Task Scheduling in Fog-Cloud Environments Using an Adapted Lemurs Optimizer
Abasi, Ammar Kamal Ababneh, Nedal Aloqaily, Moayad Guizani, Mohsen
Abasi, Ammar Kamal
Ababneh, Nedal
Aloqaily, Moayad
Guizani, Mohsen
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Machine Learning
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Conference proceeding
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Abstract
Task scheduling in Fog–Cloud computing is a complex multi-objective and NP-hard optimization problem that requires a careful balance among latency, energy consumption, and operational cost within highly heterogeneous and distributed infrastructures. This paper introduces an Adapted Lemurs Optimizer (ALO), which tailors the original Lemurs Optimizer to the Fog–Cloud scheduling domain through task–node-aware initialization and normalized multi-objective fitness evaluation. The proposed ALO efficiently explores the scheduling space while preserving computational simplicity. Extensive experiments conducted with workloads of 100, 200, and 300 tasks across 5- and 10-fog-node configurations demonstrate that ALO consistently outperforms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). On average, ALO achieves 18.6% lower makespan, 14.2% lower energy consumption, and 11.8% lower processing cost. These results confirm ALO’s capability to deliver scalable, energy-efficient, and cost-aware scheduling solutions suitable for real-time Fog–Cloud environments.
Citation
A.K. Abasi, N. Ababneh, M. Aloqaily, M. Guizani, "Task Scheduling in Fog-Cloud Environments Using an Adapted Lemurs Optimizer," 2026, pp. 1-6.
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2026 IEEE International Conference on Consumer Electronics (ICCE)
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software
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2026 IEEE International Conference on Consumer Electronics (ICCE)
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IEEE