Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges
Article No.: 256, Pages 1 - 38
Abstract
The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the Internet Technology community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management challenge in such complex infrastructure concerns the development of efficient maintenance strategies to preserve the environment’s performance and security. While the abundant resources from the academic literature could support the design of novel maintenance solutions, extracting actionable insights from the existing approaches is challenging, given the massive number of published papers. Furthermore, existing review papers, which could help summarize the state-of-the-art, scope their investigations to the maintenance of certain components in particular scenarios. This work fills this gap with a broader literature analysis that covers maintenance strategies targeting physical and logical components in cloud, edge, and IoT environments. First, we introduce a taxonomy that organizes existing solutions according to several characteristics. Then, we review the literature following the taxonomy structure to facilitate the understanding of the research landscape and the comparison between existing works. Finally, we shed light on open challenges that represent promising research directions.
References
[1]
Peter Mell and Timothy Grance. 2011. The NIST Definition of Cloud Computing. NIST, Gaithersburg, MD.
[2]
Rajkumar Buyya, Satish Narayana Srirama, Giuliano Casale, Rodrigo Calheiros, Yogesh Simmhan, Blesson Varghese, Erol Gelenbe, Bahman Javadi, Luis Miguel Vaquero, Marco A. S. Netto, et al. 2018. A manifesto for future generation cloud computing: Research directions for the next decade. ACM Comput. Surveys 51, 5 (2018), 1–38.
[3]
Rosilah Hassan, Faizan Qamar, Mohammad Kamrul Hasan, Azana Hafizah Mohd Aman, and Amjed Sid Ahmed. 2020. Internet of things and its applications: A comprehensive survey. Symmetry 12, 10 (2020), 1674.
[4]
Mahadev Satyanarayanan, Paramvir Bahl, Ramón Caceres, and Nigel Davies. 2009. The case for VM-based cloudlets in mobile computing. IEEE Pervas. Comput. 8, 4 (2009), 14–23.
[5]
Weiwei Wu, Jianping Wang, Kejie Lu, Wen Qi, Feng Shan, and Junzhou Luo. 2017. Providing service continuity in clouds under power outage. IEEE Trans. Serv. Comput. 13, 5 (2017), 930–943.
[6]
Yan-Hong Fan, Mei-Qin Wang, Yan-Bin Li, Kai Hu, and Mu-Zhou Li. 2021. A secure IoT firmware update scheme against SCPA and DoS attacks. J. Comput. Sci. Technol. 36, 2 (2021), 419–433.
[7]
Tu Dinh Ngoc, Boris Teabe, Alain Tchana, Gilles Muller, and Daniel Hagimont. 2021. Mitigating vulnerability windows with hypervisor transplant. In Proceedings of the European Conference on Computer Systems. 162–177.
[8]
Paulo S. Souza, Tiago C. Ferreto, Fábio D. Rossi, and Rodrigo N. Calheiros. 2022. Location-aware maintenance strategies for edge computing infrastructures. IEEE Commun. Lett. 26, 4 (2022), 848–852.
[9]
Yaser Mansouri and M. Ali Babar. 2021. A review of edge computing: Features and resource virtualization. J. Parallel Distrib. Comput. 150 (2021), 155–183.
[10]
Parvaneh Asghari, Amir Masoud Rahmani, and Hamid Haj Seyyed Javadi. 2019. Internet of things applications: A systematic review. Comput. Netw. 148 (2019), 241–261.
[11]
Qi Jing, Athanasios V. Vasilakos, Jiafu Wan, Jingwei Lu, and Dechao Qiu. 2014. Security of the internet of things: Perspectives and Challenges. Wireless Netw. 20, 8 (2014), 2481–2501.
[12]
Burak Kantarci and Hussein T. Mouftah. 2015. Sensing services in cloud-centric Internet of Things: A survey, taxonomy and challenges. In Proceedings of the International Conference on Communication Workshop. IEEE, London, 1865–1870.
[13]
Gary Josebeck and Arun Gowtham. 2022. Demystifying the PF curve & augmenting machine learning for maintenance optimization. In Proceedings of the Annual Reliability and Maintainability Symposium. IEEE, 1–5.
