Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments

A. Sankar, J. Wang, A. Krishnan, and H. Sundaram, “Beyond localized graph neural networks: An attributed motif regularization framework,” in

2020

Earlier work this paper cites.

A. Sankar, J. Wang, A. Krishnan, and H. Sundaram, “Protocf: Prototypical collaborative filtering for few-shot recommendation,” in

2021

Earlier work this paper cites.

L. Chen, Y. Lu, C.-T. Wu, R. Clarke, G. Yu, J. E. Van Eyk, D. M. Herrington, and Y. Wang, “Data-driven detection of subtype-specific differentially expressed genes,”

2021

Earlier work this paper cites.

M. A. Shahid, N. Islam, M. M. Alam, M. Mazliham, and S. Musa, “Towards resilient method: An exhaustive survey of fault tolerance methods in the cloud computing environment,”

2021

Earlier work this paper cites.

P. Kumari and P. Kaur, “Checkpointing algorithms for fault-tolerant execution of large-scale distributed applications in cloud,”

2021

Earlier work this paper cites.

H. S. Dhiman, D. Deb, S. Muyeen, and I. Kamwa, “Wind turbine gearbox anomaly detection based on adaptive threshold and twin support vector machines,”

2021

Earlier work this paper cites.

T. M. Tawfeeg, A. Yousif, A. Hassan, S. M. Alqhtani, R. Hamza, M. B. Bashir, and A. Ali, “Cloud dynamic load balancing and reactive fault tolerance techniques: a systematic literature review (slr),”

2022

Earlier work this paper cites.

Y. Lu, C.-T. Wu, S. J. Parker, Z. Cheng, G. Saylor, J. E. Van Eyk, G. Yu, R. Clarke, D. M. Herrington, and Y. Wang, “Cot: an efficient and accurate method for detecting marker genes among many subtypes,”

2022

Earlier work this paper cites.

M. Mudassar, Y. Zhai, and L. Lejian, “Adaptive fault-tolerant strategy for latency-aware iot application executing in edge computing environment,”

2022

Earlier work this paper cites.

Y. Wang, S. Shi, X. He, Z. Tang, X. Pan, Y. Zheng, X. Wu, A. C. Zhou, B. He, and X. Chu, “Reliable and efficient in-memory fault tolerance of large language model pretraining,”

Original

2023

Earlier work this paper cites.

J. Wang, P. Rathi, and H. Sundaram, “A pre-trained zero-shot sequential recommendation framework via popularity dynamics,” in

2024

Earlier work this paper cites.

H. Xu, X. Wang, and H. Chen, “Towards real-time and personalized code generation,” in

2024

Earlier work this paper cites.

Y. Ji, W. Ma, S. Sivarajkumar, H. Zhang, E. M. Sadhu, Z. Li, X. Wu, S. Visweswaran, and Y. Wang, “Mitigating the risk of health inequity exacerbated by large language models,”

Original

2024

Cited alongside, same era.

Q. Deng, Q. Yang, R. Yuan, Y. Huang, Y. Wang, X. Liu, Z. Tian, J. Pan, G. Zhang, H. Lin

Original

2024

Cited alongside, same era.

Z. Ding, P. Li, Q. Yang, and S. Li, “Enhance image-to-image generation with llava-generated prompts,” in

2024

Cited alongside, same era.

Z. Li, L. Zheng, B. Jin, D. Fu, B. Jing, Y. Ban, J. He, and J. Han, “Can graph neural networks learn language with extremely weak text supervision?”

Original

2024

Cited alongside, same era.

D. Fu, L. Fang, Z. Li, H. Tong, V. I. Torvik, and J. He, “Parametric graph representations in the era of foundation models: A survey and position,”

X. Hou, Y. Zhao, Y. Liu, Z. Yang, K. Wang, L. Li, X. Luo, D. Lo, J. Grundy, and H. Wang, “Large language models for software engineering: A systematic literature review,”

2024

Later among the works it cites.

D. Liu, “Contemporary model compression on large language models inference,”

Original

2024

Later among the works it cites.

G. Bai, J. Liu, X. Bu, Y. He, J. Liu, Z. Zhou, Z. Lin, W. Su, T. Ge, B. Zheng

Original

2024

Later among the works it cites.

S. Li, Y. He, H. Guo, X. Bu, G. Bai, J. Liu, J. Liu, X. Qu, Y. Li, W. Ouyang

Original

2024

Later among the works it cites.

Y. Fu, Y. Lu, Y. Wang, B. Zhang, Z. Zhang, G. Yu, C. Liu, R. Clarke, D. M. Herrington, and Y. Wang, “Ddn3. 0: Determining significant rewiring of biological network structure with differential dependency networks,”