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Enhancing Dependability Design for Large-Scale Systems: The Role of Novel AI Techniques in IoT and Supercomputers


This study is intended to propose a research procedure regarding the analysis of AI-Driven Approaches for Dependability Design in Large-Scale Systems.  A platform for the evaluation based on scrutinizing the techniques, their benefits, limitations and attributes of supercomputers are reflected through the study. Research model and Theory would make the study justified in its research form to provide a significant outcome. Research methodology has been highlighted to the researchers for further investigation through a certain process of data collection and analysis. These are necessary to evaluate the variables of the objectives pointed out in this study to gain insights from the research. 

1. Introduction 

1.1 Introduction to the industry 

Currently, the availability of the IoT and supercomputing has greatly impacted different industries because it has allowed the connectivity and processing of large amounts of data on a large scale The implication of Artificial Intelligence (AI) in the. IoT is defined as a complex of related devices which interact and exchange large amounts of information to provide increased productivity or solutions to common problems in the spheres, for example, smart cities, healthcare or industry. Supercomputers are computation-intensive machines that can deal in accomplishing a great deal in the field of scientific discoveries, meteorological inquiries as well as big data processing (Hajjaji et al. 2021). 

Figure 1: Global Market Growth of Artificial Intelligence 

(Source: Grandviewresearch, 2024) 

As the figure depicts the current market size of AI is around $196.63 billion in 2023 globally. With a global Market, a forecast of 36.6% CAGR is represented till 2030. North America’s prominence of around 37.7% of global revenue has dominated the sector and software solutions accounted for more than 37.8% in 2022 (Novaoneadvisor, 2023). 

Some of the key vendors that provided solutions in the Al in loT market are IBM, Google, and Facebook to help the business leverage real-time data towards, perhaps, efficient delivery of customer value proposition. Some of the strategic priorities that IBM has adopted include; growing its platforms, achieving productivity via automation, integrating Al into its services which as well and investing in cloud assets. The spending on research and development was significantly higher in 2017 than that in 2015 in the case of the company (Grandviewresearch, 2024). The COVID-19 outbreak forced the market of next-generation tech domains such as artificial intelligence to grow with work-from-home policies. The companies that deal in Software-as-a-Service Technology (SaaS) and cloud-based customer engagement and remote connectivity & collaboration services have recorded a high number of new sign-ups globally (Marketsandmarket, 2024). 

1.2 Research problem 

However, the continuities inherent in IoT and supercomputing introduce numerous challenges concerning dependability due to their inherently complex system and distribution. The failure of the system results in a disaster and huge losses such as monetary misappropriation, risks to human life, and leakage of information. As opined by Alzubaidi (2023), regular approaches to achieving dependability are ineffective because such systems are diverse and have constantly varying features. Introducing the dependability design with the help of AI and in particular, NLP requires invigorating methods of improvement that would reduce the risk factors. This might include fault detection, system diagnostics and predictive maintenance. 

1.3 Aim 

The primary aim of this research is to develop advanced Natural Language Processing (NLP) techniques that can enhance dependability in large-scale IoT and supercomputing systems. Along with the integration of NLP with dependability design, the study seeks to address existing challenges and improve the reliability 

1.4 Research objectives  

RO1: To analyze the current state of dependability design in IoT and supercomputing systems. 

RO2: To employ NLP techniques to identify potential issues and failures in large scale systems. 

RO3:  To develop and validate AI-based models for enhancing dependability in large-scale systems 

RO4: To examine the effectiveness of the proposed NLP techniques through simulation and real-world context 

1.5 Research questions  

Based on the objectives some of the susceptible questions are as follows: 

RQ1: What are the main dependability challenges faced by large-scale IoT and supercomputing systems? 

RQ2: How can advanced NLP techniques be integrated into large-scale IoT and supercomputing systems to enhance dependability? 

RQ3: How effective are these NLP techniques in improving the dependability of large scale computing systems? 

RQ4: What are the specific benefits and limitations of using AI-based approaches in large-scale computing systems? 

1.6 Research importance 

This research has valuable implications for both academic and industrial environments. In the academic literature, it helps to advance knowledge on how AI can be utilized in dependability design and contributes new approaches. Thus, for industry, the above-discussed findings may contribute to designing better-experienced IoT and supercomputing systems, free from potential pitfalls and optimizing developmental processes. However, enhanced dependability in such systems can lead to less time wasted and more confidence from the users, to encourage the utilization and development of such systems. 

2. Literature Review  

2.1 Evaluation of dependability design in IoT and supercomputing systems 

Similar to other fields of dependability design, the IoT and supercomputing systems are assessed on reliability, availability, and maintainability. Such systems are inherently complex because of the size, diversity, and volatility incurred in their environments. According to Caporuscio et al. (2020), dependable system design paradigms like redundancy, failover means and manual failure diagnoses are inadequate to solve these issues. In this category, reliability is paramount because IoT devices are usually deployed in uncertain situations. Reliability is measured using tools such as Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR) (Sellitto and Pinho, 2023). 


Figure 2: Framework of MTBF Metric 

(Source: Ramtechno, 2022) 

However, the availability of numerous devices and heterogeneity of the working conditions, different from typical industrial conditions, are not enough for applying traditional reliability models. High-performance computing systems, specifically supper computing systems are likely to encounter reliability issues due to a large number of components attached to them and complications in terms of operations. 

Availability in service associated with IoT systems is graded. As depicted by Thomas and Cholia (2021), supercomputers used for large-scale data management and analysis must have greater availability so that they can function seamlessly while processing data. Major strategies proposed in the past, such as hardware redundancy, are costly and often not very adaptable. Maintainability means the creation of affords and conditions that define the capability of equipment for performing a maintenance task with specified parameters. According to Khanna and Kaur, (2020), in IoT systems, the major problem attributed to devices is the fact that devices are distributed across different locations hence making it difficult to maintain them. Indeed, managing and diagnostics of such complex and detailed architecture as supercomputers presupposes the usage of quite specialized tools. 

