Self-* Capabilities of Cloud-Edge Nodes: A Research Review
Abstract
:1. Introduction
2. Background
3. Research Methodology
- Written in English.
- Preference was given to those works published between 2015 and 2023. Although, due to their relevance, some works published previously have also been selected.
Results
4. Terminology and Taxonomy
- Cloud nodes: high-performance servers and high-capacity storage systems that provide services to their users. They allow complex calculations to be executed and are capable of permanently storing a large amount of data [31]. Topologically, these are normally placed on a central location (data center).
- MEC (Mobile or Multi-Access Edge Computing) nodes: smart nodes, normally IT servers tied to radiocommunications infrastructure (e.g., in base stations [32]), that enable the capabilities of cloud services closer to the users’ devices (namely, smartphones or end terminals).
- Edge nodes: any device with computing, storage, and network-attached capabilities, which are capable of dividing and distributing large amounts of work. Examples of these devices are access points, routers, small servers, computers, base stations, etc. [33].
- Far-edge nodes: hardware devices capable of running algorithms that collect and pre-process information received from IoT devices or versatile computing nodes [34].
- Versatile computing nodes: geographically distributed physical devices closer to the end user such as commercial devices, such as Raspberry Pis, SIEMENS SIMATIC edge elements, personal computers, laptops, smartphones, tablets, wearables, smart cards, smart vehicles, etc., with enough computing power to execute tasks [31]. Versatile computing nodes can sometimes also be considered far-edge nodes; they are very close terms that vary mainly in their topological and geographical position, as well as in their role in an edge computing distributed system.
- IoT nodes: physical devices such as sensors, readers, surveillance cameras, actuators, embedded devices, etc. They are able to detect events or characteristics of real objects and transmit them to the upper layer for processing [5,31]. In most recent deployments, IoT nodes are increasingly improving their embedded computing capabilities, starting to act as versatile computing nodes. These are known as smart devices and are a genuine part of the Next-Generation IoT [35].
- Self-configuration: autonomous systems are capable of configuring themselves and their components, following high-level policies.
- Self-optimization: the capacity to continually improve their performance by monitoring and identifying their resources to become more efficient.
- Self-healing: automatic diagnosis and resolution of hardware and software faults.
- Self-protection: the ability to anticipate and avoid problems and autonomously defend against external attacks or internal failures with self-healing measures.
- Self-immunity: the system is capable of restoring security predicates after an attack, eventually preventing them from being compromised again.
- Self-containment: the ability to keep functional parts of the system uncompromised by a malicious attack.
- Self-awareness.
- Self-orchestration.
- Self-diagnose.
- Self-healing.
- Self-scaling.
- Self-configuration.
- Self-optimization.
- Self-adaptation.
- Self-learning.
5. Literature Review and Analysis
5.1. Sensors and Systems of Sensors Overview
5.2. Analysis of Self-* Capabilities Research Status
5.2.1. Self-Awareness
- Networked stimulus-awareness: allows the system to know how to respond to events in its environment with the stimuli received.
- Networked interaction-awareness: determines that the stimuli received and the actions performed form relationships with the surrounding environment.
- Networked time-awareness: obtains information about historical stimuli in order to predict future stimuli and their effect on other nodes.
- Networked goal-awareness: having knowledge of the objectives, goals, constraints, and preferences of the rest of the nodes allows them to know how it affects them, based on specific tables dependent on network information.
- Networked meta-self-awareness: the system is capable of determining its own level of network self-awareness and how it is exercised.
- Monitor: obtain data and information from the environment for the node self-awareness.
- Analyze: the most important information obtained in the monitoring phase is selected and studied.
- Plan: the necessary actions for achieving goals and objectives are defined and built.
- Execute: the procedures for the execution of the plans are defined.
- Knowledge: the information used in the four previous phases is stored as shared knowledge.
5.2.2. Self-Orchestration
5.2.3. Self-Diagnose
5.2.4. Self-Healing
- Self-detector: its purpose is to obtain information from the device on which it works.
- Self-monitor: check the health status of the IoT device, analyzing the information obtained by the self-detector component. From these data, health score metrics are extracted, which are compared with thresholds to determine if the device is OK or not.
- Self-remediator: if the self-monitor component detects a bad state of health of the device, it sends a notification to this component to try to recover (through a series of operations) the good state of health. If this is not possible, other operations are applied to try to recover the state of health again.
5.2.5. Self-Scaling
- Self-scaling self-sufficient cell model (SCM): this model is characterized by the lack of direct interactions between containers. This design, in turn, is subdivided into three variants (SCM-A, SCM-B, and SCM-C).
- Self-scaling interactive cell model (ICM): this model is characterized by containers that have information about the containers that are in their environment. The exchange of information can be carried out directly (between containers) or through intermediate services.
5.2.6. Self-Configuration
- Sensor module: gathers preliminary system and application data.
- Modeling module: automatically analyzes raw data from the sensor module.
- Controller module: for each virtual machine running on the graphics card, an agent monitors its performance and sends the information to a scheduler. This analyzes the information of all the virtual machines and sends an instruction to activate the control system.
- Self-control-configuration module: manages the self-configuration of the controller parameters.
