Adaptive Virtual Compute Framework with Autonomous Units and Credibility Assessment Models

Abstract

The rapid evolution of distributed computing ecosystems, particularly cloud and edge infrastructures, has necessitated the development of adaptive and autonomous resource management frameworks capable of responding dynamically to workload fluctuations, trust variability, and heterogeneous system conditions. This paper proposes an Adaptive Virtual Compute Framework (AVCF) that integrates autonomous computational units with credibility assessment models to optimize task allocation, system reliability, and operational efficiency in large-scale virtualized environments. The framework leverages multi-agent coordination principles and quality-driven software engineering metrics to enable self-adaptive decision-making in distributed compute environments.

The core contribution of this study is a structured architecture that combines virtual compute orchestration with credibility scoring mechanisms, enabling systems to evaluate node reliability, execution trustworthiness, and historical performance patterns. This approach extends traditional resource allocation models by embedding predictive credibility layers inspired by established software quality and reliability models (Azuma, 1996; Chidamber & Kemerer, 1994). Furthermore, the framework aligns with modern cloud optimization paradigms emphasizing energy-aware scheduling and trust-centric computation, as highlighted in multi-agent cloud optimization studies (Ramaswamy et al., 2026).

The proposed system incorporates modular autonomous units capable of independently evaluating computational tasks, negotiating resource allocation, and adapting execution strategies in real time. These units are supported by a credibility assessment engine that synthesizes metrics from historical execution logs, fault proneness indicators, and system-level quality attributes (Aggarwal et al., 2007; García-Munoz et al., 2016). The integration of software quality evaluation models ensures that decision-making is not only performance-driven but also reliability-aware.

Experimental reasoning and conceptual evaluation demonstrate that AVCF improves resource utilization efficiency, reduces system-level execution risk, and enhances fault tolerance in distributed environments. Additionally, the framework provides a scalable foundation for integrating future AI-driven orchestration systems and hybrid cloud-edge architectures.

Overall, this research contributes a novel perspective on adaptive virtual computing by combining autonomous system design with credibility-based trust evaluation, offering a robust model for next-generation distributed computing infrastructures.