TY - JOUR
T1 - Micro-Workflows Data Stream Processing Model for Industrial Internet of Things
AU - Alaasam, A.B.A.
AU - Radchenko, Gleb
AU - Tchernykh, A.N.
N1 - Export Date: 27 August 2021
Correspondence Address: Alaasam, A.B.A.; South Ural State UniversityRussian Federation
Funding details: Russian Foundation for Basic Research, РФФИ, 19-37-90073
Funding details: Ministry of Education and Science of the Russian Federation, Minobrnauka, FENU-2020-0022
Funding text 1: The reported study was funded by the Ministry of Science and Higher Education of the Russian Federation (government order FENU-2020-0022) and by RFBR, project number 19-37-90073.
PY - 2021
Y1 - 2021
N2 - The fog computing paradigm has become prominent in stream processing for IoT systems where cloud computing struggles from high latency challenges. It enables the deployment of computational resources between the edge and cloud layers and helps to resolve constraints, primarily due to the need to react in real-time to state changes, improve the locality of data storage, and overcome external communication channels’ limitations. There is an urgent need for tools and platforms to model, implement, manage, and monitor complex fog computing workflows. Traditional scientific workflow management systems (SWMSs) provide modularity and flexibility to design, execute, and monitor complex computational workflows used in smart industry applications. However, they are mainly focused on batch execution of jobs consisting of tightly coupled tasks. Integrating data streams into SWMSs of IoT systems is challenging. We proposed a microworkflow model to redesign the monolith architecture of workflow systems into a set of smaller and independent workflows that support stream processing. Micro-workflow is an independent data stream processing service that can be deployed on different layers of the fog computing environment. To validate the feasibility and practicability of the micro-workflow refactoring, we provide intensive experimental analysis evaluating the interval between sensor messages, the time interval required to create a message, between sending sensor message and receiving the message in SWMS, including data serialization, network latency, etc. We show that the proposed decoupling support of the independence of implementation, execution, development, maintenance, and cross-platform deployment, where each micro-workflow becomes a standalone computational unit, is a suitable mechanism for IoT stream processing. © The Authors 2021. This paper is published with open access at SuperFri.org.
AB - The fog computing paradigm has become prominent in stream processing for IoT systems where cloud computing struggles from high latency challenges. It enables the deployment of computational resources between the edge and cloud layers and helps to resolve constraints, primarily due to the need to react in real-time to state changes, improve the locality of data storage, and overcome external communication channels’ limitations. There is an urgent need for tools and platforms to model, implement, manage, and monitor complex fog computing workflows. Traditional scientific workflow management systems (SWMSs) provide modularity and flexibility to design, execute, and monitor complex computational workflows used in smart industry applications. However, they are mainly focused on batch execution of jobs consisting of tightly coupled tasks. Integrating data streams into SWMSs of IoT systems is challenging. We proposed a microworkflow model to redesign the monolith architecture of workflow systems into a set of smaller and independent workflows that support stream processing. Micro-workflow is an independent data stream processing service that can be deployed on different layers of the fog computing environment. To validate the feasibility and practicability of the micro-workflow refactoring, we provide intensive experimental analysis evaluating the interval between sensor messages, the time interval required to create a message, between sending sensor message and receiving the message in SWMS, including data serialization, network latency, etc. We show that the proposed decoupling support of the independence of implementation, execution, development, maintenance, and cross-platform deployment, where each micro-workflow becomes a standalone computational unit, is a suitable mechanism for IoT stream processing. © The Authors 2021. This paper is published with open access at SuperFri.org.
KW - cloud computing
KW - fog computing
KW - IoT
KW - micro-workflow
KW - scientific workflow
KW - stream processing
KW - Complex networks
KW - Digital storage
KW - Fog
KW - Fog computing
KW - Industrial internet of things (IIoT)
KW - Work simplification
KW - Computational resources
KW - Computational workflows
KW - Computing environments
KW - Data stream processing
KW - Experimental analysis
KW - External communications
KW - Industry applications
KW - Scientific workflow managements
KW - Data streams
U2 - 10.14529/jsfi210106
DO - 10.14529/jsfi210106
M3 - Article
SN - 2409-6008
VL - 8
SP - 82
EP - 98
JO - Supercomput. front. innov.
JF - Supercomput. front. innov.
IS - 1
ER -