5.2.7. Self-Optimization
5.2.8. Self-Adaptation
5.2.9. Self-Learning
5.3. Literature Comparison
6. Future Research Directions
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Kaufman, L.M. Data Security in the World of Cloud Computing. IEEE Secur. Priv. 2009, 7, 61–64. [Google Scholar] [CrossRef]
- Liu, W. Research on Cloud Computing Security Problem and Strategy. In Proceedings of the 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), Yichang, China, 21–23 April 2012; pp. 1216–1219. [Google Scholar] [CrossRef]
- Mell, P.; Grance, T. The NIST Definition of Cloud Computing; Recommendations of the National Institute of Standards and Technology; National Institute of Standards and Technology: Gaithersburg, MA, USA, 2011. [Google Scholar] [CrossRef]
- Jadeja, Y.; Modi, K. Cloud Computing—Concepts, Architecture and Challenges. In Proceedings of the 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Nagercoil, India, 21–22 March 2012; pp. 877–880. [Google Scholar] [CrossRef]
- Yu, W.; Liang, F.; He, X.; Hatcher, W.G.; Lu, C.; Lin, J.; Yang, X. A Survey on the Edge Computing for the Internet of Things. IEEE Access 2017, 6, 6900–6919. [Google Scholar] [CrossRef]
- Xu, M.; Buyya, R. Managing Renewable Energy and Carbon Footprint in Multi-Cloud Computing Environments. J. Parallel Distrib. Comput. 2020, 135, 191–202. [Google Scholar] [CrossRef]
- Satyanarayanan, M. The Emergence of Edge Computing. Computer 2017, 50, 30–39. [Google Scholar] [CrossRef]
- Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge Computing: Vision and Challenges. IEEE Internet Things J. 2016, 3, 637–646. [Google Scholar] [CrossRef]
- Edge Computing Reference Architecture 2.0. Available online: http://en.ecconsortium.net/Uploads/file/20180328/1522232376480704.pdf (accessed on 11 January 2023).
- Zhang, J.; Chen, B.; Zhao, Y.; Cheng, X.; Hu, F. Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues. IEEE Access 2018, 6, 18209–18237. [Google Scholar] [CrossRef]
- Carvalho, L.I.; Da Silva, D.M.A.; Sofia, R.C. Leveraging Context-Awareness to Better Support the IoT Cloud-Edge Continuum. In Proceedings of the 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC), Paris, France, 20–23 April 2020; pp. 356–359. [Google Scholar] [CrossRef]
- Tang, Y.; Zhao, C.; Wang, J.; Zhang, C.; Sun, Q.; Zheng, W.X.; Du, W.; Qian, F.; Kurths, J. Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey. IEEE Trans. Neural Netw. Learn. Syst. 2022, 1–21. [Google Scholar] [CrossRef]
- Schieferdecker, I. Quality Assurance for Autonomous Systems—A Review of Model-Based Methods. In Proceedings of the 8th International Symposium on Autonomous Decentralized Systems (ISADS’07), Sedona, AZ, USA, 21–23 March 2007; pp. 305–307. [Google Scholar] [CrossRef]
- H-CLOUD White Paper Consultation—EU Agenda. Available online: https://euagenda.eu/events/2022/02/24/hcloud-white-paper-consultation (accessed on 11 January 2023).
- Strategy. Available online: https://single-market-economy.ec.europa.eu/industry/strategy_en (accessed on 11 January 2023).
- A 2021 Perspective on Edge Computing White Paper Scientific Community. Available online: https://atos.net/wp-content/uploads/2021/08/atos-2021-perspective-on-edge-computing-white-paper.pdf (accessed on 11 January 2023).
- IoT and the Future of Edge Computing in Europe|Shaping Europe’s Digital Future. Available online: https://digital-strategy.ec.europa.eu/en/news/iot-and-future-edge-computing-europe (accessed on 11 January 2023).
- European Industrial Technology Roadmap for the Next Generation Cloud-Edge Offering. Available online: https://ec.europa.eu/newsroom/repository/document/2021-18/European_CloudEdge_Technology_Investment_Roadmap_for_publication_pMdz85DSw6nqPppq8hE9S9RbB8_76223.pdf (accessed on 11 January 2023).
- Future Cloud Research Roadmap—Google Drive. Available online: https://drive.google.com/file/d/1Qw-PIR5D4H-ZZ4-CZ1pXRkf8lUZjLMzE/view (accessed on 11 January 2023).
- Energy-Efficient Cloud Computing Technologies and Policies for an Eco-Friendly Cloud Market|Shaping Europe’s Digital Future. Available online: https://digital-strategy.ec.europa.eu/en/library/energy-efficient-cloud-computing-technologies-and-policies-eco-friendly-cloud-market (accessed on 11 January 2023).
- Top 8 Cloud Trends to Watch in 2021 & Beyond. Available online: https://www.veritis.com/blog/top-8-cloud-trends-to-watch-in-2021-and-beyond/ (accessed on 11 January 2023).
- Pourabdollahian, G.; Gole, J.; Eisenträger, M.; Giuffrida, M. Understanding Cloud-Edge-IoT: Challenges and Opportunities—Webinar Slides; Zenodo: Geneva, Switzerland, 2022. [Google Scholar] [CrossRef]
- EUCloudEdgeIOT—Building the European Cloud Edge IoT Continuum for Business and Research. Available online: https://eucloudedgeiot.eu/ (accessed on 11 January 2023).
