Pareto Ant Colony Algorithm Based Task Offloading Optimization for Functional Safety Critical Applications in Fog Computing

Hybrid fuzzy clustering with Golden Eagle Optimization Algorithm for fault tolerant load balancing in fog computing environment

Fog Computing (FC), when integrated with emerging 5G technologies, provides significant potential to reduce latency and enhance Quality of Service (QoS). Nevertheless, current scheduling strategies tend to lack the in-depth fault-tolerance and load balancing provisions as virtual machine (VM) health indicators, including CPU utilization, memory status, and reliability are not specifically taken into account during task assignment. To overcome these difficulties, this paper will propose a Hybrid Fuzzy Clustering with Golden Eagle Optimization Algorithm (HFC-GEOA). The framework incorporates capacity aware VM clustering into three levels (HCC, MCC, LCC), fuzzy-based task prioritization and localized metaheuristic optimization, which allows efficient mapping of tasks to VM under resource constraints and adaptive inter-cluster migration. Extensive scaling simulations in iFogSim2 using five task-VM scenarios (1,000-20,000 tasks; 20-1,500 VMs) of 30 independent runs demonstrate that HFC-GEOA is a strong and consistent scale performer. In large scale scenarios HFC-GEOA is used to reduce the average turnaround time by up to 78% over FGELB, 71% over ACO-LWC, and 85% over FCLB. The greatest scenario shows improvement of up to 87% of the average wait time over FGELB and 81% improvement over ACO-LWC and 91% improvement over FCLB. The consumption of energy has been competitive as it has remained 11 to 16% less than ACO-LWC over S3-S5. The stably maintained system reliability at 0.71-0.74 across all scenarios more than twice that of EWOA (0.31-0.33) and the failure rates are kept in control under 2.88 to 3.81%. The fault tolerance score increases progressively when compared to and exceeding EWOA (0.77) and FCLB (0.71) at large scale, and as the system resilience to scale improves. Overall, HFC-GEOA offers a scalable, health-conscious, and fault-tolerant scheduler solution that fully leverages the best latency performance at scale with health-conscious energy usage and stable reliability in heterogeneous fog computing systems.

OPTIMIZED TASK SCHEDULING IN FOG-CLOUD ENVIRONMENTS USING A COST-AWARE GENETIC ALGORITHM

A Hybrid of Bee Colony Optimization and Genetic Algorithm for Task Allocation in Multi-Core Systems to Minimize Makespan

Task scheduling in multi-core systems is a critical NP-hard optimization problem that significantly impacts system performance and resource utilization. This paper proposes a novel hybrid approach combining Bee Colony Optimization (BCO) and Genetic Algorithm (GA) for efficient task scheduling in multi-core processor systems. The hybrid BCO-GA algorithm leverages the global exploration capabilities of BCO and the exploitation strengths of GA to achieve optimal task-to-core assignments while minimizing makespan and balancing system load. The proposed approach incorporates adaptive parameter tuning, elite preservation strategies, and dynamic population management to enhance convergence speed and solution quality. Experimental evaluation using standard benchmark task graphs demonstrates that the hybrid BCO-GA algorithm achieves an average makespan reduction of 18.7% compared to standalone BCO, 15.3% compared to pure GA, and 23.4% compared to the Heterogeneous Earliest Finish Time (HEFT) algorithm. The results also show improved load balancing with a 21.5% reduction in load imbalance factor and 16.8% enhancement in processor utilization. The proposed hybrid approach demonstrates superior performance in handling various task graph characteristics including different Communication-to-Computation Ratios (CCR), task counts, and dependency structures, making it a robust solution for multi-core task scheduling problems.

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.

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.

Energy-Aware Task Scheduling using Grasshopper Optimisation Algorithm

Efficient energy scheduling in heterogeneous computing environments is a critical challenge, as task allocation decisions directly affect both energy consumption and execution performance. This work presents an energy aware scheduling framework based on a discretized grasshopper optimization algorithm (GOA), designed to balance energy reduction with acceptable makespan. The model formulates scheduling as a constrained objectives optimization problem, incorporating energy use, makespan, heterogeneous resource capacities, workflow precedence, and non preemptive execution. A constant aware representation and repair based decoding strategy enable GOA to generate feasible task to resources assignments. Implemented in Python, the framework is evaluated against HEFT, Min and Random scheduling under varying workload. Results show that the schedules based on GOA achieves lower energy consumption and improved performance delay energy while maintaining competitive makespan, with performance gains becoming more pronounced as workload complexity increases. These findings demonstrate the scalability and effectiveness of discretised GOA as a metaheuristic solution for energy aware scheduling in heterogeneous systems.