Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm

Chakravarthi, Koneti Kalyan; Shyamala, L.; Vaidehi, V.

doi:10.1007/s10489-020-01875-1

Cited by 36 publications

(10 citation statements)

References 40 publications

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“…Authors in References 43‐50 considered the cost minimization as performance metric for scheduling the single workflow without any constraints. Authors in References 51‐53 optimized the makespan of the workflow application.…”

Section: Application Of Gtlbo In Workflow Scheduling Problem On Cloudmentioning

confidence: 99%

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Ram

Srivastava

Mishra

2021

Concurrency and Computation

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Teaching learning‐based optimization (TLBO) was proposed by Rao to solve optimization problems. It is based on the theory of teaching‐learning mechanism. Although it performs well in unimodal problems yet its performance is not good in multimodal problems. To further improve this algorithm's performance and make it suitable for both unimodal problems and multimodal problems, we made some major changes in the theory and the algorithm's operators. The proposed algorithm is able to capture diverse optimal solutions in less number of iterations and is very good for solving multimodal problems. This newly created variant of TLBO is named generalized TLBO (GTLBO). The performance of GTLBO is tested on CEC−06, 2019 benchmark functions and other 15 classical benchmark functions, and it is found that the proposed algorithm is performing better comparatively. Then it is simulated for solving the workflow scheduling problem in CloudSim. Standard scientific workflow applications as Montage, Epigenomics, Sipht, and a sample workflow are used as dataset to test algorithms' performance in cloud environments. Our proposed approach, GTLBO, provides the proper distribution of workloads and offers minimal execution‐cost for the workflow applications. Results reflect the supremacy of the proposed algorithm GTLBO comparatively.

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Section: Application Of Gtlbo In Workflow Scheduling Problem On Cloudmentioning

confidence: 99%

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Ram

Srivastava

Mishra

2021

Concurrency and Computation

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“…The edges signify data and control dependencies among the tasks, and the nodes signify distinct tasks that are being executed. The XML files of Workflowsim [24][25][26] consist of task files, and output data of one task can be given as input to another task. At the beginning, the location of the data and executables are not known to the workflow.…”

Section: Workflow Implementation Modelmentioning

confidence: 99%

Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing

Hilda

Chandrasekaran

2021

Future Internet

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A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly challenging, as they elicit rigorous computational tasks over the communication network. For example, it was discovered that the time to execute a task in an unsuited resource consumes more cost and time in the cloud data centers. In this paper, a new cost- and time-efficient planning algorithm for scientific workflow scheduling has been proposed for heterogeneous systems in the cloud based upon the Predict Optimistic Time and Cost (POTC). The proposed algorithm computes the rank based not only on the completion time of the current task but also on the successor node in the critical path. Under a tight deadline, the running time of the workflow and the transfer cost are reduced by using this technique. The proposed approach is evaluated using true cases of data-exhaustive workflows compared with other algorithms from written works. The test result shows that our proposed method can remarkably decrease the cost and time of the experimented workflows while ensuring a better mapping of the task to the resource. In terms of makespan, speedup, and efficiency, the proposed algorithm surpasses the current existing algorithms—such as Endpoint communication contention-aware List Scheduling Heuristic (ELSH)), Predict Earliest Finish Time (PEFT), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Minimal Optimistic Processing Time (MOPT) and Predict Earlier Finish Time (PEFT)—while holding the same time complexity.

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“…Tabrizchi et al [16] proposed a novel self-adaptive hybrid Imperialist Competitive Algorithm (ICA)-PSO algorithm for dealing with associate multi-task scheduling problems. The authors combined ICA and PSO to improve the exploration.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware task scheduling in cloud compting based on discrete pathfinder algorithm

2021

IJE

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Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm

Cited by 36 publications

References 40 publications

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing

Energy-aware task scheduling in cloud compting based on discrete pathfinder algorithm

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