[14]
Ashok Prajapati, James Bechtel, and Subramaniam Ganesan. 2012. Condition based maintenance: A survey. J. Qual. Maint. Eng. 18, 4 (2012), 384–400.
[15]
Ruben Sipos, Dmitriy Fradkin, Fabian Moerchen, and Zhuang Wang. 2014. Log-based predictive maintenance. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM, New York, 1867–1876.
[16]
Donghwan Kim, Seungchul Lee, and Daeyoung Kim. 2021. An applicable predictive maintenance framework for the absence of run-to-failure data. Appl. Sci. 11, 11 (2021), 5180.
[17]
Luca Silvestri, Antonio Forcina, Vito Introna, Annalisa Santolamazza, and Vittorio Cesarotti. 2020. Maintenance transformation through industry 4.0 technologies: A systematic literature review. Comput. Industry 123 (2020), 16.
[18]
Hans Christian Benestad, Bente Anda, and Erik Arisholm. 2009. Understanding software maintenance and evolution by analyzing individual changes: A literature review. J. Softw. Maint. Evol.: Res. Pract. 21, 6 (2009), 349–378.
[19]
Martin Monperrus. 2018. Automatic software repair: A bibliography. Comput. Surveys 51, 1 (2018), 1–24.
[20]
Justyna Petke, Saemundur O. Haraldsson, Mark Harman, William B. Langdon, David R. White, and John R. Woodward. 2017. Genetic improvement of software: A comprehensive survey. IEEE Trans. Evolution. Comput. 22, 3 (2017), 415–432.
[21]
Mostafa Fadaeefath Abadi, Fariborz Haghighat, and Fuzhan Nasiri. 2020. Data center maintenance: Applications and future research directions. Facilities 38, 9/10 (2020), 691–714.
[22]
Heiner Lasi, Peter Fettke, Hans-Georg Kemper, Thomas Feld, and Michael Hoffmann. 2014. Industry 4.0. Bus. Info. Syst. Eng. 6 (2014), 239–242.
[23]
Tiago Zonta, Cristiano André Da Costa, Rodrigo da Rosa Righi, Miromar Jose de Lima, Eduardo Silveira da Trindade, and Guann Pyng Li. 2020. Predictive maintenance in the industry 4.0: A systematic literature review. Comput. Industr. Eng. 150 (2020), 106889.
[24]
Chenhao Qu, Rodrigo N. Calheiros, and Rajkumar Buyya. 2018. Auto-scaling web applications in clouds: A taxonomy and survey. Comput. Surveys 51, 4 (2018), 1–33.
[25]
Xunyun Liu and Rajkumar Buyya. 2020. Resource management and scheduling in distributed stream processing systems: A taxonomy, review, and future directions. Comput. Surveys 53, 3 (2020), 1–41.
[26]
Raquel V. Lopes and Daniel Menascé. 2016. A taxonomy of job scheduling on distributed computing systems. IEEE Trans. Parallel Distrib. Syst. 27, 12 (2016), 3412–3428.
[27]
Shingo Okuno, Fumi Iikura, and Yukihiro Watanabe. 2019. Maintenance scheduling for cloud infrastructure with timing constraints of live migration. In Proceedings of the International Conference on Cloud Engineering. IEEE, 179–189.
[28]
Weigang Hou, Wenxiao Li, Lei Guo, Yiwei Sun, and Xintong Cai. 2017. Recycling edge devices in sustainable internet of things networks. Internet Things J. 4, 5 (2017), 1696–1706.
[29]
Chen Ying, Baochun Li, Xiaodi Ke, and Lei Guo. 2022. Raven: Scheduling virtual machine migration during datacenter upgrades with reinforcement learning. Mobile Netw. Appl. 27, 1 (2022), 1–12.
[30]
Felipe Rubin, Paulo Souza, and Tiago Ferreto. 2023. Reducing power consumption during server maintenance on edge computing infrastructures. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing. 691–698.
[31]
Kathryn A. Dowsland and William B. Dowsland. 1992. Packing problems. Eur. J. Oper. Res. 56, 1 (1992), 2–14.
