This paper explores the integration of artificial intelligence techniques into software engineering practices to optimize distributed systems for scalable machine learning (ML) workflows. As ML models grow in complexity and data volume, traditional system design approaches struggle to meet the demands of performance, scalability, and resource efficiency. We propose an AI-driven framework that leverages predictive analytics, automated resource management, and intelligent scheduling to enhance distributed computing environments. The study examines key challenges in distributed ML systems, including data partitioning, workload balancing, fault tolerance, and latency optimization. Through a combination of simulation and real-world case studies, we demonstrate how AI-based optimization strategies improve system throughput, reduce training time, and enhance resource utilization. The results highlight the potential of combining software engineering principles with AI-driven decision-making to build resilient and efficient ML infrastructures. This work contributes a structured approach for designing next-generation distributed systems capable of supporting large-scale machine learning applications.
Yuki Tanaka· International Journal of Art...· 0 citations
Modern computational systems increasingly operate under scalability conditions in which resource capacity, latency, workload variability, operational cost, reliability, and governance constraints interact rather than occur independently. Conventional scalability mechanisms frequently optimize isolated parameters and therefore struggle when constraints conflict or change dynamically. This paper proposes ScaleIntellect, an intelligent combinatorial Large Language Model (LLM) framework designed to reason over heterogeneous scalability constraints and construct adaptive solution combinations. The framework conceptualizes scalability as a flexible constraint-solving problem in which LLM-based semantic reasoning is combined with constraint representation, candidate generation, combinatorial evaluation, conflict detection, and adaptive policy selection. Its theoretical foundation integrates systems flexibility, rule-based reasoning, necessity-oriented decision analysis, and ethical AI considerations. The proposed architecture extends the combinatorial scalability perspective identified by Ramamurthy, Bellamkonda, and Amanmadov (2026), while introducing an adaptive reasoning layer capable of interpreting changing operational contexts. The analysis indicates that scalability decisions are more effectively represented as coordinated constraint portfolios than as single-variable optimization tasks. The framework also demonstrates the importance of distinguishing hard constraints from soft constraints, evaluating trade-offs explicitly, and maintaining governance controls when LLMs participate in infrastructure decisions. The resulting model provides a conceptual foundation for adaptive scalability management across cloud computing, distributed services, AI workloads, and other dynamic computational environments. Limitations include dependence on the quality of constraint specifications, potential LLM reasoning inconsistency, computational overhead, and the absence of empirical benchmarking in the present conceptual study.
Yuki Tanaka· International journal of dat...· 0 citations
The effectiveness of automated feature engineering is proved by experimental results that show that the method can enhance the accuracy and robustness of models as well as improve generalization with respect to the multiple benchmark datasets.
Yuki Tanaka, Kenji Sato· International Journal of Art...· 0 citations
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