Generalized Statistical Indicators For Cloud Computing Fault Tolerance


  • Subramanyam M Vadlamaani Research Scholar, Department of CSE, Shri Venkateshwara University, Gajraula, UP
  • P K Bharti Vice-chancellor, Shri Venkateshwara University, Gajraula, UP
  • M. Rudra Kumar Professor, Department of CSE, GPCET, Kurnool


Cloud Computing Indicator Patterns, Computer Programming, Fault Tolerance, Heikin Ashi Cloud Indicators, KPI indicators for Cloud Computing


Contemporary information and transaction processing systems mainly rely on cloud computing for their infrastructure. A shared pool of reconfigurable computing resources (such as networks, servers, storage, applications, and services) may be quickly deployed and released with no administration labour or service disruption thanks to cloud computing, which offers universal, practical, on-demand network connectivity. Due to the increasing need for more fault-tolerant and high-availability cloud computing servers, server managers must increasingly concentrate on key performance indicators for these systems. Server administrators are very concerned about the performance of high-availability and fault-tolerance systems since it is usual for unanticipated and unplanned outages to occur often. Based on the sustainability and pattern indicators discussed in earlier research, this study suggests a directional analytic pattern indicator for cloud computing servers. The Heikin Ashi charting pattern, which is akin to a Japanese candlestick pattern, is used by the Generalized Statistical Indicators for Fault Tolerance (GSI-FT) to identify the patterns in service consumption across various time periods. The preceding research's ISO/IEC 30134-1 and 30134-2-Datacenters-Main performance indicators, which examined the internet bandwidth frequency for high availability and fault tolerance movements, were used as the basis for the experiments. Results of testing have demonstrated that GSI-FT may function as a directional indicator to identify the pattern frequencies of the performance indicators and offer details on fault tolerance by giving observation frequency, performance trend, and awareness level for this pattern. The red candlesticks show the model's progress toward fault tolerance, while the green symbolises the model's high availability. A charting model that has evolved over time over the pattern shows the model's capacity to depict server-level signals that are plausible using our methodology.


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Xu, Dong. "Cloud computing: An emerging technology." In 2010 International Conference On Computer Design andApplications, vol. 1, pp. V1-100. IEEE, 25th June 2010, DOI: 10.1109/ICCDA.2010.5541105.

Shahidinejad, Ali, Mostafa Ghobaei-Arani, and Mohammad Masdari. "Resource provisioning using workload clustering in cloud computing environment: a hybrid approach." Cluster Computing, 24.1, March (2021): 319-342, DOI:

Chinnaiah, Mylara Reddy, and Nalini Niranjan. “Fault tolerant software systems using software configurations for cloud computing.” Journal of Cloud Computing, 7.1, 7th December (2018): 1-17, DOI:

Sai, M. P. ., V. A. . Rao, K. . Vani, and P. . Poul. “Prediction of Housing Price and Forest Cover Using Mosaics With Uncertain Satellite Imagery”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 8, Aug. 2022, pp. 36-46, doi:10.17762/ijritcc.v10i8.5666.

Kumar, Madapuri Rudra, et al. “Change Request Impacts in Software Maintenance.” CRC Press, 6th August 2020.

Hasan, Moin, and Major Singh Goraya. “Fault tolerance in cloud computing environment: A systematic survey.” Computers in Industry 99, 1st August (2018): 156-172,

Kero, A., Khanna, A., Kumar, D., & Agarwal, A. An Adaptive Approach Towards Computation Offloading for Mobile Cloud Computing. International Journal of Information Technology and Web Engineering (IJITWE), 14(2), 1st April 2019, 52-73,

Vale, Kádna Maria Alves Camboim, and Fernanda Maria Ribeiro de Alencar. "Challenges, patterns and sustainability indicators for cloud computing." Brazilian Journal of Development, 6, no. 8, 14th August (2020): 57031-57053, DOI:

Madapudi, Rudra Kumar, A. Ananda Rao, and Gopichand Merugu. "Change requests artifacts to assess impact on structural design of SDLC phases." Int’l J. Computer Applications, 54, no. 18 (2012): 21-26,

Jhawar, Ravi, and Vincenzo Piuri. "Fault tolerance and resilience in cloud computing environments." In Computer and information security handbook, pp. 165-181. Morgan Kaufmann, 2017,

Padmakumari, P., and A. Umamakeswari. "Methodical review on various fault tolerant and monitoring mechanisms to improve reliability on cloud environment." Indian Journal of Science and Technology, 8, no. 35, 20th December (2015): 1-6,

Gill, D. R. . (2022). A Study of Framework of Behavioural Driven Development: Methodologies, Advantages, and Challenges. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 09–12.