[32]
Long Wang, Harigovind V. Ramasamy, and Richard E. Harper. 2020. Scheduling physical machine maintenance on qualified clouds: What if migration is not allowed? In Proceedings of the International Conference on Cloud Computing. IEEE, 485–492.
[33]
Cyril Gavoille. 2001. Routing in distributed networks: Overview and open problems. ACM SIGACT News 32, 1 (2001), 36–52.
[34]
Arjang A. Assad. 1978. Multicommodity network flows—A survey. Networks 8, 1 (1978), 37–91.
[35]
Deepika Saxena and Ashutosh Kumar Singh. 2022. OFP-TM: An online VM failure prediction and tolerance model towards high availability of cloud computing environments. J. Supercomput. 78, 6 (2022), 8003–8024.
[36]
Ziyuan Wang, Zekai Zhang, Jingjing Wang, Chunxiao Jiang, Wei Wei, and Yong Ren. 2024. Auv-assisted node repair for iout relying on multiagent reinforcement learning. IEEE Internet of Things Journal 11, 3 (2024), 4139–4151.
[37]
Yuting Wang, Xiaofan Han, and Shunfu Jin. 2024. Performance analysis of a vm-pm repair strategy in mec-enabled wireless systems with bursty traffic. IEEE Transactions on Vehicular Technology 73, 1 (2024), 1146–1161.
[38]
Jacob H. Cox, Joaquin Chung, Sean Donovan, Jared Ivey, Russell J. Clark, George Riley, and Henry L. Owen. 2017. Advancing software-defined networks: A survey. IEEE Access 5 (2017), 25487–25526.
[39]
Wei Ren, Yan Sun, Hong Luo, and Mohsen Guizani. 2018. BLLC: A batch-level update mechanism with low cost for SDN-IoT networks. IEEE Internet Things J. 6, 1 (2018), 1210–1222.
[40]
Zulqar Nain, Arslan Musaddiq, Yazdan Ahmad Qadri, Ali Nauman, Muhammad Khalil Afzal, and Sung Won Kim. 2021. RIATA: A reinforcement learning-based intelligent routing update scheme for future generation IoT networks. IEEE Access 9 (2021), 81161–81172.
[41]
Mustafa Banikhalaf, Ahmad M. Manasrah, Ahmed F. AlEroud, Nabhan Hamadneh, Ahmad Qawasmeh, and Ahmed Y. Al-Dubai. 2019. A reliable route repairing scheme for internet of vehicles. Int. J. Comput. Appl. Technol. 61, 3 (2019), 229–238.
[42]
Zulqar Nain, Arslan Musaddiq, Yazdan Ahmad Qadri, and Sung Won Kim. 2021. History-aware adaptive route update scheme for low-power and lossy networks. In Proceedings of the International Conference on Information and Communication Technology Convergence. IEEE, 1830–1834.
[43]
Bo Han, Vijay Gopalakrishnan, Lusheng Ji, and Seungjoon Lee. 2015. Network function virtualization: Challenges and opportunities for innovations. IEEE Commun. Mag. 53, 2 (2015), 90–97.
[44]
Muhammad Taqi Raza, Zhowei Tan, Ali Tufail, and Fatima Muhammad Anwar. 2022. LTE NFV rollback recovery. IEEE Trans. Netw. Serv. Manage. 19, 3 (2022), 2468–2477.
[45]
Huawei Huang and Song Guo. 2019. Proactive failure recovery for NFV in distributed edge computing. IEEE Commun. Mag. 57, 5 (2019), 131–137.
[46]
Zhenyi Huang and Huawei Huang. 2020. Proactive failure recovery for stateful NFV. In Proceedings of the International Conference on Parallel and Distributed Systems. IEEE, 536–543.
[47]
Yu Wu, Duo Liu, Yujuan Tan, Moming Duan, Longpan Luo, Weilve Wang, and Xianzhang Chen. 2023. LFPR: A lazy fast predictive repair strategy for mobile distributed erasure coded cluster. IEEE Internet of Things Journal 10, 1 (2023), 704–719.
[48]
Rekha Nachiappan, Rodrigo N. Calheiros, Kenan M. Matawie, and Bahman Javadi. 2023. Optimized proactive recovery in erasure-coded cloud storage systems. IEEE Access 11, 1 (2023), 38226–38239.
