M. (2014). Fault-tolerant scheduling in homogeneous real-time systems. ACM Computing Surveys (CSUR), 46(4), 1-34.
4. Pathan, R. M. (2014). Fault-tolerant and real-time scheduling for mixed-criticality systems. Real-Time Systems, 50, 509-547.
5. Thekkilakattil, A., Dobrin, R., & Punnekkat, S. (2014, July). Mixed criticality scheduling in fault-tolerant distributed real-time systems. In 2014 International conference on embedded systems (ICES) (pp. 92-97). IEEE.
6. Malik, S., & Huet, F. (2011, July). Adaptive fault tolerance in real time cloud computing. In 2011 IEEE World Congress on services (pp. 280-287). IEEE.
7. Poledna, S. (2007). Fault-tolerant real-time systems: The problem of replica determinism (Vol. 345). Springer Science & Business Media.
8. Avresky, D. R., & Kaeli, D. R. (Eds.). (2012). Fault-tolerant parallel and distributed systems. Springer Science & Business Media.
9. Gorender, S., Macedo, R. J. D. A., & Raynal, M. (2007). An adaptive programming model for fault-tolerant distributed computing. IEEE Transactions on Dependable and Secure Computing, 4(1), 18-31.
10. Luo, W., Qin, X., Tan, X. C., Qin, K., & Manzanares, A. (2009). Exploiting redundancies to enhance schedulability in fault-tolerant and real-time distributed systems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39(3), 626-639.
11. Alam, K., Mostakim, M. A., & Khan, M. S. I. (2017). Design and Optimization of MicroSolar Grid for Off-Grid Rural Communities. Distributed Learning and Broad Applications in Scientific Research, 3.
12. Integrating solar cells into building materials (Building-Integrated Photovoltaics-BIPV) to turn buildings into self-sustaining energy sources. Journal of Artificial Intelligence Research and Applications, 2(2).
13. Agarwal, A. V., & Kumar, S. (2017, November). Unsupervised data responsive based monitoring of fields. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 184-188). IEEE.
14. Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.
15. Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).
16. Agarwal, A. V., & Kumar, S. (2017, October). Intelligent multi-level mechanism of secure data handling of vehicular information for post-accident protocols. In 2017 2nd International Conference on Communication and Electronics Systems (ICCES) (pp. 902-906). IEEE.
17. Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R. (2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia: case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
18. Shakibaie-M, B. (2013). Comparison of the effectiveness of two different bone substitute materials for socket preservation after tooth extraction: a controlled clinical study. International Journal of Periodontics & Restorative Dentistry, 33(2).
19. Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the treatment of acute hyponatremia associated with acute kidney injury. Case reports in nephrology, 2013(1), 801575.
20. Shilpa, Lalitha, Prakash, A., & Rao, S. (2009). BFHI in a tertiary care hospital: Does being Baby friendly affect lactation success?. The Indian Journal of Pediatrics, 76, 655-657.
21. Singh, V. K., Mishra, A., Gupta, K. K., Misra, R., & Patel, M. L. (2015). Reduction of microalbuminuria in type-2 diabetes mellitus with angiotensin-converting enzyme inhibitor alone and with cilnidipine. Indian Journal of Nephrology, 25(6), 334-339.
22. Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and survival of patients with eccrine carcinoma: a cohort study. Journal of the American Academy of Dermatology, 75(1), 215-217.
23. Lin, L. I., & Hao, L. I. (2024). The efficacy of niraparib in pediatric recurrent PFA⁃ type ependymoma. Chinese Journal of Contemporary Neurology & Neurosurgery, 24(9), 739.
24. Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental Sciences, 2(43), 8251-8255.
25. Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking Malignancy: A Case Report. tuberculosis, 14, 15.
26. Krishnan, S., Shah, K., Dhillon, G., & Presberg, K. (2016). 1995: FATAL PURPURA FULMINANS AND FULMINANT PSEUDOMONAL SEPSIS. Critical Care Medicine, 44(12), 574.
