Intelligent Run-Time Partitioning of Low-Code System Models

Sorour Jahanbin; Dimitris Kolovos; Simos Gerasimou

doi:10.1145/3417990.3420198

Intelligent Run-Time Partitioning of Low-Code System Models

Sorour Jahanbin, Dimitris Kolovos, Simos Gerasimou

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Over the last 2 decades, several dedicated languages have been proposed to support model management activities such as model validation, transformation, and code generation. As software systems become more complex, underlying system models grow proportionally in both size and complexity. To keep up, model management languages and their execution engines need to provide increasingly more sophisticated mechanisms for making the most efficient use of the available system resources. Efficiency is particularly important when model-driven technologies are used in the context of low-code platforms where all model processing happens in pay-per-use cloud resources. In this paper, we present our vision for an approach that leverages sophisticated static program analysis of model management programs to identify, load, process and transparently discard relevant model partitions - instead of naively loading the entire models into memory and keeping them loaded for the duration of the execution of the program. In this way, model management programs will be able to process system models faster with a reduced memory footprint, and resources will be freed that will allow them to accommodate even larger models.

Original language	English
Title of host publication	Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
Place of Publication	New York, NY, USA
Publisher	ACM
ISBN (Print)	9781450381352
DOIs	https://doi.org/10.1145/3417990.3420198
Publication status	Published - 2020

Publication series

Name	MODELS '20
Publisher	Association for Computing Machinery

Keywords

partial loading
memory management
model partitioning
model-driven software engineering

Access to Document

10.1145/3417990.3420198

https://doi.org/10.1145/3417990.3420198

Cite this

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title = "Intelligent Run-Time Partitioning of Low-Code System Models",

abstract = "Over the last 2 decades, several dedicated languages have been proposed to support model management activities such as model validation, transformation, and code generation. As software systems become more complex, underlying system models grow proportionally in both size and complexity. To keep up, model management languages and their execution engines need to provide increasingly more sophisticated mechanisms for making the most efficient use of the available system resources. Efficiency is particularly important when model-driven technologies are used in the context of low-code platforms where all model processing happens in pay-per-use cloud resources. In this paper, we present our vision for an approach that leverages sophisticated static program analysis of model management programs to identify, load, process and transparently discard relevant model partitions - instead of naively loading the entire models into memory and keeping them loaded for the duration of the execution of the program. In this way, model management programs will be able to process system models faster with a reduced memory footprint, and resources will be freed that will allow them to accommodate even larger models.",

keywords = "partial loading, memory management, model partitioning, model-driven software engineering",

author = "Sorour Jahanbin and Dimitris Kolovos and Simos Gerasimou",

year = "2020",

doi = "10.1145/3417990.3420198",

language = "English",

isbn = "9781450381352",

series = "MODELS '20",

publisher = "ACM",

booktitle = "Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings",

address = "United States",

}

Intelligent Run-Time Partitioning of Low-Code System Models. / Jahanbin, Sorour ; Kolovos, Dimitris ; Gerasimou, Simos.
Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. New York, NY, USA: ACM, 2020. (MODELS '20).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Intelligent Run-Time Partitioning of Low-Code System Models

AU - Jahanbin, Sorour

AU - Kolovos, Dimitris

AU - Gerasimou, Simos

PY - 2020

Y1 - 2020

N2 - Over the last 2 decades, several dedicated languages have been proposed to support model management activities such as model validation, transformation, and code generation. As software systems become more complex, underlying system models grow proportionally in both size and complexity. To keep up, model management languages and their execution engines need to provide increasingly more sophisticated mechanisms for making the most efficient use of the available system resources. Efficiency is particularly important when model-driven technologies are used in the context of low-code platforms where all model processing happens in pay-per-use cloud resources. In this paper, we present our vision for an approach that leverages sophisticated static program analysis of model management programs to identify, load, process and transparently discard relevant model partitions - instead of naively loading the entire models into memory and keeping them loaded for the duration of the execution of the program. In this way, model management programs will be able to process system models faster with a reduced memory footprint, and resources will be freed that will allow them to accommodate even larger models.

AB - Over the last 2 decades, several dedicated languages have been proposed to support model management activities such as model validation, transformation, and code generation. As software systems become more complex, underlying system models grow proportionally in both size and complexity. To keep up, model management languages and their execution engines need to provide increasingly more sophisticated mechanisms for making the most efficient use of the available system resources. Efficiency is particularly important when model-driven technologies are used in the context of low-code platforms where all model processing happens in pay-per-use cloud resources. In this paper, we present our vision for an approach that leverages sophisticated static program analysis of model management programs to identify, load, process and transparently discard relevant model partitions - instead of naively loading the entire models into memory and keeping them loaded for the duration of the execution of the program. In this way, model management programs will be able to process system models faster with a reduced memory footprint, and resources will be freed that will allow them to accommodate even larger models.

KW - partial loading

KW - memory management

KW - model partitioning

KW - model-driven software engineering

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DO - 10.1145/3417990.3420198

M3 - Conference contribution

SN - 9781450381352

T3 - MODELS '20

BT - Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

PB - ACM

CY - New York, NY, USA

ER -

Intelligent Run-Time Partitioning of Low-Code System Models

Abstract

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Keywords

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