Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

Khurana, Savita; Singh, Rahul

doi:10.4108/eai.13-7-2018.161408

Cited by 10 publications

(12 citation statements)

References 27 publications

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“…It used PEFT based heuristic algorithm for initialization of the population. 41 The proposed approach reduces the random initialization of the tasks in the workflow.…”

Section: Proposed Nature-inspired Heuristicmentioning

confidence: 99%

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CSFPA: An intelligent hybrid workflow scheduling algorithm based upon global and local optimization approach in cloud

Sharma¹,

Khurana²,

Harnal

et al. 2022

Concurrency and Computation

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Summary In cloud, the most prominent area is workflow scheduling due to its widespread application in different domains. It comes under the NP‐complete problem, henceforth researchers have suggested the nature‐inspired heuristics and metaheuristic algorithms but still, the results of these heuristics are not optimal. These algorithms are not competitive but complementary to each other, so in that case, hybridization may yield better results. Since then, researchers have started to combine different heuristics for better results. But in this process of hybridization, the two most important factors have not been considered yet, namely global and local optimization. The proposed work has considered the two most popular nature‐inspired workflow scheduling Cuckoo Search (CS) algorithm and Flower Pollination Algorithm (FPA) for hybridization. CS works best for global optimization, and FPA provides good results in the case of local optimization. The proposed work leveraged the benefits of both optimizations and proposed a novel hybrid algorithm entitled Cuckoo Search Flower Pollination Algorithm (CSFPA). The proposed hybrid algorithm uses multi‐objective functions to minimize cost and time and thus enhance the utilization of resources. The CSFPA algorithm has been compared with both single nature aspired algorithms and hybrid algorithm for performance evaluation.

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“…It used PEFT based heuristic algorithm for initialization of the population. 41 The proposed approach reduces the random initialization of the tasks in the workflow.…”

Section: Proposed Nature-inspired Heuristicmentioning

confidence: 99%

“…It proposed hybrid enhancement to enhance the efficiency and utilization of virtual machines by considering local and global optimization policies. It used PEFT based heuristic algorithm for initialization of the population 41 . The proposed approach reduces the random initialization of the tasks in the workflow.…”

Section: Proposed Nature‐inspired Heuristicmentioning

confidence: 99%

CSFPA: An intelligent hybrid workflow scheduling algorithm based upon global and local optimization approach in cloud

Sharma¹,

Khurana²,

Harnal

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The hybridization of the GWO and GA algorithm was proposed by Bouzary and Frank 33 who found that the proposed algorithm is superior to GWO and GA in terms of cost. Khurana and Singh 34 have introduced a hybrid of flower pollination algorithm and GWO to reduce the cost and time and provide efficient results in comparison to flower pollination with GA. Table 1 summarizes some existing scheduling algorithms proposed in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

Workflow scheduling using particle swarm optimization and gray wolf optimization algorithm in cloud computing

Arora

Banyal

2021

Concurrency and Computation

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Cloud computing is one of the emerging technologies in computer science in which services are provided through the internet on-demand. Workflow scheduling is considered to be an NP-hard problem and has a significant issue in the cloud environment.Finding the polynomial-time solutions for workflow scheduling problem is difficult with most of the existing algorithms designed for traditional computing platforms. Some existing meta-heuristics algorithms proposed for workflow scheduling problem are stuck in the local optimal solution and fails to give the global optimal solution. In this article, a hybrid of particle swarm optimization and gray wolf optimization, named the PSO-GWO algorithm, is proposed for workflow scheduling. The proposed algorithm was tested to reduce the total executing cost (TEC) and total execution time (TET) of the dependent tasks in the cloud computing environment. The proposed algorithm takes advantage of both the standard PSO and GWO algorithms and does not stick in the local optimal solution. The experiment results show that the PSO-GWO outperformed compared with the standard PSO and GWO algorithm in TEC and TET.

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“…The simulation results proved that the ACO outperforms traditional FCFS and Round-Robin algorithms. The authors [14] developed a hybrid optimization technique using Flower Pollination and Grey Wolf Optimization. The PEFT algorithm was used for the initialization of the proposed method for workflows.…”

Section: Related Workmentioning

confidence: 99%

An HC-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Computing System

Bhagwan¹,

Kumar²

2021

IJACSA

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Many scientists are using meta-heuristic techniques for dynamic workflow task scheduling in the area of cloud computing systems to get optimum solutions. Many swarm intelligent algorithms have been designed so far which are having many limitations as some get trapped in local optima, a few are having low convergence speed, some are having poor global search facilities, etc. Still, there is a requirement of designing a new algorithm or modification of existing algorithms to overcome the limitations of the existing techniques. A new Hybrid Cat Swarm Optimization algorithm named H-CSO was designed inspired by the HEFT algorithm and the initialization problem of the Cat Swarm Optimization was overcome. Still, that algorithm has a limitation of getting stuck in local minima. To overcome this algorithm a part of the Crow Search Algorithm has been integrated into H-CSO and described in this paper. After simulation, it was found that the new hybrid algorithm named HC-CSO outperforms CSO and H-CSO.

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Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

Cited by 10 publications

References 27 publications

CSFPA: An intelligent hybrid workflow scheduling algorithm based upon global and local optimization approach in cloud

CSFPA: An intelligent hybrid workflow scheduling algorithm based upon global and local optimization approach in cloud

Workflow scheduling using particle swarm optimization and gray wolf optimization algorithm in cloud computing

An HC-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Computing System

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