Workflow scheduling of scientific workflows under simultaneous deadline and budget constraints

Taghinezhad-Niar, Ahmad; Pashazadeh, Saeid; Taheri, Javid

doi:10.1007/s10586-021-03314-3

Cited by 13 publications

(4 citation statements)

References 31 publications

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“…The projected start time of the first task issued to a virtual machine (VM) and the estimated finish time of the last work assigned to it set limits on these leasing periods [49]. Some algorithms in [33], [32], [5], [36], [41], [42], [45], [50], [52] use the dynamic provisioning strategy. At the beginning of application scheduling, the number of resources to be used remains unknown.…”

Section: A) Staticmentioning

confidence: 99%

“…Numerous strategies for optimizing scientific workflows based on heuristics have been proposed such as BAGS [31], PDPPS [32], BDAS [33], FDHEFT [34], GRP-HEFT [36], NBWS [37], CTTSW [38], CTTWSDP [41], SRPSM [50], BDDC+BDC [42], MOWOS [56], MHPSLP [43] [51], SMWDSA [52], REWS [46], EViMA [45]. Heuristics are generally a collection of guidelines designed to solve certain problems [57].…”

Section: A) Optimization Strategy • Heuristicmentioning

confidence: 99%

“…In comparison to other current algorithms, their testing results demonstrate its cost-effectiveness, responsiveness to environmental uncertainties, and ability to meet deadlines. Taghinezhad-Niar et al [42] suggested in 2021, two algorithms to account for time and money restrictions when developing scientific workflows on cloud IaaS platforms: Budget Deadline Delicate Cloud (BDDC) and Budget Deadline Cloud (BDC). BDDC is a heuristic algorithm that distributes budgets and deadlines for each level taking into account the overall task execution time to acquire economic costs for each level.…”

Section: Surveymentioning

confidence: 99%

See 2 more Smart Citations

Taxonomy and survey of scientific workflow scheduling in infrastructure-as-a-service cloud computing systems

Stéphane,

Corneille,

Yassa

et al. 2024

JACST

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Scientific workflows are groups of scientific application tasks organized in oriented graphs. These scientific workflows are characterized by a large number of tasks requiring sufficient resources for their execution. Tasks with all available input data can be computed simultaneously. Cloud computing is an appropriate environment for the implementation of scientific workflows. Although the cloud computing environment has unlimited resources and can run some scientific workflow tasks simultaneously, scheduling scientific workflow tasks using pay-as-you-go cloud computing resources is an NP-complete problem. This difficulty is due to constraints on the part of the cloud resource provider and the part of the user (customer). The algorithm tries to find efficient schedules that take into account several requirements of client service (QoS) such as deadlines, budgets, and resource providers’ profits, i.e., the minimization of energy consumption and many others. There have been several papers recommending effective solutions to workflow schedule problems. This paper reviews existing and more recent papers on the plan of scientific workflows in pay-as-you-go IaaS cloud computing environments, focusing on future directions for algorithms that can improve the optimal solution.

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Section: A) Staticmentioning

confidence: 99%

Section: A) Optimization Strategy • Heuristicmentioning

confidence: 99%

Section: Surveymentioning

confidence: 99%

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Taxonomy and survey of scientific workflow scheduling in infrastructure-as-a-service cloud computing systems

Stéphane,

Corneille,

Yassa

et al. 2024

JACST

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“…Patel and Bhoi [ 16 ] reviewed several priority-based task scheduling algorithms and performed their comparison so that one can conclude which algorithm is more beneficial and can be approached for further research in the sector of cloud computing to resolve the existing issues which is a huge challenge to offer the best services to the user within less time. In this literature review article, the authors have analyzed various conventional scheduling algorithms in cloud computing and found that the PA-LBIMM algorithm is the best among all priority-based algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Aggarwal

Kumar

Bhatt³

et al. 2022

Computational Intelligence and Neuroscience

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Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in the cloud is task scheduling or workflow scheduling. Multiple factors are becoming a challenge for scheduling in cloud computing namely the heterogeneity of resources, tasks, and user priority. User priority has been encountered as the most challenging problem during the last decade as the number of users is increasing worldwide. This issue has been resolved by an advanced encryption standard (AES) algorithm, which decreases the response time and execution delay of the user-request. There are multifarious tasks, for instance, deploying the data on the cloud, that will be executed according to first come first serve (FCFS) and not on the payment basis, which provides an ease to the users. These investigated techniques are 30.21%, 25.20%, 25.30%, 30.25%, 24.26%, and 36.9 8% improved in comparison with the traditional FFOA, DE, ABC, PSO, GA, and ETC, respectively. Moreover, during iteration number 5, this approach is 15.20%, 20.22%, 30.56%, 26.30%, and 36.23% improved than that of the traditional techniques FFOA, DE, ABC, PSO, GA, and ETC, respectively. This investigated method is more efficient and applicable in certain arenas where user priority is the primary concern and can offer all the required services to the users without any interruption.

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Security-Aware Scheduling of Multiple Scientific Workflows in Cloud

Roy,

Gharote,

Ramamurthy

et al. 2024

Communications in Computer and Information Science

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Workflow scheduling of scientific workflows under simultaneous deadline and budget constraints

Cited by 13 publications

References 31 publications

Taxonomy and survey of scientific workflow scheduling in infrastructure-as-a-service cloud computing systems

Taxonomy and survey of scientific workflow scheduling in infrastructure-as-a-service cloud computing systems

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

Security-Aware Scheduling of Multiple Scientific Workflows in Cloud

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