Self-scalable services in service oriented software for cost-effective data farming

Król, Dariusz; Kitowski, Jacek

doi:10.1016/j.future.2015.07.003

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…The Scalarm platform's 14 [28] data farming use case allows illustrating how to specify CAMEL models conforming to CAMEL's textual syntax. We limit the presentation to those specific CAMEL sub-models presented in "The CAMEL language" section to illustrate the definition of essential properties for the use case.…”

Section: Camel Application: the Data Farming Use Casementioning

confidence: 99%

The cloud application modelling and execution language

et al. 2019

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Cloud computing offers a flexible pay-as-you-go model for provisioning application resources, which enables applications to scale on-demand based on the current workload. In many cases, though, users face the single vendor lock-in effect, missing opportunities for optimal and adaptive application deployment across multiple clouds. Several cloud modelling languages have been developed to support multi-cloud resource management, but still they lack holistic cloud management of all aspects and phases. This work defines the Cloud Application Modelling and Execution Language (CAMEL), which (i) allows users to specify the full set of design time aspects for multi-cloud applications, and (ii) supports the models@runtime paradigm that enables capturing an application's current state facilitating its adaptive provisioning. CAMEL has been already used in many projects, domains and use cases due to its wide coverage of cloud management features. Finally, CAMEL has been positively evaluated in this work in terms of its usability and applicability in several domains (e.g., data farming, flight scheduling, financial services) based on the technology acceptance model (TAM).

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Section: Camel Application: the Data Farming Use Casementioning

confidence: 99%

The cloud application modelling and execution language

et al. 2019

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“…The support for workflows is available via Nimrod/K [76], which incorporates Kepler's capabilities on building complex workflows, and Nimrod's ability to execute sweeps over distributed resources. Scalarm [77], which stands for Massively Scalable Platform for Data Farming, is another platform that supports parameter sweep applications on heterogeneous computing infrastructures, such as grids and clouds. Scalarm has been developed in order to support data farming, from the experiment phase to the simulation execution and analysis of results, allowing users to manage computational resources regardless of the computing infrastructures used.…”

Section: Parameter Sweepsmentioning

confidence: 99%

A characterization of workflow management systems for extreme-scale applications

Silva

Filgueira

Pietri

et al. 2017

Future Generation Computer Systems

114

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Automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies, workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today's computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. The paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems.

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“…It has been designed and implemented as an extension (named Pathfinder) of an existing platform supporting large-scale parameter studies and data farming experiments called Scalarm, which was developed in our previous work [1]. It has been designed and implemented as an extension (named Pathfinder) of an existing platform supporting large-scale parameter studies and data farming experiments called Scalarm, which was developed in our previous work [1].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose a new approach to SA‐based experiments featuring massively parallel execution on HPC systems and automatic input parameter space exploration to study complex industrial problems with computer simulations. It has been designed and implemented as an extension (named Pathfinder ) of an existing platform supporting large‐scale parameter studies and data farming experiments called Scalarm, which was developed in our previous work . In this work, we introduce new functionality to Scalarm, that is, capability to automatically decide which input parameter values of a simulation model should be explored based on a given algorithm such as a selected SA method.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis on HPC systems with Scalarm platform

Bachniak

Rauch

Król

et al. 2016

Concurrency and Computation

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Sensitivity analysis is widely used in numerical simulations applied in industry. The robustness of such applications is crucial, which means they have to be fast and precise at the same time. However, the conventional approach to sensitivity analysis assumes realization of multiple execution of computationally intensive simulations to discover input/output dependencies. In this paper, we present a novel computational approach for performing large-scale sensitivity analysis integrated with an extended platform for parameter studies -Scalarm -to make use of modern e-infrastructures for distribution and parallelization purposes, profitable for complicated industrial problems. PPAM 2015 SPECIAL ISSUE 11 of 15 differences in SA results for two subsequent steps and finally lead to weak convergence. The idea is to obtain good convergence of SA results as a condition of reliability of analysis. Figure 8. Morris Design convergence test (mean absolute error).

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Self-scalable services in service oriented software for cost-effective data farming

Cited by 17 publications

References 18 publications

The cloud application modelling and execution language

The cloud application modelling and execution language

A characterization of workflow management systems for extreme-scale applications

Sensitivity analysis on HPC systems with Scalarm platform

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