A Resource Management Method for Fog-DSDN Networks Using Microservices Architecture and Echo State Networks (ESN)
Subject Areas : electrical and computer engineeringA. Payamani 1 , Ehsan Matinfar 2 , E. Maghami 3 , M. Janbozorgi 4
1 - Elec. and Comp. Faculty, Dorud Branch, National University of Skill, Dorud, Iran
2 - Dept. of Comp., Dorud Branch, Islamic Azad University, Dorud, Iran
3 - School of Electrical and Computer Engineering,
4 - Elec. and Comp. Faculty, Dorud Branch, National University of Skill, Dorud, Iran
Keywords: Fog computing, software-defined distributed networks (SDDN), resource management, deep learning.,
Abstract :
In Fog–DSDN (Software Defined Distributed Network) environments, optimal resource allocation among multiple nodes with temporal, processing, and memory constraints represents a fundamental challenge. Existing approaches often lack adaptive mechanisms to address rapidly changing network conditions, resulting in increased service latency and reduced resource efficiency. This research presents an innovative method based on a microservices architecture and an Echo State Network (ESN) for managing and optimizing resource allocation in Fog–DSDN. In this approach, each microservice independently handles local data collection and processing, with its output aggregated as an Information Map at the fog controller level. Subsequently, the ESN model learns temporal traffic patterns to predict future node processing loads, enabling adaptive resource allocation decisions. A dual-layer design of input and output queues within each information cell further reduces processing congestion and improves system response time. To evaluate the performance of the proposed method, simulations were conducted in OMNeT++ across diverse traffic scenarios, and results were compared against two baseline systems: a microservices architecture without load prediction, and a base non-hierarchical Fog-DSDN model employing the TFS resource management method. Experimental results demonstrate that the proposed method achieves, on average, a 12.57% improvement in processing resource efficiency, an 18.85% improvement in memory utilization, and a 13.39% reduction in service latency compared to the two baseline architectures and the TFS resource management approach. These findings indicate that the combination of a modular microservices structure with intelligent load prediction using ESN can provide an efficient, scalable, and lightweight solution for resource management in Fog–DSDN.
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