Survey of task scheduling in cloud computing based on particle swarm optimization

Alkayal, Entisar S.; Jennings, Nicholas R.; Abulkhair, Maysoon F.

doi:10.1109/icecta.2017.8251985

Cited by 9 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 41 ] authors have analyzed the PSO-based task scheduling algorithms in Cloud. The authors have classified the current PSO-based research work on the basis of the no of objectives that need to be optimized.…”

Section: Related Workmentioning

confidence: 99%

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

Nabi

Ahmad

Ibrahim

et al. 2022

Sensors

View full text Add to dashboard Cite

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 Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

Nabi

Ahmad

Ibrahim

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…PSO algorithm is one of the most popular algorithms that are used to solve optimization problems in task scheduling processes [18]- [20]. However, scheduling a large number of tasks using PSO algorithm can reduce the system performance [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment

et al. 2019

View full text Add to dashboard Cite

Task scheduling is one of the essential techniques in the cloud computing environment. It is required for allocating tasks to the proper resources and optimizing the overall system performance. Particle swarm optimization (PSO) algorithm is one of the most popular scheduling algorithms, which is used to maximize resource utilization. However, the performance of the PSO scheduling algorithm decreases when the number of tasks is significant. In this paper, the improved PSO (IPSO) algorithm is proposed to provide the optimal allocation for a large number of tasks. This is achieved by splitting the submitted tasks into batches in a dynamic way. The resources utilization state is considered in each creation of batches. After getting a sub-optimal solution for each batch, the algorithm appends all the sub-optimal solutions for batches into a final allocation map. Finally, IPSO tries to balance the loads over the final allocation map. The proposed algorithm is compared with different scheduling algorithms, namely, honey bee, ant colony, and round-robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of makespan, standard deviation of load, and degree of imbalance.INDEX TERMS Cloud computing, task scheduling, load balancing, particle swarm optimization.

show abstract