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Application Component Placement and Resource Optimization in Computing Continua
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AI
DOI：10.1109/TSC.2025.3625265.png)
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Abstract
En 中文
The proliferation of the Internet of Things, artificial intelligence, and real-time data processing applications has driven the demand for distributed computing architectures that span cloud, fog, and edge layers in a computing continuum. These architectures must address critical challenges in component placement and resource optimization to ensure low latency, cost efficiency, and compliance with Quality of Service (QoS) constraints. This paper introduces a novel optimization framework for addressing the joint problem of component placement and resource optimization in computing continua. The framework employs a Mixed Integer Nonlinear Programming model, where application components are modeled as a Directed Acyclic Graph and their performance is predicted using analytical models. A method based on the Karush-Kuhn-Tucker conditions is employed to compute the optimal number of virtual machine instances for a given component placement. This optimization is embedded within a reinforcement learning loop that iteratively refines placement decisions in response to fluctuations in workload. This hybrid approach ensures cost effectiveness while adhering to QoS constraints. Extensive experimental evaluations demonstrate the superiority of our framework. It outperforms leading approaches, including BARON solver, SPACE4AI-D, PPO_DLX, and a minimum k-cut baseline, achieving average cost reductions of 19%, 60%, 11%, and 6%, respectively, under dynamic workload conditions. These results highlight the efficiency, scalability, and adaptability of our approach, making it a robust solution to the demands of modern distributed systems.
Keywords:
Edge
cloud computing
component placement
resource optimization
reinforcement learning
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