- Funding & Tenders. Available online: https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl4-2021-data-01-05 (accessed on 11 January 2023).
- Funding & Tenders. Available online: https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl4-2022-data-01-02 (accessed on 11 January 2023).
- Hu, G.J.; Vardanega, T. An Architectural View on the Compute Continuum: Challenges and Technologies. SSRN Electron. J. 2023. [Google Scholar] [CrossRef]
- Islam, M.M.; Ramezani, F.; Lu, H.Y.; Naderpour, M. Optimal Placement of Applications in the Fog Environment: A Systematic Literature Review. J. Parallel Distrib. Comput. 2023, 174, 46–69. [Google Scholar] [CrossRef]
- Gill, S.S.; Xu, M.; Ottaviani, C.; Patros, P.; Bahsoon, R.; Shaghaghi, A.; Golec, M.; Stankovski, V.; Wu, H.; Abraham, A.; et al. AI for next Generation Computing: Emerging Trends and Future Directions. Internet Things 2022, 19, 100514. [Google Scholar] [CrossRef]
- An Open Ecosystem for European Strategic Autonomy and Interoperability across the Computing Continuum Industry|OpenContinuum Project|Fact Sheet|HORIZON|CORDIS|European Commission. Available online: https://cordis.europa.eu/project/id/101070030 (accessed on 11 January 2023).
- Rosendo, D.; Costan, A.; Valduriez, P.; Antoniu, G. Distributed Intelligence on the Edge-to-Cloud Continuum: A Systematic Literature Review. J. Parallel Distrib. Comput. 2022, 166, 71–94. [Google Scholar] [CrossRef]
- Hu, P.; Dhelim, S.; Ning, H.; Qiu, T. Survey on Fog Computing: Architecture, Key Technologies, Applications and Open Issues. J. Netw. Comput. Appl. 2017, 98, 27–42. [Google Scholar] [CrossRef]
- Yu, Y. Mobile Edge Computing towards 5G: Vision, Recent Progress, and Open Challenges. China Commun. 2016, 13, 89–99. [Google Scholar] [CrossRef] [Green Version]
- Wu, X.; Dunne, R.; Zhang, Q.; Shi, W. Edge Computing Enabled Smart Firefighting: Opportunities and Challenges. In Proceedings of the Fifth ACM/IEEE Workshop on Hot Topics in Web Systems and Technologies, San Jose, CA, USA, 14 October 2017; Volume 17. [Google Scholar] [CrossRef]
- Antonini, M.; Pincheira, M.; Vecchio, M.; Antonelli, F. Tiny-MLOps: A Framework for Orchestrating ML Applications at the Far Edge of IoT Systems. In Proceedings of the 2022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), Larnaca, Cyprus, 25–26 May 2022. [Google Scholar] [CrossRef]
- Ang, L.M.; Seng, K.P.; Wachowicz, M. Embedded Intelligence and the Data-Driven Future of Application-Specific Internet of Things for Smart Environments. Int. J. Distrib. Sens. Netw. 2022, 18, 15501329221102371. [Google Scholar] [CrossRef]
- Shakarami, A.; Shakarami, H.; Ghobaei-Arani, M.; Nikougoftar, E.; Faraji-Mehmandar, M. Resource Provisioning in Edge/Fog Computing: A Comprehensive and Systematic Review. J. Syst. Archit. 2022, 122, 102362. [Google Scholar] [CrossRef]
- Razzaque, M.A.; Milojevic-Jevric, M.; Palade, A.; Cla, S. Middleware for Internet of Things: A Survey. IEEE Internet Things J. 2016, 3, 70–95. [Google Scholar] [CrossRef] [Green Version]
- Xiao, A.; Lu, Z.; Du, X.; Wu, J.; Hung, P.C.K. ORHRC: Optimized Recommendations of Heterogeneous Resource Configurations in Cloud-Fog Orchestrated Computing Environments. In Proceedings of the 2020 IEEE International Conference on Web Services (ICWS), Beijing, China, 19–23 October 2020; pp. 404–412. [Google Scholar] [CrossRef]
- Projects Aeros Meta-Operating Systems for the Next-Generation IoT and Edge Computing. Available online: https://ec.europa.eu/newsroom/repository/document/2022-25/Factsheet__Horizon_Europe_metaOS_projects_8aBnKLInqjY1Epj4HIvU4vzlY_87827.pdf (accessed on 11 January 2023).
- Comparison between Cloud Computing, Grid Computing, Cluster Computing and Virtualization. Available online: https://www.researchgate.net/publication/271531358_Comparison_between_Cloud_Computing_Grid_Computing_Cluster_Computing_and_Virtualization (accessed on 13 February 2023).
