ArticleSensors (Basel, Switzerland)2022
AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Exemplar Learning and Memory Retrieval-Based Particle Swarm Optimization Algorithm with Engineering Applications.Biomimetics (Basel, Switzerland) · 2025Article
- Optimizing multiprocessor performance in real-time systems using an innovative genetic algorithm approach.Scientific reports · 2025Article
- Efficient deep reinforcement learning based task scheduler in multi cloud environment.Scientific reports · 2024Article
- Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing.Sensors (Basel, Switzerland) · 2023Article
- Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization.Bioengineering (Basel, Switzerland) · 2023Article
- An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization.Sensors (Basel, Switzerland) · 2023Article
- A task scheduling algorithm with deadline constraints for distributed clouds in smart cities.PeerJ. Computer science · 2023Article
- Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
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Abstract
Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely
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