[49]
Oded Leiba, Ron Bitton, Yechiav Yitzchak, Asaf Nadler, Davidoz Kashi, and Asaf Shabtai. 2019. IoTPatchPool: Incentivized delivery network of IoT software updates based on proofs-of-distribution. Pervas. Mobile Comput. 58 (2019), 1–21.
[50]
Nachiket Tapas, Yechiav Yitzchak, Francesco Longo, Antonio Puliafito, and Asaf Shabtai. 2020. P4UIoT: Pay-per-piece patch update delivery for IoT using gradual release. Sensors 20, 7 (2020), 1–27.
[51]
Elizabeth Nathania Witanto, Yustus Eko Oktian, Sang-Gon Lee, and Jin-Heung Lee. 2020. A blockchain-based OCF firmware update for IoT devices. Appl. Sci. 10, 19 (2020), 1–22.
[52]
Tatsuhiro Fukuda and Kazumasa Omote. 2021. Efficient blockchain-based IoT firmware update considering distribution incentives. In Proceedings of the Conference on Dependable and Secure Computing. IEEE, 1–8.
[53]
Antonio Langiu, Carlo Alberto Boano, Markus Schuß, and Kay Römer. 2019. UpKit: An open-source, portable, and lightweight update framework for constrained IoT devices. In Proceedings of the International Conference on Distributed Computing Systems. IEEE, 2101–2112.
[54]
N. Asokan, Thomas Nyman, Norrathep Rattanavipanon, Ahmad-Reza Sadeghi, and Gene Tsudik. 2018. ASSURED: Architecture for secure software update of realistic embedded devices. IEEE Trans. Comput.-Aided Design Integr. Circ. Syst. 37, 11 (2018), 2290–2300.
[55]
Xinchi He, Sarra Alqahtani, Rose Gamble, and Mauricio Papa. 2019. Securing over-the-air IoT firmware updates using blockchain. In Proceedings of the International Conference on Omni-Layer Intelligent Systems. 164–171.
[56]
Frederico Cerveira, Raul Barbosa, and Henrique Madeira. 2021. Mitigating virtualization failures through migration to a co-located hypervisor. IEEE Access 9 (2021), 105255–105269.
[57]
Sharifeh Fakhrolmobasheri, Ehsan Ataie, and Ali Movaghar. 2018. Modeling and evaluation of power-aware software rejuvenation in cloud systems. Algorithms 11, 10 (2018), 160.
[58]
Matheus Torquato, Paulo Maciel, and Marco Vieira. 2019. A model for availability and security risk evaluation for systems with VMM rejuvenation enabled by VM migration scheduling. IEEE Access 7 (2019), 138315–138326.
[59]
Andrea Segalini, Dino Lopez Pacheco, Guillaume Urvoy-Keller, Fabien Hermenier, and Quentin Jacquemart. 2022. Hy-FiX: Fast in-place upgrades of KVM hypervisors. IEEE Trans. Cloud Comput. 10, 4 (2022), 2679–2690.
[60]
Mark Russinovich, Naga Govindaraju, Melur Raghuraman, David Hepkin, Jamie Schwartz, and Arun Kishan. 2021. Virtual machine preserving host updates for zero day patching in public cloud. In Proceedings of the European Conference on Computer Systems. 114–129.
[61]
Kolade Olorunnife, Kevin Lee, and Jonathan Kua. 2021. Automatic failure recovery for container-based IoT edge applications. Electronics 10, 23 (2021), 3047.
[62]
Martin Weißbach, Nguonly Taing, Markus Wutzler, Thomas Springer, Alexander Schill, and Siobhan Clarke. 2016. Decentralized coordination of dynamic software updates in the Internet of Things. In Proceedings of the World Forum on Internet of Things. IEEE, 171–176.
[63]
Ngoc Hai Bui, Kim Khoa Nguyen, Chuan Pham, and Mohamed Cheriet. 2019. Energy efficient software update mechanism for networked IoT devices. In Proceedings of the Global Communications Conference. IEEE, 1–6.