2.2 Specific benefits and limitations of using AI-based approaches in large scale computing system 

AI-based approaches offer significant benefits for dependability design in large-scale computing systems.  The implication of Machine Learning (ML) and Natural Language Processing (NLP) can predictably enhance the maintenance process (Shreda and Hanani, 2021). 

2.2.1 Benefits 

Improved Diagnosis: AI can sift through masses of data coming from sensors and logs and observe patterns that can be indicative of failures.  As opined by Li et al. (2022) NLP tools can help with unstructured data analysis, for instance with logs that record maintenance status or user complaints. This makes it possible to diagnose faults within a short span of time and this is also accurate. 

Predictive Maintenance: Preventive maintenance using machine learning models The model above can be used to forecast when a specific machine is likely to fail, from the history of other similar machines therefore allowing for prospective maintenance. As depicted by Teoh et al. (2021), it minimizes the time that the equipment may take to be out of service, hence, the expenses on frequent maintenance. For instance, the deep learning models built can be used for the time series analysis of data which will help predict when a certain component is likely to fail so that necessary corrective measures can be taken promptly. 

Enhanced Scalability: Interfacing the emerging technologies implies that AI algorithms can support IoT and supercomputing systems. It can further evolve the ability to learn and thus respond to variations in the environment and constantly enhance its own performance. 

2.2.2 Limitation 

Data QualityAssurance: Training AI models needs high quality data which is vast in volume. This implies that if information acquired is either inconsistent or incomplete then the predictions and diagnostics carried out will also be wrong.  

Computational Resources: Supercomputing is typically needed when training and deploying deep learning models, which are commonly recognized as AI models. This can be a limitation, especially in areas where there is always a limited amount of specific resources in the environment (Chen et al. 2020).  

Integration and Reliability Challenges: AI implementation into the system is always challenging and often causes major changes to system infrastructure and operations. Maintaining a balance between the frequency and process needs to be maintained; otherwise it would fail to function and results to the superfluous actions. Focusing on the dependability of certain actions are significant to overcome the major concern for shaping the reliability aspect. 

2.3 Theoretical Approach 

Technology Acceptance Model (TAM) is relatively functional in the context of modern technological integration and large scale system dynamics. It paves the flowing platform for AI and IoT enabled devices to be active on supercomputers. As per the opinion of Sagnier et al. (2023), this model enables the researcher to comprehend the acceptability ration of user and the updated behaviours of the modern technologies. NLP and other language programs, thus ensures the innovativeness in this aspect ensuring these innovations are effectively adopted.  

Figure 3: Framework of Technology Acceptance Model 

(Source: Ncl, 2023) 

On the other hand, Technological Parasitism Theory that helps to evaluate the concerns of the technologies’ improvement based on the existed system works as complimentary support system for TAM. It provide services to increase the active participation of the system formulation depending on the present scenario.  

Both of these aspects of Theoretical perspective conceptually provide direction to the technological advancement. As a guiding principle these gain supports of AI enhancement in system dependability.  

3. Methodology 

3.1 Research Approach 

Research approach generally stages the concept of study’s commencement of literature pieces based on the anticipation of their context. This research will adopt an inductive approach to conduct empirical observation to test the objective relation (Proudfoot, 2023). In the context of this study, the research will be built upon existing knowledge of dependability design in IoT and supercomputing systems, as well as the potential applications of AI techniques such as NLP. This will test the variables regarding the efficacy of these AI techniques in enhancing dependability design, 

3.2 Research Philosophy 

Research philosophy shows the factors or beliefs that were preconceived by the researcher while conducting the research. As depicted by Younus and Zaidan, (2022), the components of positivism philosophy assists in comprehending the situational variables with the observation procedure as derived from actual examples in life. It is customary for researchers to employ these methods in the understanding of the importance of this factor towards the use of data. Positivism permits a methodical but versatile investigation of how AI techniques may enhance dependability design, whilst embracing both quantitative data and theoretical principles. 

3.3 Data Collection  

In the context of this research study the common secondary qualitative data research method will be employed. Secondary data sources comprise the existing literature in the form of journals, industry reports, technical papers, case studies and research articles. It would help to contextualize dependability design in IoT and supercomputing systems and that pertains to the use of AI techniques within these fields. A detailed information that allows understanding the prospects of AI use would be grasped from vast availability of online sources. On the other hand, this process will enable me to save time and the overall cost that would be incurred when conducting the primary research methods (Pandey and Pandey, 2021). 

3.4 Data Analysis 

The sorted data collected from the secondary method will be analyzed through the thematic method. This would be executed with the objective of identifying patterns as provided by the researcher, examining those patterns and reporting them on the basis of the observable theme they constitute. According to Vears and Gillam (2022), this method will be appropriate for qualitative research because it enables the researcher to find the true amount of the data. Thematic analysis is important in qualitative research and the identification assists in describing literature under certain conditions and existence in real-life situations. 

4. Conclusion 

In this research proposal dependability design in IoT and supercomputing systems can be developed by AI-supported methods to a remarkable extent with uses in fault identification, assessment and prognosis for the subsequent maintenance. It is necessary to improve the factors that can define the reliability and solidity of these brilliant technologies concentrating on the elimination of the drawbacks that are characteristic of the fundamental methods. The technologies being utilized in the modern IoT and systems of supercomputing can also be used to optimize the data collection and analysis which in turn can reduce the variability and improve dependability. 

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