Tool-supported design of data aggregation processes in cloud monitoring systems

Efficient monitoring of a cloud system involves multiple aggregation processes and large amounts of data with various and interdependent requirements. A thorough understanding and analysis of the characteristics of data aggregation processes can help to improve the software quality and reduce develo...

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Vydáno v:	Journal of ambient intelligence and humanized computing Ročník 10; číslo 7; s. 2519 - 2535
Hlavní autoři:	Cai, Simin, Gallina, Barbara, Nyström, Dag, Seceleanu, Cristina, Larsson, Alf
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2019 Springer Nature B.V
Témata:	Artificial Intelligence Automation Based specification Boolean Case studies Cloud computing Cloud monitoring Cloud monitoring system design Computational Intelligence Computer software selection and evaluation Consistency Consistency checking Data aggregation Data management Design Designers Efficient monitoring Engineering Feature model Feature modeling Large amounts of data Monitoring Monitoring systems Original Research Propositional formulas Quality control Robotics and Automation Software Solvers Specifications Taxonomies Taxonomy User Interfaces and Human Computer Interaction Video transmission Data aggregation Cloud monitoring system design Feature model Consistency checking
ISSN:	1868-5137, 1868-5145, 1868-5145
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Abstract	Efficient monitoring of a cloud system involves multiple aggregation processes and large amounts of data with various and interdependent requirements. A thorough understanding and analysis of the characteristics of data aggregation processes can help to improve the software quality and reduce development cost. In this paper, we propose a systematic approach for designing data aggregation processes in cloud monitoring systems. Our approach applies a feature-oriented taxonomy called DAGGTAX (Data AGGregation TAXonomy) to systematically specify the features of the designed system, and SAT-based analysis to check the consistency of the specifications. Following our approach, designers first specify the data aggregation processes by selecting and composing the features from DAGGTAX. These specified features, as well as design constraints, are then formalized as propositional formulas, whose consistency is checked by the Z3 SAT solver. To support our approach, we propose a design tool called SAFARE (SAt-based Feature-oriented dAta aggREgation design), which implements DAGGTAX-based specification of data aggregation processes and design constraints, and integrates the state-of-the-art solver Z3 for automated analysis. We also propose a set of general design constraints, which are integrated by default in SAFARE. The effectiveness of our approach is demonstrated via a case study provided by industry, which aims to design a cloud monitoring system for video streaming. The case study shows that DAGGTAX and SAFARE can help designers to identify reusable features, eliminate infeasible design decisions, and derive crucial system parameters.
AbstractList	Efficient monitoring of a cloud system involves multiple aggregation processes and large amounts of data with various and interdependent requirements. A thorough understanding and analysis of the characteristics of data aggregation processes can help to improve the software quality and reduce development cost. In this paper, we propose a systematic approach for designing data aggregation processes in cloud monitoring systems. Our approach applies a feature-oriented taxonomy called DAGGTAX (Data AGGregation TAXonomy) to systematically specify the features of the designed system, and SAT-based analysis to check the consistency of the specifications. Following our approach, designers first specify the data aggregation processes by selecting and composing the features from DAGGTAX. These specified features, as well as design constraints, are then formalized as propositional formulas, whose consistency is checked by the Z3 SAT solver. To support our approach, we propose a design tool called SAFARE (SAt-based Feature-oriented dAta aggREgation design), which implements DAGGTAX-based specification of data aggregation processes and design constraints, and integrates the state-of-the-art solver Z3 for automated analysis. We also propose a set of general design constraints, which are integrated by default in SAFARE. The effectiveness of our approach is demonstrated via a case study provided by industry, which aims to design a cloud monitoring system for video streaming. The case study shows that DAGGTAX and SAFARE can help designers to identify reusable features, eliminate infeasible design decisions, and derive crucial system parameters.
Author	Gallina, Barbara Larsson, Alf Seceleanu, Cristina Nyström, Dag Cai, Simin
Author_xml	– sequence: 1 givenname: Simin surname: Cai fullname: Cai, Simin email: simin.cai@mdh.se organization: School of Innovation, Design and Engineering, Mälardalen University – sequence: 2 givenname: Barbara surname: Gallina fullname: Gallina, Barbara organization: School of Innovation, Design and Engineering, Mälardalen University – sequence: 3 givenname: Dag surname: Nyström fullname: Nyström, Dag organization: School of Innovation, Design and Engineering, Mälardalen University – sequence: 4 givenname: Cristina surname: Seceleanu fullname: Seceleanu, Cristina organization: School of Innovation, Design and Engineering, Mälardalen University – sequence: 5 givenname: Alf surname: Larsson fullname: Larsson, Alf organization: Ericsson AB
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Cites_doi	10.1016/j.entcs.2004.08.046 10.1002/cpe.3832 10.1186/s13677-014-0024-2 10.1109/MSP.2011.33 10.1145/289.291 10.1016/j.knosys.2012.07.025 10.1109/2.769440 10.1016/j.comnet.2013.04.001 10.1016/j.scico.2012.06.004 10.1007/BF01264051 10.1016/j.future.2013.02.011 10.1007/s12652-015-0316-5 10.1145/1721654.1721672 10.1145/844128.844142 10.1109/MWC.2007.358967 10.1016/j.procs.2017.05.412 10.1023/A:1009726021843 10.1007/11554844_3 10.1016/j.future.2017.05.046 10.21236/ADA235785 10.1109/ICSE.2004.1317496 10.1109/INM.2015.7140277 10.1109/APSEC.2005.7 10.1007/s12652-017-0520-6 10.1007/978-3-319-66854-3_25
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SubjectTerms	Artificial Intelligence Automation Based specification Boolean Case studies Cloud computing Cloud monitoring Cloud monitoring system design Computational Intelligence Computer software selection and evaluation Consistency Consistency checking Data aggregation Data management Design Designers Efficient monitoring Engineering Feature model Feature modeling Large amounts of data Monitoring Monitoring systems Original Research Propositional formulas Quality control Robotics and Automation Software Solvers Specifications Taxonomies Taxonomy User Interfaces and Human Computer Interaction Video transmission
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Title	Tool-supported design of data aggregation processes in cloud monitoring systems
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