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A Self-Adaptive Runtime for Data-Intensive Graph Analytics in Geo-Distributed Cloud–Edge Systems
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DOI：10.1007/s10723-026-09839-4.png)
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Abstract
En 中文
The grid computing infrastructure requires runtime systems that handle three simultaneous tasks of managing data locations, tracking communication costs, and maintaining system operation stability to execute its large-scale iterative and data-parallel workloads. Existing runtime systems fail to achieve proper dynamic execution conditions because they treat data placement scheduling and synchronization as separate elements, which creates unstable operations. The paper introduces a complete runtime control system that achieves optimal results through its base-driven model for making migration scheduling and coordination choices. The proposed model integrates both locality awareness and migration cost with a Lyapunov-inspired stability control component into a unified feedback system. The formal model establishes computation, communication, and synchronization expenses, which combine with queue-based coordination feedback to direct runtime choices through an established process. The framework evaluation occurs through a controlled event-driven simulation environment that replicates the analytical model's runtime control structure. The evaluation uses various workload intensity configurations to assess how the proposed method performs against typical static and heuristic dynamic runtime strategies. The experimental analysis studies three areas of execution efficiency, communication and synchronization overhead, and runtime stability. The results show that coordination stability integration into runtime control systems creates better execution reliability and execution prediction accuracy for grid-based iterative processing systems, while unified feedback-driven runtime management becomes essential for future large-scale distributed analytics platforms.
Keywords:
Grid computing
Runtime control framework
Stability-aware scheduling
Dynamic migration and coordination
Lyapunov-based control
Event-driven simulation
Iterative and data-parallel workloads
Communication and synchronization overhead
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