Brief Announcement: Generative Markov Model for Distributed Computing Systems
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 o...
| Authors | Alfreds Lapkovskis, Ali Beikmohammadi, Sindri Magnússon et al. |
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| Primary category | cs.DC |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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