CARE-FSP: A Cost-Aware and Resource-Efficient Hybrid RSO–SFO Framework for Fog-Based IoT Service Placement

QoS-aware and energy-efficient metaheuristic optimization based service placement strategy for fog-based IoT applications

Optimal placement of services is a major challenge in the context of fog computing due to the resource-constrained and heterogeneous nature of nodes. Services must be placed optimally without overloading the nodes to fulfill the increasing demand of Internet of Things (IoT) applications. Energy consumption should also be minimized, along with the provision of Quality of Service (QoS). Typical problems of existing metaheuristic approaches, such as premature convergence, limited exploration capability, and high computational cost, result in suboptimal service placement. To solve this problem, we propose a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS. In this metaheuristic optimization-based strategy, Differential Evolution (DE) is integrated with JAYA-based exploitation, which explores the search space, which in turn accelerates convergence toward optimum service placement in fog computing environments. This proposed strategy accounts for both communication and computational energy consumption when placing service modules, thereby enhancing execution efficiency and optimizing service delay and network usage in constrained fog computing settings. The experimental results demonstrate that the developed hybrid algorithm reduces energy consumption by 3.09% compared to the existing baseline approaches, while optimizing delay and minimizing network usage. Additionally, the proposed approach has been found to minimize network usage significantly compared with baselines.

Framework Evaluation for Energy-Efficient Resource Allocation in Industrial IoT Using Hybrid Swarm Optimization: Comparative Analysis by Integrating Sand Cat Swarm Optimization and Moth Flame Optimization with SVM-Based Path Quality Assessment

Industrial Internet of Things (IIoT) environments generate enormous volumes of data that must be routed through cloud infrastructure with strict constraints on energy consumption, network latency, and service reliability. Existing resource allocation frameworks often rely on single-objective heuristics that fail to balance competing performance objectives across large-scale virtual machine (VM) deployments. The proliferation of heterogeneous IoT sensor nodes, each with distinct energy profiles and trust characteristics, further complicates optimal path selection in multi-hop cloud networks. These challenges demand intelligent optimization frameworks capable of simultaneously minimizing routing cost, energy usage, and latency while maximizing throughput and node trustworthiness.This paper proposes a novel hybrid swarm intelligence framework for energy-efficient resource allocation in Industrial IoT networks. The framework integrates two biologically inspired metaheuristic algorithms: Sand Cat Swarm Optimization (SCSO), which mimics the vibration-based hunting behaviour of sand cats to perform global exploration across candidate routing paths, and Moth Flame Optimization (MFO), which employs logarithmic spiral movement toward optimal flame positions to execute precise local refinement. The two-phase pipeline feeds the entire SCSO candidate population directly into the MFO refinement stage, enabling the hybrid to escape local optima that afflict standalone algorithms. A Support Vector Machine (SVM) classifier acts as a quality gate, filtering paths whose average trust score falls below 0.5 or whose average network latency exceeds 60 milliseconds. The framework is evaluated on a real-world dataset comprising 500 virtual machine nodes recorded at five-minute intervals, with 16 normalized feature dimensions including CPU utilization, memory usage, network throughput, latency, power consumption, response time, and SLA violation rate. Experimental results demonstrate that the Hybrid SCSO-MFO achieves a path efficiency of 95.3% at 300 nodes, representing improvements of 6.4 percentage points over GNN-based intrusion detection methods and 10.6 percentage points over dynamic graph transformer approaches. Energy consumption along optimal paths is reduced by 18.7% compared to standalone MFO and by 26.4% compared to standalone SCSO. Convergence is achieved 34% faster than MFO alone. These results confirm that the proposed framework delivers superior energy efficiency, network reliability, and scalability for IIoT cloud deployments.

Toward efficient and reliable fog computing: a mega node-based service placement strategy with improved metaheuristic optimization

Optimized task offloading and resource allocation framework for edge-assisted IoT applications

This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.

Optimization of Service Function Chain Placement in Cloud-Fog-Edge Networks

There is an explosion in IoT devices, 5G technology, and MECs, that results in increasing demands on effective and scalable network services management. Service function chaining, defined as the sequence of functions in VNFs on a path, is one of the core principles behind the NFV architecture design. SFC allocation to the heterogeneous clouds–fogs–edges network is an NP-hard problem characterized by mutually conflicting goals, such as latency minimization, energy and cost reduction, and resource maximization. In this study, an in-depth comparison study is carried out on three population-based optimization algorithms for solving the placement problem of SFCs using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO) on three cases: (1) VNF deployment cost/QoE optimization in a 5G hybrid cloud with 12 nodes and weighting factor γ=0.4; (2) SFC graph matching on MEC-NFV networks with a 100-node physical network, 20 VNFs, and equal utilization weights α=β=γ=1/3; and (3) multi-instance SFC mapping on Fog-to-Cloud (F2C) IoT environment with a 5-VNF chain across 5 nodes. These three algorithms have been evaluated under identical conditions: 10 independent runs, 200 iterations, 20–30 agents. Results demonstrate that GWO achieves the best VNF deployment objective (W =73.92, a 15.1% improvement over the BGWO baseline), PSO achieves the highest resource utilization (52.9%) in MEC-NFV placement, and both PSO and GWO reduce F2C end-to-end latency by 25% compared to the ILP reference (12 vs. 16 units at three instances), while all three algorithms reduce latency by approximately 80% relative to cloud-only deployment. PSO emerges as the most consistently high-performing algorithm across all three scenarios.

Multi-objective formulation of efficient microservice deployment in Kubernetes with dynamic resource allocation using hybrid nature-inspired algorithm

Traditional Kubernetes microservice deployment methods often suffer from static resource allocation, leading to inefficient resource usage, high operational costs, and an inability to handle dynamic service dependencies in real-time. To address these challenges, this framework proposes a Hybridized Clouded leopard and Cock-hen-chicken Optimization (HCCO) model. The primary innovation of this research is the development of an adaptive switching framework that integrates Clouded Leopard Optimization (CLO) for robust global exploration with Cock-hen-chicken Optimization (CHCO) for hierarchical local refinement. The rationale for selecting this hybrid approach over other meta-heuristics, such as the Black Hole Algorithm, is its superior ability to avoid premature convergence in high-dimensional search spaces; while the Black Hole Algorithm often struggles with maintaining diversity during rapid resource shifts, HCCO utilizes a dynamic switching variable to ensure a balanced and faster search process. Experimental validation shows that the developed work achieves significant numerical improvements, including a throughput of 91.62%, a resource usage cost reduction of 9.61%, and a minimized execution time of 17.9 min. Ultimately, the HCCO framework provides a highly efficient and scalable solution for optimizing cost and performance in dynamic, real-time cloud environments.