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Performability Analysis for Large-Scale Multi-State Computing Systems: Methodologies, Advances, and Future Directions
Review Article  ·  Published: 31 October 2025
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ICCK Transactions on Systems Safety and Reliability
Volume 1, Issue 2, 2025: 81-97
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Performability Analysis for Large-Scale Multi-State Computing Systems: Methodologies, Advances, and Future Directions

by
Yuchang Mo Yuchang Mo 1 * ORCID
Yuan Fan Yuan Fan 2
Chunyu Miao Chunyu Miao 2
Mirlan Chynybaev Mirlan Chynybaev 3 ORCID
Faer Gui Faer Gui 4
Rengui Zhang Rengui Zhang 5
Jianyong Hu Jianyong Hu 6
Jinbin Mu Jinbin Mu 7
Akylbek Chymyrov Akylbek Chymyrov 3 ORCID
1 School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
2 Hangzhou Anheng Information Technology Co., Ltd., Hangzhou 310051, China
3 Razzakov Kyrgyz State Technical University, Bishkek 720044, Kyrgyzstan
4 Zhejiang Keepsoft Information Technology Corp.,Ltd., Hangzhou 310051, China
5 Zhejiang YuGong Information Technology Co., Ltd., Hangzhou 310002, China
6 Engineering Research Center of Digital Twin Basin of Zhejiang Province, Hangzhou 310018, China
7 Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
* Corresponding Author: Yuchang Mo, [email protected]
Volume 1, Issue 2
Volume 1, Issue 2 2025
Received: 09 September 2025 Accepted: 26 September 2025 Published: 31 October 2025 CrossMark
Download PDF 3.18 MB Cite Metrics
DOI: 10.62762/TSSR.2025.527003
ARK: ark:/57805/tssr.2025.527003

Article Information

Published in ICCK Transactions on Systems Safety and Reliability
Volume/Issue Volume 1, Issue 2, 2025
Pages 81-97
Cited by 2 (Crossref) 1 (Scopus)

Abstract

Large-scale computing systems, such as cloud data centers, grid infrastructures, and high-performance computing clusters, are the backbone of modern information technology ecosystems. These systems typically consist of numerous heterogeneous, multi-state computing nodes that exhibit varying performance levels due to component failures, degradation, or dynamic resource allocation. Performability analysis, which integrates both system reliability and performance evaluations to quantify the probability of the system operating at a specified performance level, is critical for ensuring the efficient, reliable, and cost-effective operation of these complex systems. This paper presents a comprehensive review of recent advancements in performability analysis for large-scale multi-state computing systems over the past decade. It classifies existing research into three core methodological categories: binary decision diagram (BDD)-based approaches, multi-valued decision diagram (MDD)-based approaches, and comparative benchmarking with traditional methods (e.g., continuous-time Markov chains (CTMC), universal generating function (UGF)). For each category, the paper details key methodologies, algorithmic innovations, and practical applications. Additionally, the promising future directions are proposed to address emerging challenges, such as handling dynamic system behaviors, integrating real-time data, and optimizing resource allocation for performability. This review provides a valuable reference for researchers, system designers, and operators seeking to enhance the performability of large-scale computing systems and mitigate risks associated with service level agreement (SLA) violations.

Graphical Abstract

Performability Analysis for Large-Scale Multi-State Computing Systems: Methodologies, Advances, and Future Directions

Keywords

performability analysis large-scale computing systems multi-state systems binary decision diagrams (BDD) multi-valued decision diagrams (MDD) reliability system performance

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

Yuan Fan and Chunyu Miao are employees of Hangzhou Anheng Information Technology Co., Ltd., Hangzhou 310051, China; Faer Gui is an employee of Zhejiang Keepsoft Information Technology Corp.,Ltd., Hangzhou 310051, China; Rengui Zhang is an employee of Zhejiang YuGong Information Technology Co., Ltd., Hangzhou 310002, China; Jianyong Hu is an employee of Engineering Research Center of Digital Twin Basin of Zhejiang Province, Hangzhou 310018, China; Jinbin Mu is an employee of Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China.

Ethical Approval and Consent to Participate

Not applicable.

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* Citation data provided by Crossref Cited-by.

