Cost-time trade-off efficient workflow scheduling in cloud

Mboula, Jean Etienne Ndamlabin; Kamla, Vivient Corneille; Djamegni, Clémentin Tayou

doi:10.1016/j.simpat.2020.102107

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…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%

“…For the simulation, the CloudSim tool is used and the obtained results are compared with basic algorithms such as BDHEFT, BHEFT, and IC-Loss on various real-world scientific workflows. Mboula et al [38] in 2020, suggested a novel workflow scheduling technique with the goal of minimizing processing expenses and execution times: Effective Workflow Scheduling with a Cost-Time Trade-off (CTTWS). Implicit Requested Instance Types Range (IRITR) evaluation is a novel idea used by the CTTWS scheduling algorithm to identify a range of virtual machine instance types that best suit the workflow execution and prevent overbids and underbids, which can result in budget and deadline violations, respectively.…”

Section: Surveymentioning

confidence: 99%

“…The latter uses a cost-time trade-off function on heterogeneous instances and dynamic virtual machine provisioning with a restricted number of rented virtual machines to identify the most practical scheduling. CTTWSDP also presented an improved evaluation of the Implicit Requested Instance Type Range (IRITR), which is a scheduling concept developed in their prior work [38]. The goal of the IRITR assessment is to identify a range of virtual machine instance types that are most appropriate for executing the workflow, preventing underbids and overbids that could violate deadlines and the budget, respectively.…”

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) Optimization Strategy • Heuristicmentioning

confidence: 99%

Section: Surveymentioning

confidence: 99%

Section: Surveymentioning

confidence: 99%

See 1 more Smart Citation

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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“…The authors proposed a linear programming model for time-cost-quality trade-off (TCQT) problems; Khang and Myint [32] implemented the model at a cement factory in Bangkok, Thailand, to confirm the proposed model. Ndamlabin Mboula et al [33] introduced a novel scheduling technique called Cost-Time Trade-off efficient workflow scheduling, which consists of four basic steps: activity selection, assessment of the Implicit Requested Instance Types Range, evaluation of the spare budget, and selection of the VM. Hu and He [34] presented a time-cost-quality optimization model using a genetic algorithm.…”

Section: Studies Of Resource Trade-offsmentioning

confidence: 99%

BIM-Based Resource Tradeoff in Project Scheduling Using Fire Hawk Optimizer (FHO)

et al. 2022

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Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO’s capability for solving it is assessed. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to the trade-off of various resource options in projects.

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“…The authors proposed a linear programming model for time-cost-quality trade-off (TCQT) problems; Khang and Myint [42] implemented the model at a cement factory in Bangkok, Thailand, to confirm the proposed model. Ndamlabin Mboula, et al [43] introduced a novel scheduling technique called Cost-Time Trade-off efficient workflow scheduling, which consists of four basic steps: activity selection, assessment of the Implicit Requested Instance Types Range, evaluation of the spare budget, and selection of the VM. Hu and He [44] presented a time-cost-quality optimization model using a genetic algorithm.…”

Section: Studies Of Resource Trade-offsmentioning

confidence: 99%

Fire Hawk Optimizer for Resource Trade-off in Project Scheduling based on BIM

Shishehgarkhaneh¹,

Azizi²,

Basiri³

et al. 2022

Preprint

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Project managers should balance a variety of resource elements in building projects while taking into account many major concerns, including time, cost, quality, risk, and the environment. This study presents a framework for resource trade-offs in project scheduling based on the Building Information Modeling (BIM) methodology and metaheuristic algorithms. First, a new metaheuristic algorithm called Fire Hawk Optimizer (FHO) is used. Using project management software and the BIM process, a 3D model of the construction is created. In order to maximize quality while minimizing time, cost, risk, and CO2 in the project under consideration, an optimization problem is created, and the FHO's capability for solving it is assessed. A predefined stopping condition is taken into account while doing 30 independent optimization runs to obtain the statistical metrics, such as the mean, standard deviation, and the required number of objective function evaluations. The results show that the FHO algorithm is capable of producing competitive and exceptional outcomes when it comes to trade-off various resource options in projects.

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Cost-time trade-off efficient workflow scheduling in cloud

Cited by 18 publications

References 25 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

BIM-Based Resource Tradeoff in Project Scheduling Using Fire Hawk Optimizer (FHO)

Fire Hawk Optimizer for Resource Trade-off in Project Scheduling based on BIM

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