Brief Announcement: Generative Markov Model for Distributed Computing Systems

• URL: http://arxiv.org/abs/2606.03061v1
• Date: Tue, 02 Jun 2026 02:50:58 GMT
• Title: Brief Announcement: Generative Markov Model for Distributed Computing Systems
• Abstract summary: We propose a general framework for modeling distributed computing systems as a generative Markov model, factorized over a structured system state.<n>This yields a tractable model enabling simulation, inference, and policy learning over otherwise intractable system states.<n>We demonstrate our framework through a case study of collaborative AI inference, in which a dedicated server combines resources with those volunteered by service users.
• Score: 3.756527555629108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emerging distributed computing paradigms, such as the computing continuum, are inherently heterogeneous, stochastic, and complex. Efficiently and effectively utilizing all available resources across the continuum demands a unified formal model of the system. To address this gap, we propose a general framework for modeling distributed computing systems as a generative Markov model, factorized over a structured system state. In our model, the state decomposes into high-dimensional variables, each further factorized over its elements, reflecting the sparse dependency structure inherent to distributed systems. This yields a tractable model enabling simulation, inference, and policy learning over otherwise intractable system states, bridging distributed computing with Markov chain theory and reinforcement learning (RL). We demonstrate our framework through a case study of collaborative AI inference, in which a dedicated server combines resources with those volunteered by service users. Our results show that centralized scheduling becomes a bottleneck at scale, while distributing computation across user devices reduces both latency and server resource consumption. These findings highlight the value of adaptive decision-making in distributed computing systems and demonstrate the framework's utility for modeling, simulation, and optimization.

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