[64]
Hemant Gupta and Paul C. Van Oorschot. 2019. Onboarding and software update architecture for IoT devices. In Proceedings of the International Conference on Privacy, Security and Trust. IEEE, 1–11.
[65]
Mohammad Salar Arbabi and Mehdi Shajari. 2019. Decentralized and secure delivery network of iot update files based on ethereum smart contracts and blockchain technology. In Proceedings of the Annual International Conference on Computer Science and Software Engineering. ACM, 110–119.
[66]
Asad Waqar Malik, Anis U. Rahman, Arsalan Ahmad, and Max Mauro Dias Santos. 2022. Over-the-air software-defined vehicle updates using federated fog environment. IEEE Transactions on Network and Service Management 19, 4 (2022), 5078–5089.
[67]
Jingzhu He, Ting Dai, Xiaohui Gu, and Guoliang Jin. 2020. HangFix: Automatically fixing software hang bugs for production cloud systems. In Proceedings of the ACM Symposium on Cloud Computing. ACM, 344–357.
[68]
Haining Meng, Xu Zhang, Lei Zhu, Lei Wang, and Zijiang Yang. 2017. Optimizing software rejuvenation policy based on CDM for cloud system. In Proceedings of the Conference on Industrial Electronics and Applications. IEEE, 1850–1854.
[69]
Oleksandr Rolik, Sergii Telenyk, and Eduard Zharikov. 2018. Management of services of a hyperconverged infrastructure using the coordinator. In Proceedings of the International Conference on Computer Science, Engineering and Education Applications. Springer, 456–467.
[70]
Sam Halabi. 2019. Hyperconverged Infrastructure Data Centers: Demystifying HCI. Cisco Press.
[71]
Shin-Ming Cheng, Pin-Yu Chen, Ching-Chao Lin, and Hsu-Chun Hsiao. 2017. Traffic-aware patching for cyber security in mobile IoT. IEEE Commun. Mag. 55, 7 (2017), 29–35.
[72]
Jen-Wei Hu, Lo-Yao Yeh, Shih-Wei Liao, and Chu-Sing Yang. 2019. Autonomous and malware-proof blockchain-based firmware update platform with efficient batch verification for Internet of Things devices. Comput. Secur. 86 (2019), 238–252.
[73]
A. Anastasiou, Panayiotis Christodoulou, Klitos Christodoulou, Vasos Vassiliou, and Zinon Zinonos. 2020. IoT device firmware update over LoRa: The blockchain solution. In Proceedings of the International Conference on Distributed Computing in Sensor Systems. IEEE, 404–411.
[74]
Woei-Jiunn Tsaur, Jen-Chun Chang, and Chin-Ling Chen. 2022. A highly secure IoT firmware update mechanism using blockchain. Sensors 22, 2 (2022), 530.
[75]
Njabulo Sakhile Mtetwa, Paul Tarwireyi, Cecilia Nombuso Sibeko, Adnan Abu-Mahfouz, and Matthew Adigun. 2022. Blockchain-based security model for LoRaWAN firmware updates. J. Sensor Actuat. Netw. 11, 1 (2022), 5.
[76]
Gabriel Solomon, Peng Zhang, Rachael Brooks, and Yuhong Liu. 2023. A secure and cost-efficient blockchain facilitated iot software update framework. IEEE Access 11 (2023), 44879–44894.
[77]
Songran Liu, Mingsong Lv, Wei Zhang, Xu Jiang, Chuancai Gu, Tao Yang, Wang Yi, and Nan Guan. 2023. Light flash write for efficient firmware update on energy-harvesting IoT devices. In Proceedings of the Design, Automation and Test in Europe Conference and Exhibition (DATE’23). IEEE, 1–6.
[78]
Haruto Taka, Fujun He, and Eiji Oki. 2023. Joint service placement and user assignment model in multi-access edge computing networks against base-station failure. Int. J. Netw. Manage. (Apr. 2023), e2233.
[79]
Sahil Garg, Kuljeet Kaur, Neeraj Kumar, Georges Kaddoum, Albert Y. Zomaya, and Rajiv Ranjan. 2019. A hybrid deep learning-based model for anomaly detection in cloud datacenter networks. IEEE Trans. Netw. Service Manage. 16, 3 (2019), 924–935.
