arXiv:2606.03061v1 Announce Type: cross 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 t
The increasing complexity and heterogeneity of distributed computing, particularly in paradigms like the computing continuum, necessitate new modeling approaches as AI integration scales.
A unified formal model for distributed computing systems is crucial for efficiently managing and optimizing resources, which directly impacts the performance, scalability, and cost of AI and other complex networked applications.
The proposal of a generative Markov model offers a more robust and adaptable framework for understanding and controlling distributed systems, potentially leading to better resource utilization and system resilience.
- · Cloud computing providers
- · Distributed AI developers
- · Systems architects
- · Edge computing companies
- · Legacy distributed system management tools
- · Inefficient resource allocation strategies
Improved performance and reliability of large-scale distributed AI systems.
Reduced operational costs for data centers and computing continuum deployments due to optimized resource scheduling.
Acceleration of truly intelligent and autonomous distributed systems capable of self-optimization and self-healing.
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Read at arXiv cs.LG