Towards Real-Time Automated Failure Detection and Self-Healing Mechanisms in Cloud Environments: A Comparative Analysis of Existing Systems
Keywords:
automated failure detection, self-healing mechanisms, cloud environments, comparative analysisAbstract
Automated failure detection and self-healing mechanisms are crucial components of modern cloud environments, ensuring continuous availability and reliability of services. This research paper presents a comparative analysis of existing systems aimed at achieving real-time automated failure detection and self-healing in cloud environments.
The paper begins by emphasizing the importance of rapid failure detection and mitigation in cloud computing, highlighting the potential impact of downtime on business operations and user experience. Traditional approaches to failure detection and recovery often rely on manual intervention or reactive strategies, leading to increased response times and service disruptions.
Next, the paper surveys existing systems and frameworks designed to address the challenges of automated failure detection and self-healing in cloud environments. These systems encompass a variety of approaches, including rule-based systems, machine learning-based anomaly detection, and proactive fault tolerance mechanisms. Each approach offers unique advantages and trade-offs in terms of accuracy, scalability, and resource overhead.
A comparative analysis of these systems is conducted based on several key criteria, including:
- Detection Accuracy: The ability to accurately identify failures or anomalies in real-time, minimizing false positives and false negatives.
- Response Time: The speed at which the system can detect failures and initiate self-healing actions, reducing service downtime and impact on users.
- Scalability: The ability to scale with increasing workloads and infrastructure size, ensuring consistent performance under varying conditions.
- Resource Overhead: The computational and storage resources required to deploy and operate the system, optimizing efficiency and cost-effectiveness.
- Robustness: The resilience of the system to handle diverse failure scenarios and environmental changes, ensuring reliable operation in dynamic cloud environments.
Based on the comparative analysis, the paper identifies strengths and limitations of existing systems and provides insights into emerging trends and future directions in the field of automated failure detection and self-healing in cloud environments.
Furthermore, the paper discusses practical considerations and challenges associated with deploying and integrating these systems into existing cloud infrastructures. These include data collection and preprocessing, model training and evaluation, integration with orchestration frameworks, and coordination of self-healing actions across distributed systems.
Real-world case studies and examples are presented to illustrate the application of automated failure detection and self-healing mechanisms in cloud environments. These case studies demonstrate how organizations have leveraged these systems to enhance availability, reduce operational overhead, and improve overall system resilience.
In conclusion, automated failure detection and self-healing mechanisms play a critical role in ensuring the reliability and availability of cloud services. By conducting a comparative analysis of existing systems and understanding their strengths and limitations, organizations can make informed decisions about selecting and deploying appropriate solutions for their cloud environments.
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References
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
A. T. Velte, T. J. Velte, and R. Elsenpeter, "Cloud Computing: A Practical Approach," McGraw-Hill, 2010.
H. T. Dinh, C. Lee, D. Niyato, and P. Wang, "A survey of mobile cloud computing: architecture, applications, and approaches," Wireless Communications and Mobile Computing, vol. 13, no. 18, pp. 1587-1611, 2013.
M. Armbrust et al., "A view of cloud computing," Communications of the ACM, vol. 53, no. 4, pp. 50-58, 2010.
R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, "Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility," Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.
K. Hwang, G. C. Fox, and J. J. Dongarra, "Distributed and Cloud Computing: From Parallel Processing to the Internet of Things," Morgan Kaufmann, 2011.
Y. Jadeja and K. Modi, "Cloud computing - concepts, architecture and challenges," 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 877-880, 2012.
L. Liu et al., "A survey of cloud storage systems," 2011 IEEE 10th International Conference on Networks, pp. 9-13, 2011.
Q. Zhang, L. Cheng, and R. Boutaba, "Cloud computing: state-of-the-art and research challenges," Journal of Internet Services and Applications, vol. 1, no. 1, pp. 7-18, 2010.
P. Mell and T. Grance, "The NIST definition of cloud computing," NIST Special Publication, vol. 800, no. 145, pp. 1-7, 2011.
A. Avizienis, J.-C. Laprie, B. Randell, and C. Landwehr, "Basic concepts and taxonomy of dependable and secure computing," IEEE Transactions on Dependable and Secure Computing, vol. 1, no. 1, pp. 11-33, 2004.
