A resilience framework for fault-tolerance in cloud-based microservice applications
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.3.23Keywords:
Bulkhead, little law, Fault tolerance, Auto Retry Circuit Breaker (ARCB), Resilience, framework, microservicesDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Cloud-distributed systems offer significant opportunities for fault-tolerant applications. Microservices have gained significant acceptance as a cloud-based architecture for building fault-tolerant cloud applications. The primary aim of this study is to develop a dependable resilience framework, incorporating appropriate design patterns, that can be applied to any cloud applications. This framework combines a bulkhead utilizing a little law approach and an auto-retry circuit breaker, which can be seen as a fault tolerance pattern. This will eliminate the need for manual setting of design patterns, resulting in maximum throughput, availability of resources and the performance can be increased up to 55.3% from the average execution duration.Abstract
How to Cite
Downloads
Similar Articles
- S. Munawara Banu, M. Mohamed Surputheen, M. Rajakumar, Bio-Inspired and Machine Learning-Driven Multipath Routing Protocol for MANETs Using Predictive Link Analytics , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Priyanka Prajapati, Dipak Makwana, Work-Life Balance, Mental Health, and Sustainable Innovation: A Study of Women in Industry , The Scientific Temper: Vol. 17 No. 03 (2026): The Scientific Temper
- Heena Gulia, Sunder Singh Arya, Neha Yadav, Ajay Kumar, Monika Janaagal, Mamta Sawariya, Naveen Kumar, Himanshu Mehra, Sunil Yadav, Sudershan Singh, Reetu Verma, Strategies for adaptations and mitigation of abiotic stresses in crops: A review , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
- Hemamalini V., Victoria Priscilla C, Deep learning driven image steganalysis approach with the impact of dilation rate using DDS_SE-net on diverse datasets , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- N. Anbarasi, K. Anitha, S. Hemalatha, A study on energy sum of dominating sets in East Indian states , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- V. Mahalakshmi, M. Manimekalai, Location Specific Paddy Yield Prediction using Monte Carlo Simulation incorporated Long Short-Term Memory , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- A. Angelpreethi, M. Lakshmi Priya, R. Kavitha, DeepPre-OM: An Enhanced Pre-processing Framework for Opinion Classification of Microblog Data , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Merlin Sofia S, D. Ravindran, G. Arockia Sahaya Sheela, Clean Balance-Ensemble CHD: A Balanced Ensemble Learning Framework for Accurate Coronary Heart Disease Prediction , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- A. Pappa, P. Muruganantham, A. Nagoor Gani, Properties on semi-ring of fuzzy matrices with compatible norm , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Merla Agnes Mary, S. Britto Ramesh Kumar, DAJO: A Robust Machine Learning–Based Framework for Preprocessing and Denoising Fetal ECG Signals , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