- Zeng, F.; Tang, J.; Liu, C.; Deng, X.; Li, W. Task-Offloading Strategy Based on Performance Prediction in Vehicular Edge Computing. Mathematics 2022, 10, 1010. [Google Scholar] [CrossRef]
- Nikolopoulos, V.; Nikolaidou, M.; Voreakou, M.; Anagnostopoulos, D. Fog Node Self-Control Middleware: Enhancing Context Awareness towards Autonomous Decision Making in Fog Colonies. Internet Things 2022, 19, 100549. [Google Scholar] [CrossRef]
- Kephart, J.O.; Chess, D.M. The Vision of Autonomic Computing. Computer 2003, 36, 41–50. [Google Scholar] [CrossRef]
- Berns, A.; Ghosh, S. Dissecting Self-* Properties. In Proceedings of the 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, San Francisco, CA, USA, 14–18 September 2009; pp. 10–19. [Google Scholar] [CrossRef]
- Sterritt, R.; Hinchey, M. SPAACE IV: Self-Properties for an Autonomous & Autonomic Computing Environment—Part IV a Newish Hope. In Proceedings of the 2010 Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems, Oxford, UK, 22–26 March 2010; pp. 119–125. [Google Scholar] [CrossRef]
- Yeh, C.H.; Tsai, N.; Zhuang, Y.H.; Chow, C.W.; Liu, W.F. Fault Self-Detection Technique in Fiber Bragg Grating-Based Passive Sensor Network. IEEE Sens. J. 2016, 16, 8070–8074. [Google Scholar] [CrossRef]
- Zhu, M.; Li, J.; Wang, W.; Chen, D. Self-Detection and Self-Diagnosis Methods for Sensors in Intelligent Integrated Sensing System. IEEE Sens. J. 2021, 21, 19247–19254. [Google Scholar] [CrossRef]
- Richardson, A.; Cheneler, D. Self-Monitoring, Self-Healing Biomorphic Sensor Technology. In Proceedings of the 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design, IOLTS 2019, Rhodes, Greece, 1–3 July 2019; pp. 121–124. [Google Scholar] [CrossRef] [Green Version]
- Bicocchi, N.; Mamei, M.; Prati, A.; Cucchiara, R.; Zambonelli, F. Pervasive Self-Learning with Multi-Modal Distributed Sensors. In Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, Venice, Italy, 20–24 October 2008; pp. 61–66. [Google Scholar] [CrossRef]
- Gotzinger, M.; Juhasz, D.; Taherinejad, N.; Willegger, E.; Tutzer, B.; Liljeberg, P.; Jantsch, A.; Rahmani, A.M. RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems. IEEE Access 2020, 8, 141373–141394. [Google Scholar] [CrossRef]
- Diaconescu, A.; Porter, B.; Rodrigues, R.; Pournaras, E. Hierarchical Self-Awareness and Authority for Scalable Self-Integrating Systems. In Proceedings of the 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W), Trento, Italy, 3–7 September 2018; pp. 168–175. [Google Scholar] [CrossRef] [Green Version]
- Esterle, L.; Brown, J.N.A. Levels of Networked Self-Awareness. In Proceedings of the 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W), Trento, Italy, 3–7 September 2018; pp. 237–238. [Google Scholar] [CrossRef]
- Lewis, P.R.; Chandra, A.; Faniyi, F.; Glette, K.; Chen, T.; Bahsoon, R.; Torresen, J.; Yao, X. Architectural Aspects of Self-Aware and Self-Expressive Computing Systems: From Psychology to Engineering. Computer 2015, 48, 62–70. [Google Scholar] [CrossRef] [Green Version]
- Anzanpour, A.; Azimi, I.; Gotzinger, M.; Rahmani, A.M.; Taherinejad, N.; Liljeberg, P.; Jantsch, A.; Dutt, N. Self-Awareness in Remote Health Monitoring Systems Using Wearable Electronics. In Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE), Lausanne, Switzerland, 27–31 March 2017; pp. 1056–1061. [Google Scholar] [CrossRef]
- Andrade, R.; Torres, J. Self-Awareness as an Enabler of Cognitive Security. In Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 1–3 November 2018; pp. 701–708. [Google Scholar] [CrossRef]
- An Architectural Blueprint for Autonomic Computing. Available online: https://www-03.ibm.com/autonomic/pdfs/AC%20Blueprint%20White%20Paper%20V7.pdf (accessed on 11 January 2023).