27. Krishnan, S. K., Khaira, H., & Ganipisetti, V. M. (2014, April). Cannabinoid hyperemesis syndrome-truly an oxymoron!. In JOURNAL OF GENERAL INTERNAL MEDICINE (Vol. 29, pp. S328-S328). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.
28. Krishnan, S., & Selvarajan, D. (2014). D104 CASE REPORTS: INTERSTITIAL LUNG DISEASE AND PLEURAL DISEASE: Stones Everywhere!. American Journal of Respiratory and Critical Care Medicine, 189, 1.
29. Mahmud, U., Alam, K., Mostakim, M. A., & Khan, M. S. I. (2018). AI-driven micro solar power grid systems for remote communities: Enhancing renewable energy efficiency and reducing carbon emissions. Distributed Learning and Broad Applications in Scientific Research, 4.
30. Nagar, G. (2018). Leveraging Artificial Intelligence to Automate and Enhance Security Operations: Balancing Efficiency and Human Oversight. Valley International Journal Digital Library, 78-94.
31. Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.
32. Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).
33. Agarwal, A. V., Verma, N., & Kumar, S. (2018). Intelligent Decision Making Real-Time Automated System for Toll Payments. In Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017 (pp. 223-232). Springer Singapore
34. Gadde, H. (2019). Integrating AI with Graph Databases for Complex Relationship Analysis. International
35. Gadde, H. (2019). AI-Driven Schema Evolution and Management in Heterogeneous Databases. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 10(1), 332-356.
36. Gadde, H. (2019). Exploring AI-Based Methods for Efficient Database Index Compression. Revista de Inteligencia Artificial en Medicina, 10(1), 397-432.
37. Alam, K., Mostakim, M. A., & Khan, M. S. I. (2017). Design and Optimization of MicroSolar Grid for Off-Grid Rural Communities. Distributed Learning and Broad Applications in Scientific Research, 3.
38. Integrating solar cells into building materials (Building-Integrated Photovoltaics-BIPV) to turn buildings into self-sustaining energy sources. Journal of Artificial Intelligence Research and Applications, 2(2).
39. Agarwal, A. V., & Kumar, S. (2017, November). Unsupervised data responsive based monitoring of fields. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 184-188). IEEE.
40. Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.
41. Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).
42. Agarwal, A. V., & Kumar, S. (2017, October). Intelligent multi-level mechanism of secure data handling of vehicular information for post-accident protocols. In 2017 2nd International Conference on Communication and Electronics Systems (ICCES) (pp. 902-906). IEEE.
43. Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R. (2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia: case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
44. Shakibaie-M, B. (2013). Comparison of the effectiveness of two different bone substitute materials for socket preservation after tooth extraction: a controlled clinical study. International Journal of Periodontics & Restorative Dentistry, 33(2).
45. Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the treatment of acute hyponatremia associated with acute kidney injury. Case reports in nephrology, 2013(1), 801575.
46. Shilpa, Lalitha, Prakash, A., & Rao, S. (2009). BFHI in a tertiary care hospital: Does being Baby friendly affect lactation success?. The Indian Journal of Pediatrics, 76, 655-657.
47. Singh, V. K., Mishra, A., Gupta, K. K., Misra, R., & Patel, M. L. (2015). Reduction of microalbuminuria in type-2 diabetes mellitus with angiotensin-converting enzyme inhibitor alone and with cilnidipine. Indian Journal of Nephrology, 25(6), 334-339.
48. Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and survival of patients with eccrine carcinoma: a cohort study. Journal of the American Academy of Dermatology, 75(1), 215-217.
49. Lin, L. I., & Hao, L. I. (2024). The efficacy of niraparib in pediatric recurrent PFA⁃ type ependymoma. Chinese Journal of Contemporary Neurology & Neurosurgery, 24(9), 739.
50. Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental Sciences, 2(43), 8251-8255.
51. Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking Malignancy: A Case Report. tuberculosis, 14, 15.
