Meta-Computing

What if a computing system could do more than execute a computation-what if it could observe its own computational state, evaluate how that computation is progressing, and adapt its execution strategy accordingly? META-COMPUTING presents a research-based framework for adaptive optimization of AI workloads in distributed computing systems. The book introduces a Meta-Computing Architecture (MCA) that integrates runtime observation, meta-level analysis, adaptive control, and continuous feedback to support self-optimizing computation. The proposed framework brings together the Adaptive Meta-Optimization Algorithm (AMOA), Workload Complexity Index (WCI), Meta-Efficiency Score (MES), and Meta-Computing Feedback Loop (MCFL). These components are supported by a mathematical model describing computational state, workload complexity, optimization decisions, and feedback-driven adaptation. The book further presents a simulation-based evaluation under different workload-complexity conditions. The results indicate improved execution performance for the proposed approach within the evaluated simulation environment, while the limitations of simulation-based validation are explicitly discussed. Beyond the proposed architecture, this work establishes a foundation for further research into adaptive, self-optimizing, and increasingly autonomous computing systems, including applications in distributed, cloud, and edge computing environments.