Hasan, Moin, and Major Singh Goraya. "Fault tolerance in cloud computing environment: A systematic survey." Computers in Industry, 99, 1st August (2018): 156-172,

Kaur, Satjot, and Gurbhej Singh. "Review on Fault Tolerance Techniques in Cloud Computing." International Journal of Engineering and Management Research (IJEMR), 7, no. 3 (2017): 69-71,

Kumari, Priti, and Parmeet Kaur. "A survey of fault tolerance in cloud computing." Journal of King Saud University-Computer and Information Sciences, 33, no. 10, 1st December (2021): 1159-1176,

Tebaa, Maha, and Said EL Hajji. "From single to multi-clouds computing privacy and fault tolerance." IERI procedia, 10, 1st January (2014): 112-118,

Linda R. Musser. (2020). Older Engineering Books are Open Educational Resources. Journal of Online Engineering Education, 11(2), 08–10. Retrieved from

Jhawar, Ravi, Vincenzo Piuri, and Marco Santambrogio. "Fault tolerance management in cloud computing: A system-level perspective." IEEE Systems Journal, 7, no. 2, (2012): 288-297, DOI: 10.1109/JSYST.2012.2221934 .

Das, Pranesh, and Pabitra Mohan Khilar. "VFT: A virtualization and fault tolerance approach for cloud computing." In 2013 IEEE Conference on Information & Communication Technologies, pp. 473-478. IEEE, 11th April 2013, DOI: 10.1109/CICT.2013.6558142.

Shaikh, Anwar Ahamed, and Sheesh Ahmad. "Fault tolerance management for cloud environment: a critical review." International Journal of Advanced Research in Computer Science, 9, Issue 2, (2018): 34,

Modiya, P., & Vahora, S. (2022). Brain Tumor Detection Using Transfer Learning with Dimensionality Reduction Method. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 201–206. Retrieved from

Yang, Chao-Tung, Jung-Chun Liu, Ching-Hsien Hsu, and Wei-Li Chou. "On improvement of cloud virtual machine availability with virtualization fault tolerance mechanism." The Journal of Supercomputing, 69, no. 3, September (2014): 1103-1122, DOI:

Agarwal, Himanshu, and Anju Sharma. "A comprehensive survey of fault tolerance techniques in cloud computing." In 2015 International Conference on Computing and Network Communications (CoCoNet), pp. 408-413. IEEE, 16th December 2015, DOI: 10.1109/CoCoNet.2015.7411218 .

Madapuri, Rudra Kumar, and P. C. Mahesh. "HBS-CRA: scaling impact of change request towards fault proneness: defining a heuristic and biases scale (HBS) of change request artifacts (CRA)." Cluster Computing 22.5 (2019): 11591-11599

Katuk, N., & Chiadighikaobi, I. R. (2022). An Enhanced Block Pre-processing of PRESENT Algorithm for Fingerprint Template Encryption in the Internet of Things Environment. International Journal of Communication Networks and Information Security (IJCNIS), 13(3).

Trivedi, Smita Roy. “Technical Analysis Strategies: Development of Heiken Ashi Stochastic.”, MPRA, (2018), pp. 1-5,

Rudra Kumar, M., Rashmi Pathak, and Vinit Kumar Gunjan. "Machine Learning-Based Project Resource Allocation Fitment Analysis System (ML-PRAFS)." Computational Intelligence in Machine Learning. Springer, Singapore, 2022. 1-14

Rosemaro, E. . (2022). Understanding the Concept of Entrepreneurship Management and Its Contribution in Organization. International Journal of New Practices in Management and Engineering, 11(01), 24–30.

Kumar, M. Rudra, Rashmi Pathak, and Vinit Kumar Gunjan. "Diagnosis and Medicine Prediction for COVID-19 Using Machine Learning Approach." Computational Intelligence in Machine Learning: Select Proceedings of ICCIML 2021 834 (2022): 123

Nishchal Sharma, Dr, and Chaman S. Chauhan. “Heikin-Ashi Transformation and Its Effect on Neural Network Learning for Stock Market Data.” (2016).,and%20green%20during%20an%20UpTrend.

Suneel, Chenna Venkata, K. Prasanna, and M. Rudra Kumar. "Frequent data partitioning using parallel mining item sets and MapReduce." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2.4 (2017)

Roy Trivedi, Smita. "Technical analysis using Heiken Ashi Stochastic: To catch a trend, use a HASTOC." International Journal of Finance & Economics, 27, no. 2 (2022): 1836-1847,

Heikin ashi trend indicators




How to Cite

Vadlamaani , S. M., Bharti, P. K. ., & Kumar, M. R. . (2022). Generalized Statistical Indicators For Cloud Computing Fault Tolerance. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 269 –. Retrieved from



Research Article

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