M. Kwiatkowska, G. Norman, and D. Parker, "Probabilistic model checking: Advances and applications," Formal Methods for the Design of Computer, Communication and Software Systems: Performance Evaluation, pp. 126-151, 2007.
A. Mahmood and E. Khan, "A survey on detecting black hole attack in AODV-based mobile ad hoc networks," 2011 International Conference on Future Information Technology and Management Engineering, pp. 120-124, 2011.
L. Wu, S. K. Garg, and R. Buyya, "SLA-based resource allocation for software as a service provider (SaaS) in cloud computing environments," 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 195-204, 2011.
T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, "A review of auto-scaling techniques for elastic applications in cloud environments," Journal of Grid Computing, vol. 12, no. 4, pp. 559-592, 2014.
M. R. Aslanpour, S. Ghari-Neiat, and P. Srivastava, "Auto-scaling web applications in clouds: A cost-aware approach," 2016 8th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 221-228, 2016.
D. Petcu, "Portability and interoperability between clouds: challenges and case study," 2011 4th IEEE International Conference on Cloud Computing, pp. 62-69, 2011.
Maruthi, Srihari, et al. "Deconstructing the Semantics of Human-Centric AI: A Linguistic Analysis." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 11-30.
Dodda, Sarath Babu, et al. "Ethical Deliberations in the Nexus of Artificial Intelligence and Moral Philosophy." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 31-43.
Maruthi, Srihari, et al. "Toward a Hermeneutics of Explainability: Unraveling the Inner Workings of AI Systems." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 27-44.
Yellu, Ramswaroop Reddy, et al. "AI Ethics-Challenges and Considerations: Examining ethical challenges and considerations in the development and deployment of artificial intelligence systems." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 9-16.
Maruthi, Srihari, et al. "Automated Planning and Scheduling in AI: Studying automated planning and scheduling techniques for efficient decision-making in artificial intelligence." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 14-25.
Dodda, Sarath Babu, et al. "Conversational AI-Chatbot Architectures and Evaluation: Analyzing architectures and evaluation methods for conversational AI systems, including chatbots, virtual assistants, and dialogue systems." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 13-20.
Maruthi, Srihari, et al. "Language Model Interpretability-Explainable AI Methods: Exploring explainable AI methods for interpreting and explaining the decisions made by language models to enhance transparency and trustworthiness." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 1-9.
Dodda, Sarath Babu, et al. "Federated Learning for Privacy-Preserving Collaborative AI: Exploring federated learning techniques for training AI models collaboratively while preserving data privacy." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 13-23.
Maruthi, Srihari, et al. "Temporal Reasoning in AI Systems: Studying temporal reasoning techniques and their applications in AI systems for modeling dynamic environments." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 22-28.
Yellu, Ramswaroop Reddy, et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems." Hong Kong Journal of AI and Medicine 2.2 (2022): 12-20.
Reddy Yellu, R., et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems. Hong Kong Journal of AI and Medicine, 2 (2), 12-20." (2022).
Pulimamidi, Rahul. "To enhance customer (or patient) experience based on IoT analytical study through technology (IT) transformation for E-healthcare." Measurement: Sensors (2024): 101087.
Senthilkumar, Sudha, et al. "SCB-HC-ECC–based privacy safeguard protocol for secure cloud storage of smart card–based health care system." Frontiers in Public Health 9 (2021): 688399.
Singh, Amarjeet, Vinay Singh, and Alok Aggarwal. "Improving the Application Performance by Auto-Scaling of Microservices in a Containerized Environment in High Volumed Real-Time Transaction System." International Conference on Production and Industrial Engineering. Singapore: Springer Nature Singapore, 2023.
S. Calzavara, R. Focardi, and N. Grimm, "Microservices: A new approach to security," IEEE Security & Privacy, vol. 15, no. 5, pp. 80-84, 2017.
S. Distefano, A. Puliafito, and M. Scarpa, "A model-based approach for the run-time evaluation of cloud services," Future Generation Computer Systems, vol. 29, no. 1, pp. 39-49, 2013.
J. Park, S. Kim, and D. Ha, "A model-driven approach to user-centric service adaptation," IEEE Transactions on Services Computing, vol. 6, no. 1, pp. 42-55, 2013.