- Elhabbash, A.; Bahsoon, R.; Tino, P.; Lewis, P.R.; Elkhatib, Y. Attaining Meta-Self-Awareness through Assessment of Quality-of-Knowledge. In Proceedings of the 2021 IEEE International Conference on Web Services (ICWS), Chicago, IL, USA, 5–10 September 2021; pp. 712–723. [Google Scholar] [CrossRef]
- Regazzoni, C.S.; Marcenaro, L.; Campo, D.; Rinner, B. Multisensorial Generative and Descriptive Self-Awareness Models for Autonomous Systems. Proc. IEEE 2020, 108, 987–1010. [Google Scholar] [CrossRef]
- Zhang, N.; Bahsoon, R.; Theodoropoulos, G. Towards Engineering Cognitive Digital Twins with Self-Awareness. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020; pp. 3891–3896. [Google Scholar] [CrossRef]
- Delamer, I.M.; Martinez Lastra, J.L. Self-Orchestration and Choreography: Towards Architecture-Agnostic Manufacturing Systems. In Proceedings of the 20th International Conference on Advanced Information Networking and Applications—Volume 1 (AINA’06), Vienna, Austria, 18–20 April 2006; Volume 2, pp. 5–9. [Google Scholar] [CrossRef]
- Wen, Z.; Yang, R.; Garraghan, P.; Lin, T.; Xu, J.; Rovatsos, M. Fog Orchestration for Internet of Things Services. IEEE Internet Comput. 2017, 21, 16–24. [Google Scholar] [CrossRef] [Green Version]
- Khebbeb, K.; Hameurlain, N.; Belala, F. A Maude-Based Rewriting Approach to Model and Verify Cloud/Fog Self-Adaptation and Orchestration. J. Syst. Archit. 2020, 110, 101821. [Google Scholar] [CrossRef]
- Ruta, M.; Scioscia, F.; Loseto, G.; Di Sciascio, E. A Semantic-Enabled Social Network of Devices for Building Automation. IEEE Trans. Ind. Inform. 2017, 13, 3379–3388. [Google Scholar] [CrossRef]
- Schulz, D. Intent-Based Automation Networks: Toward a Common Reference Model for the Self-Orchestration of Industrial Intranets. In Proceedings of the IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 23–26 October 2016; pp. 4657–4664. [Google Scholar] [CrossRef]
- Discenzo, F.M.; Unsworth, P.J.; Loparo, K.A.; Marcy, H. Self-Diagnosing Intelligent Motors: A Key Enabler for next Generation Manufacturing Systems. In Proceedings of the IEE Colloquium on Intelligent and Self-Validating Sensors (Ref. No. 1999/160), Oxford, UK, 21 June 1999; pp. 15–18. [Google Scholar] [CrossRef]
- Hoang, V.-T.; Julien, N.; Berruet, P. On-Line Self-Diagnosis Based on Power Measurement for a Wireless Sensor Node. In Proceedings of the First IEEE Workshop on Highly-Reliable Power-Efficient Embedded Designs, Shenzhen, China, February 2013. [Google Scholar]
- Volotka, V. Methods of Self-Diagnosing in Telecommunication Networks. In Proceedings of the 2015 Second International Scientific-Practical Conference Problems of Infocommunications Science and Technology (PIC S&T), Kharkiv, Ukraine, 13–15 October 2015; pp. 131–134. [Google Scholar] [CrossRef]
- Raheem, S.A.; Prabhakar, M.; Venugopal, C. Comb Needle Model for Data Aggregation Using Self-Diagnose Cluster in WSN. In Proceedings of the 2017 International Conference on Smart Technologies For Smart Nation (SmartTechCon), Bengaluru, India, 17–19 August 2017; pp. 390–394. [Google Scholar] [CrossRef]
- Harte, S.; Rahman, A.; Razeeb, K.M. Fault Tolerance in Sensor Networks Using Self-Diagnosing Sensor Nodes. In IEE International Workshop on Intelligent Environments, 2005 (Ref. No. 2005/11059); IET: Colchester, UK; pp. 7–12. [CrossRef]
- Elhadef, M.; Boukerche, A.; Elkadiki, H. Self-Diagnosing Wireless Mesh and Ad-Hoc Networks Using an Adaptable Comparison-Based Approach. In Proceedings of the The Second International Conference on Availability, Reliability and Security (ARES’07), Vienna, Austria, 10–13 April 2007; pp. 983–990. [Google Scholar] [CrossRef]
- Monitoring and Notifying Enabler—ASSIST-IoT 0.1 Documentation. Available online: https://assist-iot-enablers-documentation.readthedocs.io/en/latest/verticals/self/monitoring_and_notifying_enabler.html (accessed on 11 January 2023).
- Szmeja, P.; Fornés-Leal, A.; Lacalle, I.; Palau, C.E.; Ganzha, M.; Pawłowski, W.; Paprzycki, M.; Schabbink, J. ASSIST-IoT: A Modular Implementation of a Reference Architecture for the Next Generation Internet of Things. Electronics 2023, 12, 854. [Google Scholar] [CrossRef]
- Architecture for Scalable, Self-Human-Centric, Intelligent, Secure, and Tactile next Generation IoT D5.2 Transversal Enablers Development-Preliminary Version. 2021. Available online: https://assist-iot.eu/wp-content/uploads/2021/12/ASSIST-IoT_D5.2-Transversal-Enablers-Development-Preliminary-Version-v1.0.pdf.