Cite This Article

APA Style
Mo, Y., Fan, Y., Miao, C., Chynybaev, M., Gui, F., Zhang, R., Hu, J., Mu, J. & Chymyrov, A. (2025). Performability Analysis for Large-Scale Multi-State Computing Systems: Methodologies, Advances, and Future Directions. ICCK Transactions on Systems Safety and Reliability, 1(2), 81–97. https://doi.org/10.62762/TSSR.2025.527003
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TY  - JOUR
AU  - Mo, Yuchang
AU  - Fan, Yuan
AU  - Miao, Chunyu
AU  - Chynybaev, Mirlan
AU  - Gui, Faer
AU  - Zhang, Rengui
AU  - Hu, Jianyong
AU  - Mu, Jinbin
AU  - Chymyrov, Akylbek
PY  - 2025
DA  - 2025/10/31
TI  - Performability Analysis for Large-Scale Multi-State Computing Systems: Methodologies, Advances, and Future Directions
JO  - ICCK Transactions on Systems Safety and Reliability
T2  - ICCK Transactions on Systems Safety and Reliability
JF  - ICCK Transactions on Systems Safety and Reliability
VL  - 1
IS  - 2
SP  - 81
EP  - 97
DO  - 10.62762/TSSR.2025.527003
UR  - https://www.icck.org/article/abs/TSSR.2025.527003
KW  - performability analysis
KW  - large-scale computing systems
KW  - multi-state systems
KW  - binary decision diagrams (BDD)
KW  - multi-valued decision diagrams (MDD)
KW  - reliability
KW  - system performance
AB  - Large-scale computing systems, such as cloud data centers, grid infrastructures, and high-performance computing clusters, are the backbone of modern information technology ecosystems. These systems typically consist of numerous heterogeneous, multi-state computing nodes that exhibit varying performance levels due to component failures, degradation, or dynamic resource allocation. Performability analysis, which integrates both system reliability and performance evaluations to quantify the probability of the system operating at a specified performance level, is critical for ensuring the efficient, reliable, and cost-effective operation of these complex systems. This paper presents a comprehensive review of recent advancements in performability analysis for large-scale multi-state computing systems over the past decade. It classifies existing research into three core methodological categories: binary decision diagram (BDD)-based approaches, multi-valued decision diagram (MDD)-based approaches, and comparative benchmarking with traditional methods (e.g., continuous-time Markov chains (CTMC), universal generating function (UGF)). For each category, the paper details key methodologies, algorithmic innovations, and practical applications. Additionally, the promising future directions are proposed to address emerging challenges, such as handling dynamic system behaviors, integrating real-time data, and optimizing resource allocation for performability. This review provides a valuable reference for researchers, system designers, and operators seeking to enhance the performability of large-scale computing systems and mitigate risks associated with service level agreement (SLA) violations.
SN  - 3069-1087
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Mo2025Performabi,
  author = {Yuchang Mo and Yuan Fan and Chunyu Miao and Mirlan Chynybaev and Faer Gui and Rengui Zhang and Jianyong Hu and Jinbin Mu and Akylbek Chymyrov},
  title = {Performability Analysis for Large-Scale Multi-State Computing Systems: Methodologies, Advances, and Future Directions},
  journal = {ICCK Transactions on Systems Safety and Reliability},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {81-97},
  doi = {10.62762/TSSR.2025.527003},
  url = {https://www.icck.org/article/abs/TSSR.2025.527003},
  abstract = {Large-scale computing systems, such as cloud data centers, grid infrastructures, and high-performance computing clusters, are the backbone of modern information technology ecosystems. These systems typically consist of numerous heterogeneous, multi-state computing nodes that exhibit varying performance levels due to component failures, degradation, or dynamic resource allocation. Performability analysis, which integrates both system reliability and performance evaluations to quantify the probability of the system operating at a specified performance level, is critical for ensuring the efficient, reliable, and cost-effective operation of these complex systems. This paper presents a comprehensive review of recent advancements in performability analysis for large-scale multi-state computing systems over the past decade. It classifies existing research into three core methodological categories: binary decision diagram (BDD)-based approaches, multi-valued decision diagram (MDD)-based approaches, and comparative benchmarking with traditional methods (e.g., continuous-time Markov chains (CTMC), universal generating function (UGF)). For each category, the paper details key methodologies, algorithmic innovations, and practical applications. Additionally, the promising future directions are proposed to address emerging challenges, such as handling dynamic system behaviors, integrating real-time data, and optimizing resource allocation for performability. This review provides a valuable reference for researchers, system designers, and operators seeking to enhance the performability of large-scale computing systems and mitigate risks associated with service level agreement (SLA) violations.},
  keywords = {performability analysis, large-scale computing systems, multi-state systems, binary decision diagrams (BDD), multi-valued decision diagrams (MDD), reliability, system performance},
  issn = {3069-1087},
  publisher = {Institute of Central Computation and Knowledge}
}

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