[80]
Yennun Huang, Chandra Kintala, Nick Kolettis, and N Dudley Fulton. 1995. Software rejuvenation: Analysis, module and applications. In Proceedings of the International Symposium on Fault-Tolerant Computing. IEEE, 381–390.
[81]
Tadao Murata. 1989. Petri nets: Properties, analysis and applications. Proc. IEEE 77, 4 (1989), 541–580.
[82]
William H. Sanders and John F. Meyer. 2000. Stochastic activity networks: Formal definitions and concepts. In Proceedings of the School Organized by the European Educational Forum. Springer, 315–343.
[83]
Gianfranco Ciardo and Kishor S. Trivedi. 1993. A decomposition approach for stochastic reward net models. Performance Evaluation 18, 1 (1993), 37–59.
[84]
Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub. 2022. Answer Set Solving in Practice. Springer Nature.
[85]
Michael I. Jordan and Tom M. Mitchell. 2015. Machine learning: Trends, perspectives, and prospects. Science 349, 6245 (2015), 255–260.
[86]
Xibin Dong, Zhiwen Yu, Wenming Cao, Yifan Shi, and Qianli Ma. 2020. A survey on ensemble learning. Front. Comput. Sci. 14 (2020), 241–258.
[87]
Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. MIT Press.
[88]
Sourabh Katoch, Sumit Singh Chauhan, and Vijay Kumar. 2021. A review on genetic algorithm: Past, present, and future. Multimedia Tools Appl. 80 (2021), 8091–8126.
[89]
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and Tamt Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolution. Comput. 6, 2 (2002), 182–197.
[90]
Zibin Zheng, Shaoan Xie, Hong-Ning Dai, Xiangping Chen, and Huaimin Wang. 2018. Blockchain challenges and opportunities: A survey. Int. J. Web Grid Serv. 14, 4 (2018), 352–375.
[91]
Maya Dotan, Yvonne-Anne Pignolet, Stefan Schmid, Saar Tochner, and Aviv Zohar. 2021. Survey on blockchain networking: Context, state-of-the-art, challenges. Comput. Surveys 54, 5 (2021), 1–34.
[92]
Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub. 2019. Multi-shot ASP solving with clingo. Theory Pract. Logic Program. 19, 1 (2019), 27–82.
[93]
Eitan Altman and Tania Jimenez. 2012. NS simulator for beginners. Synth. Lect. Commun. Netw. 5, 1 (2012), 1–184.
[94]
Paulo S. Souza, Tiago Ferreto, and Rodrigo N. Calheiros. 2023. EdgeSimPy: Python-based modeling and simulation of edge computing resource management policies. Future Gen. Comput. Syst. 148 (2023), 446–459.
[95]
Khaled Abdelfadeel, Tom Farrell, David McDonald, and Dirk Pesch. 2020. How to make firmware updates over LoRaWAN possible. In Proceedings of the International Symposium on World of Wireless Mobile and Multimedia Networks. IEEE, 16–25.
[96]
David S. Linthicum. 2016. Practical use of microservices in moving workloads to the cloud. IEEE Cloud Computing 3, 5 (2016), 6–9.
[97]
Daming Zhao and Jiantao Zhou. 2022. An energy and carbon-aware algorithm for renewable energy usage maximization in distributed cloud data centers. J. Parallel Distrib. Comput. 165 (2022), 156–166.
[98]
Ting Yang, Yucheng Hou, Young Choon Lee, Hao Ji, and Albert Y. Zomaya. 2020. Power control framework for green data centers. IEEE Trans. Cloud Comput. 10, 4 (2020), 2876–2886.
[99]
Ouzhu Han, Tao Ding, Xiaosheng Zhang, Chenggang Mu, Xinran He, Hongji Zhang, Wenhao Jia, and Zhoujun Ma. 2023. A shared energy storage business model for data center clusters considering renewable energy uncertainties. Renew. Energy 202 (2023), 1273–1290.
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- Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges
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Publication History
Published: 22 June 2024
Online AM: 13 April 2024
Accepted: 31 March 2024
Revised: 27 February 2024
Received: 08 February 2023
Published in CSUR Volume 56, Issue 10
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