- Dong, X.; Wang, H.; Lv, H. A Comprehensive Monitor Model for Self-Healing Systems. In Proceedings of the 2010 International Conference on Multimedia Information Networking and Security, Nanjing, China, 4–6 November 2010; pp. 751–756. [Google Scholar] [CrossRef]
- Khalil, K.; Eldash, O.; Kumar, A.; Bayoumi, M. Self-Healing Approach for Hardware Neural Network Architecture. In Proceedings of the 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), Dallas, TX, USA, 4–7 August 2019; pp. 622–625. [Google Scholar] [CrossRef]
- Yang, L.; Xiao, F.; Chen, H.; Lai, Y.; Chollot, Y. The Experiences of Decentralized Self-Healing Grid. In Proceedings of the 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP), Xi’an, China, 21–24 October 2019; pp. 1864–1867. [Google Scholar] [CrossRef]
- Liu, W.; Kang, T.; Cheng, W.; Zhao, F. The Modeling of Self-Healing Control System for Distribution Network Based on UML. In Proceedings of the 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Changsha, China, 26–29 November 2015; pp. 1435–1439. [Google Scholar] [CrossRef]
- Liu, Z.; Gui, C.; Ma, C. Design and Verification of Integrated Ship Monitoring Network with High Reliability and Zero-Time Self-Healing. In Proceedings of the 2019 Chinese Control and Decision Conference (CCDC), Nanchang, China, 3–5 June 2019; pp. 2348–2351. [Google Scholar] [CrossRef]
- Hou, J. A Method of Distribution Network Reconstruction Based on Self-Healing Technology. In Proceedings of the 2021 China International Conference on Electricity Distribution (CICED), Shanghai, China, 7–9 April 2021; pp. 784–788. [Google Scholar] [CrossRef]
- Self-Healing Device Enabler—ASSIST-IoT 0.1 Documentation. Available online: https://assist-iot-enablers-documentation.readthedocs.io/en/latest/verticals/self/self_healing_device_enabler.html (accessed on 11 January 2023).
- Elasticity in Cloud Computing: What It Is, and What It Is Not. Available online: https://www.researchgate.net/publication/264942137_Elasticity_in_Cloud_Computing_What_it_is_and_What_it_is_Not (accessed on 11 January 2023).
- Herrera, J.; Molto, G. Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms. IEEE Access 2020, 8, 52139–52150. [Google Scholar] [CrossRef]
- Mehmood, A.; Khan, T.A.; Diaz Rivera, J.J.; Song, W.C. Dynamic Auto-Scaling of VNFs Based on Task Execution Patterns. In Proceedings of the 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), Matsue, Japan, 18–20 September 2019. [Google Scholar] [CrossRef]
- Nikravesh, A.Y.; Ajila, S.A.; Lung, C.H. Towards an Autonomic Auto-Scaling Prediction System for Cloud Resource Provisioning. In Proceedings of the 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Florence, Italy, 18–19 May 2015; pp. 35–45. [Google Scholar] [CrossRef]
- Casalicchio, E.; Perciballi, V. Auto-Scaling of Containers: The Impact of Relative and Absolute Metrics. In Proceedings of the 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W), Tucson, AZ, USA, 18–22 September 2017; pp. 207–214. [Google Scholar] [CrossRef]
- Consortium, A.-I. D5.3—Transversal Enablers Development Intermediate Version. 2022. Available online: https://assist-iot.eu/wp-content/uploads/2022/05/D5.3_Transversal-Enablers-Development-Intermediate-Version.pdf (accessed on 15 January 2023).
- Resource Provisioning Enabler—ASSIST-IoT 0.1 Documentation. Available online: https://assist-iot-enablers-documentation.readthedocs.io/en/latest/verticals/self/resource_provisioning_enabler.html (accessed on 11 January 2023).
- Chattopadhyay, S.; Chatterjee, S.; Nandi, S.; Chakraborty, S. Aloe: An Elastic Auto-Scaled and Self-Stabilized Orchestration Framework for IoT Applications. In Proceedings of the IEEE INFOCOM 2019—IEEE Conference on Computer Communications, Paris, France, 29 April–2 May 2019; pp. 802–810. [Google Scholar] [CrossRef]
- Parashar, M.; Hariri, S. Autonomic Computing: An Overview. Lect. Notes Comput. Sci. 2005, 3566, 257–269. [Google Scholar] [CrossRef]
- Yang, G.; Liang, H. Self Configuration of 4G Network Terminals. In Proceedings of the 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010), Wuhan, China, 6–7 March 2010; Volume 1, pp. 80–83. [Google Scholar] [CrossRef]
- Wang, J.Z.; Vanninen, M. Self-Configuration Protocols for Small-Scale P2P Networks. IEEE Symp. Rec. Netw. Oper. Manag. Symp. 2006, 1–4. [Google Scholar] [CrossRef]
- Mombello, L.; Calarco, N.; Quintian, F.P. System-on-Chip Implementation of a Self-Configuration System for a Programmable Photodetector ASIC. In Proceedings of the 2020 Argentine Conference on Electronics (CAE), Buenos Aires, Argentina, 27–28 February 2020; pp. 99–103. [Google Scholar] [CrossRef]
- Guan, E.; Liu, J.; Zhao, Y. Self-Configuration Strategy Design for Unit-Compressible Modular Robotic System. In Proceedings of the CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020), Online Conference, 18–21 September 2020; pp. 232–237. [Google Scholar] [CrossRef]
- Abdellaoui, G.; Megnafi, H.; Bendimerad, F.T. A Novel Model Using Reo for IoT Self-Configuration Systems. In Proceedings of the 2020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP), El Oued, Algeria, 16–17 May 2020; pp. 80–84. [Google Scholar] [CrossRef]
- Yao, J.; Lu, Q.; Qi, Z. Automated Resource Sharing for Virtualized GPU with Self-Configuration. In Proceedings of the 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), Hong Kong, China, 26–29 September 2017; pp. 250–252. [Google Scholar] [CrossRef]
- Bulbul, N.S.; Ergenc, D.; Fischer, M. SDN-Based Self-Configuration for Time-Sensitive IoT Networks. In Proceedings of the 2021 IEEE 46th Conference on Local Computer Networks (LCN), Edmonton, AB, Canada, 4–7 October 2021; pp. 73–80. [Google Scholar] [CrossRef]
- Automated Configuration Enabler—ASSIST-IoT 0.1 Documentation. Available online: https://assist-iot-enablers-documentation.readthedocs.io/en/latest/verticals/self/automated_configuration_enabler.html (accessed on 11 January 2023).
