Jul 2026· International Conference Computing Methodologies and Communication· pp. 759-766· 0 citations· 21 references
Abstract
Task scheduling in multi-cloud-based IoT is a very relevant issue as the demand on energy-efficient and environmentally conscious computing is growing. The proposed study suggests that a Quantum-Inspired Metaheuristic-based scheduling method based on a Quadratic Unconstrained Binary Optimization (QUBO) model could be used to optimize the task allocation among IoT devices over distributed cloud resources. The main goal is to reduce key performance parameters, such as temperature, cost, energy consumption, and carbon emissions, which are essential in sustainable cloud operations. The suggested approach is assessed on the CEA-Curie workload data set in the CloudSim simulation platform and compared to such traditional algorithms as Genetic Algorithm (GA), Deep Reinforcement Learning (DRL), and Asynchronous Advantage Actor-Critic (A3C). The experimental findings show that the QUBO-based algorithm is much better than the current method with an average temperature, operational cost, energy consumption, and carbon reduction of 21.8%, 19.5%, 24.3%, and 22.7% respectively relative to baseline algorithms. These enhancements indicate that quantum-inspired optimization is effective in solving more complicated multi-objective scheduling issues. The results indicate that the suggested model offers a scalable and sustainable solution to next-generation IoT-cloud ecosystems, which helps increase the impact of reduced environmental impact and improved resource efficiency in multi-cloud systems.
The rapid advancement of technology like Internet of Things (IoT) and Cloud computing (CC)based heterogeneous environment required dynamic resource management system. Thecomplexity of IoT-Cloud is increasing due to abundance of dynamic data dissemination thatcreate poor performance like high energy consumption (EC), workload imbalancing, pooradaptability, and fail to handle SLA violations. The two primary contributions of the proposedwork are the Optimized Priority-Aware Hierarchical Multi-Agent Deep Q Network (OPHMDQN)and the Adaptive Multi-Objective Dung Beetle Optimization Algorithm (ADBOA). Themulti-agent strategies increase the scalability and adaptability of resource managementthrough local and global hierarchies. The approach integrates information-based decisionmakingand priority-aware allocation while accounting for SLA requirements, systemconstraints, and job complexity to optimise resource generation, utilisation, allocation, andtask scheduling. In comparison to existing optimization and Reinforcement Learning (RL)techniques, experimental results show that the proposed OPHM-DQN-ADBOA frameworkconsistently reduces EC (up to 30 % lower), execution delay, and SLA violations whileimproving resource utilisation and LB. The ADBOA enhances the proposed model throughoptimal multi-objective training, reducing EC, SLA violation, and cost while improvingresource utilization and scheduling efficiency. As a results, the model achieves high scalabilityand adaptability in heterogeneous IoT-Cloud resource management.
A. Ali, A. R. Mohamed Shanavas· THE SCIENTIFIC TEMPER· 0 citations
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.
This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared with baselines.
Pallavi Mettupalli Venkata, Thatikonda Supraja, Priyanka Chawla et al.· Journal of Supercomputing· 0 citations
Improvements in memetic-based hybrid scheduling underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures.
Chennoji Sandhya, Mandla Alphonsa, Vankudoth Biksham et al.· International Journal of Int...· 0 citations
Experimental results demonstrate that the proposed method achieves superior efficiency, resource utilization, and scalability, making it a promising approach for optimizing task scheduling in dynamic cloud computing environments.
Ratnakumari Neerukonda, B. Hariharan· Discover Computing· 0 citations
This research proposes a cost-aware, genetic-based task scheduling algorithm tailored for fog-cloud environments, which seeks to improve cost efficiency for real-time applications with strict deadlines, and demonstrates that the proposed algorithm surpasses existing techniques like Round-Robin and Trade-off algorithms.
Youssef Oukissou, Hamza Elhaou, Driss Ait Omar et al.· 1 citation
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