A Novel Approach for Optimal Service Placement in Fog-Based Internet of Things Using the Cappuccino Optimization
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32834Keywords:
Fog Computing, Internet of Things, Task Scheduling, Metaheuristic Algorithm, Cappuccino OptimizationAbstract
Abstract
With the increasing expansion of the Internet of Things (IoT) and the increasing volume of data generated by devices, the need for fast and efficient processing is increasingly felt. In this regard, fog computing has been proposed as an intermediary approach between cloud computing and edge devices, which aims to reduce latency, improve efficiency, and better resource management. Despite these advantages, challenges such as suboptimal resource allocation, high energy consumption, increased response time, and processing load imbalance are still considered major obstacles in this field. In order to overcome these problems, this research presents a novel approach based on the Cappuccino optimization algorithm that focuses on improving task scheduling and location of IoT services in the fog environment. The proposed algorithm is designed as a meta-heuristic method and is able to improve the quality of service (QoS) by optimizing resource allocation, reducing energy consumption, and balancing the load between nodes. To evaluate the performance, the algorithm was simulated in three test scenarios with different amounts of physical machines and tasks. The results showed that the proposed method has made improvements compared to reference algorithms such as CAWPQ and CAWTPQ; in one scenario, the scheduling delay was reduced by 12.5%, in another test, the energy consumption was improved by about 18%, and in the third case, the load balancing was increased by 23%. The findings indicate that the use of the Cappuccino algorithm in fog computing can be an effective step in promoting distributed processing, reducing task execution time, and optimizing the location of IoT services. However, the evaluation is based on simulations, and future work should test the approach in real fog computing environments. These achievements draw a positive outlook for future research in the field of fog computing and the Internet of Things.
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