- Nami, M.R.; Bertels, K. A Survey of Autonomic Computing Systems. In Proceedings of the Third International Conference on Autonomic and Autonomous Systems (ICAS’07), Athens, Greece, 19–25 June 2007; p. 26. [Google Scholar] [CrossRef]
- Zheng, R.; Zhang, M.; Wu, Q.; Li, G.; Wei, W. A Service Self-Optimization Algorithm Based on Autonomic Computing. In Proceedings of the 2009 IEEE International Conference on Granular Computing, Nanchang, China, 17–19 August 2009; pp. 805–808. [Google Scholar] [CrossRef]
- Shayea, I.; Ergen, M.; Azizan, A.; Ismail, M.; Daradkeh, Y.I. Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function. IEEE Access 2020, 8, 214392–214412. [Google Scholar] [CrossRef]
- Sánchez-González, J.; Pérez-Romero, J.; Sallent, O. A Rule-Based Solution Search Methodology for Self-Optimization in Cellular Networks. IEEE Commun. Lett. 2014, 18, 2189–2192. [Google Scholar] [CrossRef]
- Meshkova, E.; Wang, Z.; Rerkrai, K.; Ansari, J.; Nasreddine, J.; Denkovski, D.; Farnham, T.; Riihijarvi, J.; Gavrilovska, L.; Mahonen, P. Designing a Self-Optimization System for Cognitive Wireless Home Networks. IEEE Trans. Cogn. Commun. Netw. 2017, 3, 684–702. [Google Scholar] [CrossRef]
- Trumler, W.; Thiemann, T.; Ungerer, T. An Artificial Hormone System for Self-Organization of Networked Nodes. IFIP Int. Fed. Inf. Process. 2006, 216, 85–94. [Google Scholar] [CrossRef]
- Wang, L.; Liu, J.; Wu, Q.; Wang, X. Ship Course Control Based on BSO-PID Online Self-Optimization Algorithm. In Proceedings of the 2019 5th International Conference on Transportation Information and Safety (ICTIS), Liverpool, UK, 14–17 July 2019; pp. 1405–1411. [Google Scholar] [CrossRef]
- Cheng, B.H.C.; De Lemos, R.; Giese, H.; Inverardi, P.; Magee, J.; Andersson, J.; Becker, B.; Bencomo, N.; Brun, Y.; Cukic, B.; et al. Software Engineering for Self-Adaptive Systems: A Research Roadmap. Lect. Notes Comput. Sci. 2009, 5525, 1–26. [Google Scholar] [CrossRef] [Green Version]
- Krupitzer, C.; Roth, F.M.; Vansyckel, S.; Schiele, G.; Becker, C. A Survey on Engineering Approaches for Self-Adaptive Systems. Pervasive Mob. Comput. 2015, 17, 184–206. [Google Scholar] [CrossRef]
- Amiri, A.; Zdun, U.; van Hoorn, A.; Dustdar, S. Automatic Adaptation of Reliability and Performance Trade-Offs in Service- and Cloud-Based Dynamic Routing Architectures. In Proceedings of the 2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS), Haikou, China, 6–10 December 2021; pp. 434–445. [Google Scholar] [CrossRef]
- Chen, M.; Wang, Y.; Li, P.; Fu, H. Research on an Improved PSO Algorithm with Dual Self-Adaptation and Dual Variation. In Proceedings of the 2022 IEEE International Conference on Mechatronics and Automation (ICMA), Guilin, China, 7–10 August 2022; pp. 646–650. [Google Scholar] [CrossRef]
- Zhang, S.; Zhang, M.; Ni, L.; Liu, P. A Multi-Level Self-Adaptation Approach for Microservice Systems. In Proceedings of the 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, 12–15 April 2019; pp. 498–502. [Google Scholar] [CrossRef]
- Nallur, V.; Bahsoon, R. A Decentralized Self-Adaptation Mechanism for Service-Based Applications in the Cloud. IEEE Trans. Softw. Eng. 2013, 39, 591–612. [Google Scholar] [CrossRef]
- Ardito, L. Energy Aware Self-Adaptation in Mobile Systems. In Proceedings of the 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, USA, 18–26 May 2013; pp. 1435–1437. [Google Scholar] [CrossRef] [Green Version]
- Yuan, Y.; Zhang, W.; Zhang, X. A Context-Aware Self-Adaptation Approach for Web Service Composition. In Proceedings of the 2018 3rd International Conference on Information Systems Engineering (ICISE), Shanghai, China, 4–6 May 2018; pp. 33–38. [Google Scholar] [CrossRef]
- Boyapati, S.R.; Szabo, C. Self-Adaptation in Microservice Architectures: A Case Study. In Proceedings of the 2022 26th International Conference on Engineering of Complex Computer Systems (ICECCS), Hiroshima, Japan, 26–30 March 2022; pp. 42–51. [Google Scholar] [CrossRef]
