Exploiting Queuing Networks to Model and Assess the Performance of Self-Adaptive Software Systems: A Survey

被引:12
作者
Arcelli, Davide [1 ]
机构
[1] Univ Aquila, Dept Informat Engn Comp Sci & Math, Via Vetoio 1, I-67100 Laquila, Italy
来源
11TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 3RD INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS | 2020年 / 170卷
关键词
Self-Adaptive Software Systems; Software Architectures; Autonomous Systems; Performance Engineering; Queuing Networks; RELIABILITY;
D O I
10.1016/j.procs.2020.03.108
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Self-adaptation has emerged as a primary concern in the context of modern software systems, due to the high dynamicity of the environments where they operate, which implies the need for such systems to properly face significant degrees of uncertainty. To this aim, much work has been done, mainly by coupling autonomic managers to the managed subsystem which perceives and affects the environment through its sensors and actuators, respectively. Such coupling often results into MAPE-K feedback loop(s), i.e. a Knowledge (K)-based architectural model that divides the adaptation process into four activities, namely Monitor (M), Analyze (A), Plan (P) and Execute (E). Performance modeling notations, analysis methods and tools, have been exploited and coupled to other kinds of techniques (e.g. control theory, machine learning) for modeling and assessing the performance of autonomic managers, possibly aimed at supporting the identification of more convenient architectural alternatives. Since moving in such a big arena is not trivial and it is easy to be overwhelmed, in this literature survey, we focus on a particular performance modeling paradigm, namely Queuing Networks, with the aim of clarifying the state-of-art in exploiting such a notation to model and assess performance of Self-Adaptive Software Systems. We conclude by bringing out some research opportunities that may be worth exploring in the near future. (C) 2020 The Authors. Published by Elsevier B.V.
引用
收藏
页码:498 / 505
页数:8
相关论文
共 32 条
[1]  
[Anonymous], 2004, The Unified Modeling Language Reference Manual
[2]  
[Anonymous], QUANTITATIVE SYSTEM
[3]  
[Anonymous], 2014, 5 ACM SPEC INT C PER, DOI 10.1145/2568088.2568095
[4]  
Arbib C., 2019, INT C INF SYST CRIS
[5]   Software model refactoring based on performance analysis: better working on software or performance side? [J].
Arcelli, Davide ;
Cortellessa, Vittorio .
ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE, 2013, (108) :33-47
[6]   A Library of Modeling Components for Adaptive Queuing Networks [J].
Arcelli, Davide ;
Cortellessa, Vittorio ;
Leva, Alberto .
COMPUTER PERFORMANCE ENGINEERING, 2016, 9951 :204-219
[7]   Control Theory for Model-based Performance-driven Software Adaptation [J].
Arcelli, Davide ;
Cortellessa, Vittorio ;
Filieri, Antonio ;
Leva, Alberto .
QOSA'15 PROCEEDINGS OF THE 11TH INTERNATIONAL ACM SIGSOFT CONFERENCE ON QUALITY OF SOFTWARE ARCHITECTURES, 2015, :11-20
[8]   Proteus: Language and Runtime Support for Self-Adaptive Software Development [J].
Barati, Saeid ;
Bartha, Ferenc A. ;
Biswas, Swarnendu ;
Cartwright, Robert ;
Duracz, Adam ;
Fussell, Donald S. ;
Hoffmann, Henry ;
Imes, Connor ;
Miller, Jason E. ;
Mishra, Nikita ;
Arvind ;
Dung Nguyen ;
Palem, Krishna, V ;
Pei, Yan ;
Pingali, Keshav ;
Sai, Ryuichi ;
Wright, Andrew ;
Yang, Yao-Hsiang ;
Zhang, Sizhuo .
IEEE SOFTWARE, 2019, 36 (02) :73-82
[9]  
Becker F, 2013, AFRICAN VOICES ON SLAVERY AND THE SLAVE TRADE, P71
[10]  
Becker Matthias, 2012, P 8 INT ACM SIGSOFT, P117