Brief Announcement: Generative Markov Model for Distributed Computing Systems
For researchers in distributed computing and RL, this provides a unified formal model for heterogeneous systems, though the contribution is incremental as it applies existing Markov chain and factorization techniques to a new domain.
The paper proposes a generative Markov model for distributed computing systems, factorized over structured state to enable tractable simulation and optimization. In a case study of collaborative AI inference, distributed scheduling reduced latency and server resource consumption compared to centralized scheduling.
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.