32 lines
2.0 KiB
TeX
32 lines
2.0 KiB
TeX
\begin{abstract}
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\normalsize
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A virtual machine architecture and adaptive execution system are disclosed that
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dynamically modifies internal runtime behavior in response to real-time workload
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characteristics. The system maintains a continuously updated runtime state vector
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representing execution heat, entropy, variance, temporal decay dynamics, pipeline
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pressure, and related stability indicators. A plurality of coordinated feedback
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loops adjust internal parameters including decay functions, inference weights,
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lookup strategies, cache behavior, and prefetch timing. A supervisory controller
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selects among multiple execution modes, each mode defining a distinct and
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validated configuration of feedback-loop activity, and regulates transitions
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using bounded, non-oscillatory mechanisms such as hysteresis thresholds,
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confidence scoring, or convergence constraints.
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\par\medskip
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The system characterizes workloads into behavioral families—including stable,
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temporal, volatile, transitional, and mixed patterns—and autonomously selects the
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execution mode appropriate to each condition. This enables the virtual machine to
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maintain consistent performance, reduced variance, and shape-invariant behavior
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across heterogeneous, non-stationary, and waveform-diverse workloads. The
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architecture operates without manual tuning or fixed parameters and is robust to
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sudden workload shifts, burst-like behavior, and long-term temporal drift. The
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invention is applicable to stack-based interpreters, embedded runtimes,
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just-in-time compilation environments, microkernel subsystems, distributed
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execution engines, and real-time or soft–real-time systems requiring predictable
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and stable execution characteristics. The disclosed architecture provides a
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general-purpose method for achieving autonomous optimization, rapid convergence
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to steady-state behavior, and high stability through integrated feedback,
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statistical inference, and supervisory mode selection.
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\end{abstract}
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