PSO+LOA: hybrid constrained optimization for scheduling scientific workflows in the cloud

Li, Huifang; Wang, Danjing; Abreu, Julio Ruben Cañizares; Quan, Zhao; Pineda, Orlando Bonilla

doi:10.1007/s11227-021-03755-y

Cited by 18 publications

(2 citation statements)

References 29 publications

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“…Mating process increments the growth in population of lion and helps in exchanging the information among the members in pride. In each pride, % 𝑋𝑋 𝑚𝑚𝑚𝑚 of female lions intimate with one or more randomly selected males resident lion from the same pride to produce the offspring [56]. However, the nomad female lion mates with one of the randomly selected male among the nomads.…”

Section: Lion Optimization Algorithmmentioning

confidence: 99%

Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

Vijh¹,

Pandey

Gaurav³

2022

Neural Comput & Applic

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This paper presents an efficient skull stripping method to improve the decision-making process. Extended weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu's and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%).

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Section: Lion Optimization Algorithmmentioning

confidence: 99%

Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

Vijh¹,

Pandey

Gaurav³

2022

Neural Comput & Applic

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“…The algorithm assigns resources to each workflow task by the available budget and assigns the task to VM with the earliest finish time to ensure that the workflow makespan is minimized. Li et al 28 proposed the PSO + LOA algorithm can achieve a good balance between budget and the makespan of workflow. The hybrid algorithm can effectively restrain premature convergence and enhance convergence accuracy.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

Fan

Zhang

2022

Concurrency and Computation

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Summary The elasticity and pay‐as‐you‐go features of cloud computing are popular with customers, and more and more workflow applications are migrating to cloud platforms. Many workflow scheduling algorithms aim to obtain minimal rental costs. However, most of the existing research assumes that task execution time is deterministic. In fact, due to the performance fluctuations of VMs, the task execution time is uncertain before scheduling. Furthermore, many works ignore the cost savings given by multi‐cloud systems. To this end, this paper provides a scheduling framework for real‐time workflows. The framework includes four main components: the workflow analyzer, task pool, task allocation controller, and resource manager. Then based on the framework, we propose the RWSMC heuristic algorithm. The algorithm's goal is to minimize the total rental cost while satisfying the deadline constraints and ensuring the reliability of task execution. The RWSMC algorithm reduces the cost by selecting the appropriate billing mechanism based on the task's execution time and mitigates the impact of uncertain execution time by scheduling the task to the VM with the shortest predicted start time. Simulation experiments demonstrate that our proposed algorithm outperforms three recent state‐of‐the‐art scheduling algorithms in the total rental cost, deadline violation rate, and VM resource utilization.

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A Modified-PSO Algorithm to Schedule Scientific Workflows in Cloud

Sriperambuduri,

Nagaratna

2023

Cognitive Science and Technology

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PSO+LOA: hybrid constrained optimization for scheduling scientific workflows in the cloud

Cited by 18 publications

References 29 publications

Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

Uncertainty‐aware scheduling of real‐time workflows under deadline constraints on multi‐cloud systems

A Modified-PSO Algorithm to Schedule Scientific Workflows in Cloud

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