- Colombo, V.; Tundo, A.; Ciavotta, M.; Mariani, L. Towards Self-Adaptive Peer-To-Peer Monitoring for Fog Environments. In Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022, Pittsburgh, PA, USA, 18–23 May 2022; pp. 156–166. [Google Scholar] [CrossRef]
- Poslad, S. Autonomous Systems and Artificial Life. Ubiquitous Comput. 2009, 317–341. [Google Scholar] [CrossRef]
- Luo, Q.; Li, C.; Luan, T.H.; Shi, W.; Wu, W. Self-Learning Based Computation Offloading for Internet of Vehicles: Model and Algorithm. IEEE Trans. Wirel. Commun. 2021, 20, 5913–5925. [Google Scholar] [CrossRef]
- Srinivasan, A. IoT Cloud Based Real Time Automobile Monitoring System. In Proceedings of the 2018 3rd IEEE International Conference on Intelligent Transportation Engineering, ICITE 2018, Singapore, 3–5 September 2018; pp. 231–235. [Google Scholar] [CrossRef]
- Sacco, A.; Esposito, F.; Marchetto, G.; Montuschi, P. A Self-Learning Strategy for Task Offloading in UAV Networks. IEEE Trans. Veh. Technol. 2022, 71, 4301–4311. [Google Scholar] [CrossRef]
- Sudharsan, B.; Yadav, P.; Breslin, J.G.; Intizar Ali, M. Train++: An Incremental ML Model Training Algorithm to Create Self-Learning IoT Devices. In Proceedings of the 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People, and Smart City Innovations, SmartWorld/ScalCom/UIC/ATC/IoP/SCI 2021, Atlanta, GA, USA, 18–21 October 2021; pp. 97–106. [Google Scholar] [CrossRef]
- Tam, P.; Math, S.; Nam, C.; Kim, S. Adaptive Resource Optimized Edge Federated Learning in Real-Time Image Sensing Classifications. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 10929–10940. [Google Scholar] [CrossRef]
- Shen, Z.; Yokota, K.; Jin, J.; Tagami, A.; Higashino, T. In-Network Self-Learning Algorithms for BEMS through a Collaborative Fog Platform. In Proceedings of the 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), Krakow, Poland, 16–18 May 2018; pp. 1162–1169. [Google Scholar] [CrossRef]
- Abeysinghe, P.; Bandara, T. A Novel Self-Learning Approach to Overcome Incompatibility on TripAdvisor Reviews. Data Sci. Manag. 2022, 5, 1–10. [Google Scholar] [CrossRef]
Cloud Nodes | MEC Nodes | Edge Nodes | Far-Edge Nodes | Versatile Comp. Nodes | IoT Nodes | |
---|---|---|---|---|---|---|
Self-awareness | [54,55,56,57,59] | [54,55,56,57,59] | [55,56,59] | [56,59] | [54,56,57,58,59] | [54,56,57,58,59] |
Self-orchestration | [62] | [62] | [60,62,64] | [60,62,64] | [52,53] | [60,62,63,64] |
Self-diagnose | [71,72,73] | [71,72,73] | [47,70,71,72,73] | [47,48,67,68,69,70,71,72,73] | [67,70,71,72,73] | [46,47,48,65,67,68,69,70,71,72,73] |
Self-healing | [72,73,80] | [72,73,80] | [72,73,78,80] | [48,72,73,74,76,77,78,79,80] | [72,73,74,75,78,80] | [48,72,73,74,76,77,78,79,80] |
Self-scaling | [72,82,83,84,85,86,87] | [72,82,83,84,85,86,87] | [72,86,87] | [72,86,87] | [72,86] | [72,86,87,88] |
Self-configuration | [72,73,94,95,97] | [72,73,94,95,97] | [72,73,97] | [72,73,93,97] | [72,73,90,91,93,96,97] | [72,73,91,92,97] |
Self-optimization | [99,102] | [99,102] | [100,101,102] | [100,101,102,104] | [100,101,102,103,104] | [100,101,102,104] |
Self-adaptation | [107,110,111,112,113] | [107,110,111,112,113,114] | [48,114] | [109,114] | [48,114] | |
Self-learning | [117,119,122] | [116,118,121] | [49,116,120] | [49,116,117,118,119,120,121] | [117] | [49,116,117,118,119,120,121] |
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S-Julián, R.; Lacalle, I.; Vaño, R.; Boronat, F.; Palau, C.E. Self-* Capabilities of Cloud-Edge Nodes: A Research Review. Sensors 2023, 23, 2931. https://doi.org/10.3390/s23062931
S-Julián R, Lacalle I, Vaño R, Boronat F, Palau CE. Self-* Capabilities of Cloud-Edge Nodes: A Research Review. Sensors. 2023; 23(6):2931. https://doi.org/10.3390/s23062931
Chicago/Turabian StyleS-Julián, Raúl, Ignacio Lacalle, Rafael Vaño, Fernando Boronat, and Carlos E. Palau. 2023. "Self-* Capabilities of Cloud-Edge Nodes: A Research Review" Sensors 23, no. 6: 2931. https://doi.org/10.3390/s23062931