Baskar R, Mohanraj E, Saradha M, Monika R
Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode, Namakkal, 637 215, Tamil Nadu, India.
Department of Artificial Intelligence and Data Science, K. S. Rangasamy College of Technology, Tiruchengode, Namakkal, 637 215, Tamil Nadu, India.
Sci Rep. 2025 Jan 7;15(1):1240. doi: 10.1038/s41598-025-85142-8.
The fog computing paradigm is better for creating delay-sensitive applications in Internet of Things (IoT). As the fog devices are resource constrained, the deployment of diversified IoT applications requires effective ways for determining available resources. Therefore, implementing an efficient resource management strategy is the optimal choice for satisfying application Quality of Service (QoS) requirements to preserve the system performance. Developing an effective resource management system with many QoS criteria is a non-deterministic polynomial time (NP) complete problem. The study applies the Hybrid Prairie Dog and Dwarf Mongoose Optimisation Algorithm-based Resource Scheduling (HPDDMOARS) Technique to effectively position IoT applications and meet fog computing QoS criteria. This HPDDMOARS technique is formulated as a weighted multi-objective IoT application placement mechanism which targets optimizing the three main parameters that considered energy, cost and makespan into account. It employed Prairie Dog Optimization Algorithm (PDOA) for exploring the possibility that helps in mapping the IoT services to the available computing services in fog computing scenario. It also derived the significance of Dwarf Mongoose Optimization Algorithm (DMOA) which helps in exploiting the local factors that helped in satisfying at least one objective of QoS index. It hybridized the benefits of PDOA and DMOA mutually for the objective of balancing the phases of exploration and exploitation such that potential mapping between the IoT tasks and the available computational resources can be achieved in the fog computing environment. The experimental validation of the proposed HPDDMOARS achieved with different number of IoT applications confirmed minimized energy consumptions of 22.18%, reduced makespan of 24.98%, and lowered cost of 18.64% than the baseline metaheuristic application deployment approaches.
雾计算范式更适合在物联网（IoT）中创建对延迟敏感的应用程序。由于雾设备资源受限，多样化物联网应用的部署需要有效的方法来确定可用资源。因此，实施高效的资源管理策略是满足应用程序服务质量（QoS）要求以保持系统性能的最佳选择。开发一个具有许多QoS标准的有效资源管理系统是一个非确定性多项式时间（NP）完全问题。该研究应用基于混合草原犬鼠和矮猫鼬优化算法的资源调度（HPDDMOARS）技术来有效地定位物联网应用程序并满足雾计算QoS标准。这种HPDDMOARS技术被制定为一种加权多目标物联网应用放置机制，其目标是优化考虑能源、成本和完工时间的三个主要参数。它采用草原犬鼠优化算法（PDOA）来探索可能性，这有助于在雾计算场景中将物联网服务映射到可用的计算服务。它还推导了矮猫鼬优化算法（DMOA）的重要性，该算法有助于利用局部因素，从而有助于满足至少一个QoS指标目标。它将PDOA和DMOA的优点相互结合，以平衡探索和利用阶段，从而在雾计算环境中实现物联网任务与可用计算资源之间的潜在映射。通过不同数量的物联网应用对所提出的HPDDMOARS进行实验验证，结果表明，与基线元启发式应用部署方法相比，能耗最小化了22.18%，完工时间减少了24.98%，成本降低了18.64%。