52. Krishnan, S., Shah, K., Dhillon, G., & Presberg, K. (2016). 1995: FATAL PURPURA FULMINANS AND FULMINANT PSEUDOMONAL SEPSIS. Critical Care Medicine, 44(12), 574.
53. Krishnan, S. K., Khaira, H., & Ganipisetti, V. M. (2014, April). Cannabinoid hyperemesis syndrome-truly an oxymoron!. In JOURNAL OF GENERAL INTERNAL MEDICINE (Vol. 29, pp. S328-S328). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.
54. Krishnan, S., & Selvarajan, D. (2014). D104 CASE REPORTS: INTERSTITIAL LUNG DISEASE AND PLEURAL DISEASE: Stones Everywhere!. American Journal of Respiratory and Critical Care Medicine, 189, 1.
55. Mahmud, U., Alam, K., Mostakim, M. A., & Khan, M. S. I. (2018). AI-driven micro solar power grid systems for remote communities: Enhancing renewable energy efficiency and reducing carbon emissions. Distributed Learning and Broad Applications in Scientific Research, 4.
56. Nagar, G. (2018). Leveraging Artificial Intelligence to Automate and Enhance Security Operations: Balancing Efficiency and Human Oversight. Valley International Journal Digital Library, 78-94.
57. Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.
58. Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).
59. Agarwal, A. V., Verma, N., & Kumar, S. (2018). Intelligent Decision Making Real-Time Automated System for Toll Payments. In Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017 (pp. 223-232). Springer Singapore
60. Gadde, H. (2019). Integrating AI with Graph Databases for Complex Relationship Analysis. International
61. Gadde, H. (2019). AI-Driven Schema Evolution and Management in Heterogeneous Databases. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 10(1), 332-356.
62. Gadde, H. (2019). Exploring AI-Based Methods for Efficient Database Index Compression. Revista de Inteligencia Artificial en Medicina, 10(1), 397-432.
63. Han, J., Yu, M., Bai, Y., Yu, J., Jin, F., Li, C., ... & Li, L. (2020). Elevated CXorf67 expression in PFA ependymomas suppresses DNA repair and sensitizes to PARP inhibitors. Cancer Cell, 38(6), 844-856.
64. Maddireddy, B. R., & Maddireddy, B. R. (2020). Proactive Cyber Defense: Utilizing AI for Early Threat Detection and Risk Assessment. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 64-83.
65. Maddireddy, B. R., & Maddireddy, B. R. (2020). AI and Big Data: Synergizing to Create Robust Cybersecurity Ecosystems for Future Networks. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 40-63.
66. Damaraju, A. (2020). Social Media as a Cyber Threat Vector: Trends and Preventive Measures. Revista Espanola de Documentacion Cientifica, 14(1), 95-112
67. Chirra, B. R. (2020). Enhancing Cybersecurity Resilience: Federated Learning-Driven Threat Intelligence for Adaptive Defense. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 260-280.
68. Chirra, B. R. (2020). Securing Operational Technology: AI-Driven Strategies for Overcoming Cybersecurity Challenges. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 281-302.
69. Chirra, B. R. (2020). Advanced Encryption Techniques for Enhancing Security in Smart Grid Communication Systems. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 208-229.
70. Chirra, B. R. (2020). AI-Driven Fraud Detection: Safeguarding Financial Data in Real-Time. Revista de Inteligencia Artificial en Medicina, 11(1), 328-347.
71. Goriparthi, R. G. (2020). AI-Driven Automation of Software Testing and Debugging in Agile Development. Revista de Inteligencia Artificial en Medicina, 11(1), 402-421.
72. Goriparthi, R. G. (2020). Neural Network-Based Predictive Models for Climate Change Impact Assessment. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 421-421.
73. Reddy, V. M., & Nalla, L. N. (2020). The Impact of Big Data on Supply Chain Optimization in Ecommerce. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 1-20.
74. Nalla, L. N., & Reddy, V. M. (2020). Comparative Analysis of Modern Database Technologies in Ecommerce Applications. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 21-39.
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