A Simplified Model for Simulating the Execution of a Workflow in Cloud

Mathá, Roland; Ristov, Sasko; Prodan, Radu

doi:10.1007/978-3-319-64203-1_23

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…We compared the S TET =FT model with our previous work S TET [8] (which noises TET only), the standard Dynami-cCloudSim model S DCS with its standard heterogeneity parameter values, and the real Cloud experimental results (denoted as C). We implemented both S TET and S TET =FT models in DynamicCloudSim besides its default S DCS model.…”

Section: Simulation Environmentmentioning

confidence: 99%

“…To overcome these deficiencies that lead to an incomplete or inaccurate Cloud simulation setup, we introduced in previous work [8] a simple approach to determine and configure the Cloud performance instability by noising the task execution times. However, many scientific applications, such as workflows, are data intensive and spend up to 90 percent of their makespan on file transfers [9].…”

Section: Introductionmentioning

confidence: 99%

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Simplified Workflow Simulation on Clouds based on Computation and Communication Noisiness

Mathá

Ristov

Fahringer

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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Many researchers rely on simulations to analyze and validate their researched methods on Cloud infrastructures. However, determining relevant simulation parameters and correctly instantiating them to match the real Cloud performance is a difficult and costly operation, as minor configuration changes can easily generate an unreliable inaccurate simulation result. Using legacy values experimentally determined by other researchers can reduce the configuration costs, but is still inaccurate as the underlying public Clouds and the number of active tenants are highly different and dynamic in time. To overcome these deficiencies, we propose a novel model that simulates the dynamic Cloud performance by introducing noise in the computation and communication tasks, determined by a small set of runtime execution data. Although the estimating method is apparently costly, a comprehensive sensitivity analysis shows that the configuration parameters determined for a certain simulation setup can be used for other simulations too, thereby reducing the tuning cost by up to 82.46 percent, while declining the simulation accuracy by only 1.98 percent on average. Extensive evaluation also shows that our novel model outperforms other state-of-the-art dynamic Cloud simulation models, leading up to 22 percent lower makespan inaccuracy.

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Section: Simulation Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simplified Workflow Simulation on Clouds based on Computation and Communication Noisiness

Mathá

Ristov

Fahringer

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

Self Cite

View full text Add to dashboard Cite

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“…Several algorithms have been proposed from traditional scheduling algorithms (HEFT, 28 Min–Min, Max–Min, etc. ), to more complex algorithms that consider performance prediction, 29‐31 multisite clouds, 32 and so on. These algorithms have to be implemented in a scheduler component in each WfMS.…”

Section: Introductionmentioning

confidence: 99%

“…Although there are some approaches that aim at proposing performance models for VM live migrations and interference, 33 there is no definitive solution. In fact, depending on the type of interference it is difficult, or sometimes even impossible, to model all these characteristics because they are usually provider‐ and application‐dependent 30 . Estimating activations performance in VMs is also a difficult task to accomplish.…”

Section: Introductionmentioning

confidence: 99%

An incremental reinforcement learning scheduling strategy for data‐intensive scientific workflows in the cloud

Nascimento

Silva

Paes

et al. 2021

Concurrency and Computation

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Summary Most scientific experiments can be modeled as workflows. These workflows are usually computing‐ and data‐intensive, demanding the use of high‐performance computing environments such as clusters, grids, and clouds. This latter offers the advantage of the elasticity, which allows for changing the number of virtual machines (VMs) on demand. Workflows are typically managed using scientific workflow management systems (SWfMS). Many existing SWfMSs offer support for cloud‐based execution. Each SWfMS has its scheduler that follows a well‐defined cost function. However, such cost functions should consider the characteristics of a dynamic environment, such as live migrations or performance fluctuations, which are far from trivial to model. This article proposes a novel scheduling strategy, named ReASSIgN, based on reinforcement learning (RL). By relying on an RL technique, one may assume that there is an optimal (or suboptimal) solution for the scheduling problem, and aims at learning the best scheduling based on previous executions in the absence of a mathematical model of the environment. For this, an extension of a well‐known workflow simulator WorkflowSim is proposed to implement an RL strategy for scheduling workflows. Once the scheduling plan is generated via simulation, the workflow is executed in the cloud using SciCumulus SWfMS. We conducted a throughout evaluation of the proposed scheduling strategy using a real astronomy workflow named Montage.

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